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Multi-omics approaches investigate the bitter flavor in the shoot of Bambusa oldhamii Munro | BMC Plant Biology

The biological characteristics of Bambusa oldhamii Munro

Clumping bamboo has sympodial rhizomes that are characterized by their rhizomes growing outwards from buds on a “mother” rhizome, eventually turning upwards to become new culms (Fig. 1a). The culm height of B. oldhamii ranges from 6 to 12 m, the culm diameter ranges from 3 to 9 cm, depending on the growth environment, the internode length ranges from 20 to 35 cm, and the wall thickness ranges from 4 to 12 mm, depending on growth conditions (Fig. 1b). There are multiple branches in each node of the middle and top culms in B. oldhamii, about 5 leaves in each little branch, and the leaf length ranges from 10–20 cm, and the leaf width ranges from 1.0 to 1.5 cm (Fig. 1c). The flowering period happens between May to November and includes bracts (3–5) and florets (5–9) (Fig. 1d). The shooting period is from May to November, the young edible shoots are sold as fresh vegetables on the market or marked as processed food, and the sheaths of the shoots are deciduous, leathery, dark brown spinous-hairy (Fig. 1e-g). The shoot of B. oldhamii had their best taste and maximum edible portion when they had not erupted out of the soil surface (Fig. 1h-j). When the shoot of B. oldhamii erupted out of the soil surface, the above-ground part of the shoot sheaths would be green with the sunlight and days (Fig. 1k). The old inedible shoots would be degraded or grown into young bamboo culms and then form a new bamboo (Fig. 1l).

Fig. 1

The biological phenotype and genome characteristics of B. oldhamii. a A clumping forest of B. oldhamii; b A culm internode of B. oldhamii; c A leaf branch of B. oldhamii; d The florets of B. oldhamii; e A edible shoot of B. oldhamii; f The shoots of B. oldhamii with shells off; g The off shells of a bamboo shoot; h Phenotype of the shoots under soil surface; i The phenotype of the edible shoot usually sold on market. j The longitudinal section of the edible shoot is usually sold on the market. k–l The shoot has erupted out of the soil surface within several days. m The genome circular graph of B. oldhamii from the inner to outer track represents the density of tRNA, snRNA, rRNA, miRNA, and Genes; the last outer track represents the length of chromosomes in different colors

To comprehend this bamboo species more deeply, the genetic genome information of B. oldhamii was explored. Compared with the internal standard tomato with a genome size of 800 Mb and a peak around 51.03 using flow cytometric fluorescent detection (Fig. S1a), the estimated genome size of B. oldhamii is 1.375 Gb with a peak around 87.77 (Fig. S1b). The Illumina sequencing method was used to estimate the genome size of B. oldhamii around 1.33 Gb based on the 27 K-mer length (Fig. S2). Based on the above genome size estimation results, we processed the whole nuclear genome sequence with PacBio HiFi (40X) and HiC (100X) sequencing projects. After the PacBio HiFi and Hi-C sequencing of B. oldhamii, about 97.23% of contig sequences were anchored on the pseudomolecule chromosomes, and a total of 35 pseudochromosomes were acquired based on synteny analysis with the relative species D. latiflorus. The final Pseudochromosome genome was evaluated with the Chromosome Hi-C signal heatmap, corresponding to the Hi-C assisted genome assembly thesis (Fig. S3), and the final genome BUSCO evaluation is 96.59%.

The final assembled genome size of B. oldhamii is 1.446 Gb, the N50 of all the 1851 scaffolds is 38,936,033 bp, and the GC contents are 45% (Table 1). After the gene structure annotation, a total of 88,140 genes were acquired. All 88,140 genes were annotated in Nr, Swissprot, KEGG, KOG, TrEMBL, Interpro, and GO databases with rates of 76.87%, 50.46%, 49.24%, 47.25%, 76.70%, 62.48%, and 55.74% respectively. Finally, about 78.11% (68,843) of genes acquired the functional annotations (Table 1) (Table S1). The noncoding RNA, including miRNA, tRNA, rRNA, and snRNA, was annotated at the rate of 0.003297%, 0.043679%, 0.356499%, and 0.008666% respectively, on the whole genome (Fig. 1m) (Table S2). In the genome annotation of B. oldhamii, a total of 779,839,625 bp repeat genome sequences were acquired and occupied 53.89% of the genome size (Table S3). Based on the transcription factor annotation, the large number of transcription factor families is bHLH, NAC, MYB, bZIP, C2H2, AP2/ERF-ERF, and AP2/ERF-AP2 (Table S4).

Table 1 Statistics of the assembly genome features and gene annotation in B. oldhamii

The basic genome analysis of B. oldhamii Munro

Through the identification and analysis of genome homologous genes and families can obtain single-copy gene families and multi-copy gene families. We statistics the families and genes in the following species: A. thaliana, A. trichopoda, B. oldhamii (subgenomes A, B, and C), D. latiflorus (subgenomes A, B, and C), O. latifolia, O. sativa, P. edulis (subgenomes C and D), P. trichocarpa, and R. guianensis (Table S5). The results of orthologs and family statistics showed that A. trichopoda has the greatest number of Single-copy orthologs, P. trichocarpa has the greatest number of Multi-copy orthologs, and O. latifolia has the most Unique paralogs (Fig. S4a) (Table S5). 5522 ortholog families exist in both species of B. oldhamii (subgenomes A, B, and C) and O. sativa (Fig. S4b), and 8250 ortholog families that exist in the species of B. oldhamii_A, P. edulis_C, D. latiflorus_A, and O.latifolia (Fig. S4c). As the Phylogenetic tree analysis results show that the species of A. trichopoda is the outer taxa, the other species in the phylogenetic tree are consistent with the phylogenetic positions of the APG IV system. A. thaliana and P. trichocarpa branched together, and the others belong to Poaceae including the bamboos and rice, moreover, the A sub-genome of D. latiflorus_A and B. oldhamii_A branched together, the B sub-genome of D. latiflorus_B and B. oldhamii_B branched together, and the C sub-genome of D. latiflorus_C and B. oldhamii_C branched together (Fig. S5).

Based on the time tree criterion (http://www.timetree.org/), the divergence time between O. sativa and A. thaliana was 130–200 million years ago (Mya), and between P. trichoparca and A. thaliana was 100–120 Mya. As the result shows that the divergence time between O. latifolia and R. guianensis is 47.5 (26.3—69.8) Mya, and the divergence time between O. sativa and bamboos is 100.7 (76.1–126.1) Mya (Fig. 2a). The results of 4DTv analysis showed that the last whole genome duplication of B. oldhamii_A, B. oldhamii_B, and B. oldhamii_C happened after the first divergence of O. sativa. (Fig. 2b). We do the gene family expansion and contraction between genomes and subgenomes (Fig. 2c) (Table S6). The results showed that A. thaliana has the most expansion gene families, and P. trichocarpa has the most contraction gene families, and then is the plant species A. trichopoda (Fig. 2c). The genes in the collinearity fragment have maintained a high degree of conservation throughout species evolution. The genome synteny results showed that there are more blocks between O. sativa and B. oldhamii than between B. oldhamii and D. latiflorus, but the mean block length was larger in B.oldhamii and D. latiflorus than in B. oldhamii and O.sativa (Fig. 2d-f) (Table S7).

Fig. 2

The phenotype and genome characteristics of B. oldhamii. a The estimation of divergence time among species. The number in blue color on the branches means the estimated divergence time (Mya), and the red nodes are tagged as the criteria divergence time (Mya). b The distribution of 4DTv distance (corrected for multiple substitutions). c The gene family expansion and contraction between A. thaliana, A. trichopoda, B. oldhamii (subgenomes A, B, and C), D. latiflorus (subgenomes A, B, and C), O.latifolia, O.sativa, P.edulis (subgenomes C and D), P. trichocarpa, and R. guianensis. d The synteny analysis between O. sativa, B. oldhamii_A, and D. latiflorus_A. e The synteny analysis between O.sativa, B.oldhamii_B, and D.latiflorus_B. (f) The synteny analysis between O. sativa, B. oldhamii_C, and D. latiflorus_C

We acquired the expansion and contraction genes in the three subgenomes of B. oldhamii, in B. oldhamii_A, there were 160 contraction genes and 888 expansion genes (Table S8), and the expansion genes were enriched in the pathway of Plant-pathogen interaction, sulfur relay system, homologous recombination, and valine leucine and isoleucine degradation and others (Fig. S6) (Table S9). In B. oldhamii_B, there are 163 contraction genes belong to 128 families and 669 expansion genes belong to 99 families (Table S10), and the expansion genes were enriched in the pathway of alpha-Linolenic acid metabolism, homologous recombination, glycerophospholipid metabolism, arginine biosynthesis, sesquiterpenoid and triterpenoid biosynthesis, diterpenoid biosynthesis, flavonoid biosynthesis and others (Fig. S7) (Table S11). In B. oldhamii_C, there are 167 contraction genes belonging to 182 families, and 372 expansion genes belong to 46 families (Table S12), and the expansion was enriched in the pathway of homologous recombination and basal transcription factors (Fig. S8) (Table S13).

Data mining of transcriptome and metabolome in multiple shoot developmental phases

Seven developed phases of the shoot in B. oldhamii were picked out searching for the bitter chemicals and key genes contributing to the taste transition from delicious taste to a little bitter taste with transcriptome and metabolome (Fig. 3a). DNBSEQ platforms were used to sequence the 21 shoot samples (7 phases with three biological replicates in each phase). The transcriptome data of each sample is about 6.45 Gb, the average mapping rate of the genome is 91.49%, and the average mapping rate of the gene sets is 65.69%. Based on the reference genome annotation, 88,140 genes were Known Genes, and 3,228 Novel Genes from the RNA-seq data (Table S14). A total of 53,252 genes were detected as expressed genes, 50,129 were known genes (49,365 DEG and 764 Non DEG), and 3,123 genes were novel (3121 DEG and 2 Non DEG) (Fig. 3b) (Table S15). A total of 42,887 new transcripts were detected, 39,659 new alternative splice isoforms distributed in 20,786 known protein-coding genes, and 3,228 were novel gene coding transcripts (Table S16). The FPKM distribution of the transcription factor families in Known Genes was analyzed (Fig. 3c). The results showed that the families of bHLH, MYB-related, bZIP, C3H, HB-, C2H2-, and TUB transcription factors might play crucial roles in shoot growth and development. There are large number of those transcription factors whose expressions were activated in certain shoot developing phases (FPKM > 10).

Fig. 3

Basic data mining of transcriptome and metabolome in multiple shoots developing phases of B. oldhamii. a The sectional shoot morphology of seven developing phases; b The FPKM distribution of expressed Known genes and Novel genes in the transcriptome of seven developing phases. c The FPKM distribution of transcription factors in the genome’s known genes is based on the transcriptome expression data. d The classification of all metabolites detected in the shoot of seven developing phases; e The classification of mined bitter metabolites and relative accumulated patterns in seven developing bamboo shoots of B. oldhamii

A total of 428 qualified metabolites were identified (Table S17). The class of phytochemical compounds includes 37 flavonoids, 29 terpenoids, and others (Fig. 3d). The class of compounds with biological roles includes 31 amino acids, peptides, and analogs, 16 benzene and derivatives, 11 carbohydrates, 11 purines and derivatives, 10 organic acids, and others (Fig. 3d). The class of Lipids includes 15 polyketides, 10 Fatty acyls, and others (Fig. 3d). In order to acquire the bitter metabolites, each metabolite was checked against bitterDB. Finally, 33 bitter metabolites were acquired in this research, major distributed in the families of Amino acids metabolites (L-Isoleucine, L-Leucine, L-Tryptophan, and L-Tyrosine), Flavonoids (Naringenin, Genistein, Genistin, Daidzin, Apigenin, Isorhamnetin, Kaempferol, and Hesperetin), Purines and derivatives (Caffeine, Pentoxifylline, Theobromine, and Adenosine), Terpenoids (Limonin, Andrographolide, Ginkgolide A, L-MENTHONE, Oleuropein, Nerol, Stevioside, Obacunone, Gentiopicrin) and others (Dicumarol, Arbutin, Amarogentin, and others) (Fig. 3e). The taste transition is complex; bitter taste is usually accompanied by astringent taste generation. Tannin is a criterion to measure astringent taste. In the correlation analysis, we added tannin as a measurement criterion for mining the accompanied bitter metabolites. The bitter metabolites positively correlated with the content of tannins were Flavonoids (Genistin, Daidzin, Kaempferol), Terpenoids (Oleuropein, Stevioside, Obacunone), Purine and derivatives (Theobromine), Coumarin and derivatives (Dicumarol), Benzene and derivatives (Salicylic acid), Carbohydrate (Arbutin), and Amarogentin. The negative correlation between bitter metabolites with tannin content was amino acids (L-Isoleucine, L-Leucine, L-Tyrosine), Terpenoids (L-menthone, Nerol, Gentiopicrin), and Sinapic acid (Fig. S9).

The differentially accumulated metabolites between phases were analyzed (Table S18). In the comparison of BoS2 vs. BoS1 (81 DAMs, 46 up DAMs, and 35 down DAMs), all the DAMs were significantly enriched in the pathway of flavone and flavonol biosynthesis, monoterpenoid biosynthesis, and tyrosine metabolism (Fig. S10a). In the comparison of BoS3 vs. BoS2 (49 DAMs, 25 up DAMs, and 24 down DAMs), all the DAMs were significantly enriched in the pathway of phenylalanine, tyrosine, and tryptophan biosynthesis (Fig. S10b). In the comparison of BoS4 vs. BoS3 (61 DAMs, 26 up DAMs, and 35 down DAMs), all the DAMs were significantly enriched in the pathway of Monoterpenoid biosynthesis, Metabolic pathways, and Biosynthesis of secondary metabolites (Fig. S10c). In the comparison of BoS5 vs. BoS4 (172 DAMs, 89 up DAMs, and 83 down DAMs), the total DAMs were significantly enriched in the pathway of aminoacyl-tRNA biosynthesis, metabolic pathway, biosynthesis of secondary metabolites, biosynthesis of amino acids, and phenylalanine metabolism (Fig. S10d). In the comparison of BoS6 vs. BoS5 (45 DAMs, 31 up DAMs, and 14 down DAMs), all the DAMs were enriched in the pathway of Stilbenoid, diarylheptanoid, and gingerol biosynthesis (Fig. S10e). In the comparison of BoS7 vs. BoS6 (23 DAMs, 14 up DAMs, and 9 down DAMs), all the DAMs were enriched in the pathway of flavone and flavonol biosynthesis (Fig. S10f). Finally, a total of 373 non-repeated differentially accumulated metabolites were acquired (Table S19).

Joint analysis between transcriptome and metabolome

From the combined analysis between metabolome and transcriptome, the DAMs and DEGs in each comparison were enriched with KEGG pathways. All the 81 DAMs and 858 DEGs in comparison of BoS2 vs. BoS1 enriched in the pathway of Biosynthesis of secondary metabolites, Glyoxylate and dicarboxylate metabolism, Glycolysis/Gluconeogenesis, and other pathways (Fig. S11) (Table S20). All the 49 DAMs and 439 DEGs in comparison of BoS3 vs. BoS2 enriched in the pathway of Biosynthesis of secondary metabolites, beta-alanine metabolism, Amino sugar and nucleotide sugar metabolism, Alanine, aspartate, and glutamate metabolism, Tryptophan metabolism, and other pathways (Fig. S12) (Table S21). All the 61 DAMs and 388 DEGs in comparison of BoS4 vs. BoS3 enriched in the pathway of Biosynthesis of secondary metabolites, Glutathione metabolism, Flavonoid biosynthesis, Plant hormone signal transduction, and other pathways (Fig. S13) (Table S22). All the 172 DAMs and 516 DEGs in the comparison of BoS5 vs. BoS4 enriched in the pathway of Biosynthesis of secondary metabolites, Isoflavonoid biosynthesis, Plant hormone signal transduction, and other pathways (Fig. S14) (Table S23). All the 45 DAMs and 337 DEGs in comparison of BoS6 vs. BoS5 enriched in the pathway of Biosynthesis of secondary metabolites, Pyrimidine metabolism, and other pathways (Fig. S15) (Table S24). All the 23 DAMs and 410 DEGs in comparison of BoS7 vs. BoS6 enriched in the pathway of Flavone and flavonol biosynthesis and Flavonoid biosynthesis (Fig. S16) (Table S25). From this joint comparison analysis, we find that the pathway of biosynthesis of secondary metabolites, amino acid metabolism, biosynthesis of flavonoid, flavone, flavonol and isoflavonoid, plant hormone signal transduction, and others play important roles in the flavor transition biological process.

The WGCNA analysis between the bitter metabolites and gene expression showed that the bitter metabolites, such as L-Isoleucine, L-Leucine, and L-Tyrosine, were positively correlated with the module of MEturquoise, while Salicylic acid, arbutin, dicumarol, genistin, stevioside, and amarogentin were negatively correlated with the module of MEturquoise (Fig. 4a). The correlated analysis between bitter metabolites and transcription factors showed that the family of bHLH and HB- (including HB-BELL, HB-HD-Zip, HB-KNOX, HB-WOX, and HB-others) has large genes correlated with bitter metabolites, indicating that bHLH and HB- transcription factor families might play pivotal roles in regulating the biosynthesis or metabolism of bitter metabolites (Fig. 4b). To mine out the key genes in bHLH transcription factors, we did the network analysis of the bHLH transcription factor existing in MEturquoise (weight > 0.3) and sorted with degree values, and the results showed that Bo00073447 (bHLH) was a hub gene in the bHLH Co-expression Network (Fig. 4c) (Table S26). To mine out the key genes in HB- transcription factors, we do the network analysis of HB- transcription factors existing in MEturquoise (weight > 0.3) and sorted by degree values, and we find some key genes, Bo00029783(HB-HD-ZIP), Bo00006584(HB-HD-ZIP), Bo00043187(HB-KNOX), and Bo00010600 (HB-WOX) play roles in the HB- Co-expression Network (Fig. 4d) (Table S27).

Fig. 4

Weighted correlation network analysis and gene co-expression network between bitter metabolites and transcription factor families. a Weighted correlation network analysis (WGCNA) between bitter metabolites and expressed genes (FPKM ≥ 5). b Chord Diagram of the correlated relationship between bitter metabolites and transcription factors. c Gene Co-expression Network of bHLH transcription factors existed in the MEturquois module (weight > 0.3). d Gene Co-expression Network of HB- transcription factors existed in the MEturquois module (weight > 0.3)

Model of bitter flavor formation in the shoot of B. oldhamii

From the above joint analysis between bitter metabolites and transcription factors, we found that the expression of bHLH and HB- transcription factors was relevant to the accumulation of bitter metabolites might play roles in the biosynthesis and metabolic pathways of bitter metabolites. In this research, combined with our previous research [15], the mined bitter chemicals were searched against the KEGG pathway database, the bitter metabolites related pathways in each class were simplified and merged with flavonoids, amino acids, terpenoids, purine, solanidine, and hydrogen cyanide pathways (Fig. S17). The flavonoid bitter metabolites (Naringenin, Genistein, Genistin, Daidzin, Apigenin, Kaempferol, and Hesperetin) and other researched metabolites (Salicylate, Salicin, Arbutin, Amygdalin [15], Sinapic acid) are merged in the flavonoid-related pathway originally synthesized from bitter amino acid L-Phenylalanine (Fig. S17a). The bitter amino acids (L-Isoleucine, L-Leucine, L-Tryptophan, and L-Tyrosine) and arginine [15] merged in the amino acid biosynthesis and metabolic pathways (Fig. S17b). The terpenoid metabolites (Limonin, Ginkgolide A, Oleuropein, Nerol, Stevioside, Menthone, and Gentiopicrin) were merged in the terpenoid pathway originating from terpenoid backbone biosynthesis (Fig. S17c). Purine bitter metabolites (Caffeine, Theobromine, and Adenosine) and hypoxanthine [15] were merged in the Purine metabolism pathway (Fig. S17d). Solanidine and Amygdalin were detected in our previous research [15] were merged in the abbreviated biosynthesis of various alkaloids pathway (Fig. S17e) and the abbreviated cyanoamino acid metabolism pathway (Fig. S17f). Amygdalin is a kind of cyanogenic glucoside, but the majority of academics regard taxiphyllin as the major cyanogenic glucoside that exists in the edible bamboo shoot tip [57] and is easily decomposed to hydrogen cyanide (HCN), which causes a bitter flavor. The bitter amino acids (phenylalanine, tyrosine, isoleucine, leucine, and valine) were substances for the biosynthesis of cyanogenic glucosides(Fig. S17f) in plants [12]. We consider that there are several kinds of cyanogenic glucosides that exist in the bamboo shoot, causing the bitter flavor transition, not just one kind of cyanogenic glucoside.

From the above analysis, we hypothesize a simple model about this research (Fig. 5a). In an appropriate environment, when the shoot of B. oldhamii grown out of the soil surface and accepted sunlight signals for days, the expression of bHLH and HB- transcription factors were activated or repressed which influence the genes’ expression in the related pathways (such as, Circadian rhythm; Plant hormone signal transduction; Phenylpropanoid biosynthesis; Flavonoid biosynthesis; Flavone and flavonol biosynthesis; and others) to determine the content accumulation variation of bitter metabolites (such as, flavonoids; amino acids; terpenoids; Purines and derivatives; Cyanogenic glycosides; and others), and finally causing the bitter flavor formation or transition in the tip shoot of B. oldhamii (Fig. 5a). There were exist diversity bamboo species in the world, and the bamboo forests have diverse usages, such as edible shoots collection, bamboo timber collection, city or road ornamental, germplasm preservation, soil improvement, nature reserve, and others. Multiple bamboo species and forests were used to produce edible shoots, which could be sold as fresh shoots or used to make processed food (Picked shoots, Salted shoots, Boiled shoots, Soused shoots, Canned shoots, Dried shoots) (Fig. 5b). The majority of bamboo forests were used for ecosystem balance and bamboo timber, such as bamboo weaving, bamboo charcoal, disposable tableware, bamboo fiber clothes, bamboo timber architecture, bamboo flooring, and bamboo crafts (Fig. 5c). This research was focused on the bamboo species of B. oldhamii, which is especially distributed in the southeast of China, and the shoots that emerged in summer are mainly used as an edible shoot locally. The hypothetical model about the bitter flavor formation or transition in the tip shoot is based on the Multi-omics data in B. oldhamii, which needs to be verified with deep research. Bamboo has a variety of species, and the shoot shape, color, taste, and properties of various bamboo species were different, and the edible portion or part was different depending on the bamboo species. Each bamboo species has its unique flavor and taste; the flavor formation or transition in the edible shoot of other bamboo species still needs further research.

Fig. 5

A hypothetical model of bitter metabolites formation, and the usages of edible bamboo shoot and timber in B. oldhamii. a The hypothetical model of bitter flavor metabolites formation in the shoot tip of B. oldhamii. b The traditional uses of edible bamboo shoots. c The traditional usages of bamboo timber

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November 3, 2025
Neutrophils and neutrophil extracellular traps: double-edged swords in cancer pathophysiology and therapy resistance | Cell Communication and Signaling
Xu M, Xu H, Ling YW, Liu JJ, Song P, Fang ZQ, Yue ZS, Duan JL, He F, Wang L. Neutrophil extracellular traps-triggered hepatocellular senescence exacerbates lipotoxicity in non-alcoholic steatohepatitis. J Adv Res. 2025;S2090–1232(25):00175–4. https://doi.org/10.1016/j.jare.2025.03.015. Epub ahead of print. PMID: 40068761.

Article
CAS

Google Scholar
Zhang H, Wang Y, Qu M, Li W, Wu D, Cata JP, et al. Neutrophil, neutrophil extracellular traps and endothelial cell dysfunction in sepsis. Clin Transl Med. 2023;13(1):e1170. https://doi.org/10.1002/ctm2.1170.

Article
CAS
PubMed
PubMed Central

Google Scholar
Ma Y, Wei J, He W, Ren J. Neutrophil extracellular traps in cancer. MedComm. 2024;5(8):e647. https://doi.org/10.1002/mco2.647.

Article
CAS
PubMed
PubMed Central

Google Scholar
Petretto A, Bruschi M, Pratesi F, Croia C, Candiano G, Ghiggeri G, et al. Neutrophil extracellular traps (NET) induced by different stimuli: a comparative proteomic analysis. PLoS ONE. 2019;14(7):e0218946. https://doi.org/10.1371/journal.pone.0218946.

Article
CAS
PubMed
PubMed Central

Google Scholar
Jaillon S, Ponzetta A, Di Mitri D, Santoni A, Bonecchi R, Mantovani A. Neutrophil diversity and plasticity in tumour progression and therapy. Nat Rev Cancer. 2020;20(9):485–503. https://doi.org/10.1038/s41568-020-0281-y.

Article
CAS
PubMed

Google Scholar
Shaul ME, Fridlender ZG. Tumour-associated neutrophils in patients with cancer. Nat Rev Clin Oncol. 2019;16(10):601–20. https://doi.org/10.1038/s41571-019-0222-4.

Article
PubMed

Google Scholar
Ozveren A, Erdogan AP, Ekinci F. The inflammatory prognostic index as a potential predictor of prognosis in metastatic gastric cancer. Sci Rep. 2023;13(1):7755. https://doi.org/10.1038/s41598-023-34778-5. PMID: 37173358; PMCID: PMC10182084.

Article
CAS
PubMed
PubMed Central

Google Scholar
Coffelt SB, Wellenstein MD, de Visser KE. Neutrophils in cancer: neutral no more. Nat Rev Cancer. 2016;16(7):431–46. https://doi.org/10.1038/nrc.2016.52.

Article
CAS
PubMed

Google Scholar
Mantovani A, Cassatella MA, Costantini C, Jaillon S. Neutrophils in the activation and regulation of innate and adaptive immunity. Nat Rev Immunol. 2011;11(8):519–31. https://doi.org/10.1038/nri3024. PMID: 21785456.
Grivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010;140(6):883–99. https://doi.org/10.1016/j.cell.2010.01.025.

Article
CAS
PubMed
PubMed Central

Google Scholar
Kolaczkowska E, Kubes P. Neutrophil recruitment and function in health and inflammation. Nat Rev Immunol. 2013;13(3):159–75. https://doi.org/10.1038/nri3399.

Article
CAS
PubMed

Google Scholar
Borregaard N. Neutrophils, from marrow to microbes. Immunity. 2010;33(5):657–70. https://doi.org/10.1016/j.immuni.2010.11.011.

Article
CAS
PubMed

Google Scholar
Mantovani A, Cassatella MA, Costantini C, Jaillon S. Neutrophils in the activation and regulation of innate and adaptive immunity. Nat Rev Immunol. 2011;11(8):519–31. https://doi.org/10.1038/nri3024.

Article
CAS
PubMed

Google Scholar
Adrover JM, McDowell SAC, He XY, Quail DF, Egeblad M. NETworking with cancer: the bidirectional interplay between cancer and neutrophil extracellular traps. Cancer Cell. 2023;41(3):505–26. https://doi.org/10.1016/j.ccell.2023.02.001.

Article
CAS
PubMed
PubMed Central

Google Scholar
Papayannopoulos V. Neutrophil extracellular traps in immunity and disease. Nat Rev Immunol. 2018;18(2):134–47. https://doi.org/10.1038/nri.2017.105.

Article
CAS
PubMed

Google Scholar
Brinkmann V, Reichard U, Goosmann C, Fauler B, Uhlemann Y, Weiss DS, et al. Neutrophil extracellular traps kill bacteria. Science. 2004;303(5663):1532–5. https://doi.org/10.1126/science.1092385.

Article
CAS
PubMed

Google Scholar
Fuchs TA, Abed U, Goosmann C, Hurwitz R, Schulze I, Wahn V, et al. Novel cell death program leads to neutrophil extracellular traps. J Cell Biol. 2007;176(2):231–41. https://doi.org/10.1083/jcb.200606027.

Article
CAS
PubMed
PubMed Central

Google Scholar
Herre M, Cedervall J, Mackman N, Olsson AK. Neutrophil extracellular traps in the pathology of cancer and other inflammatory diseases. Physiol Rev. 2023;103(1):277–312. https://doi.org/10.1152/physrev.00062.2021.

Article
CAS
PubMed

Google Scholar
Leung HHL, Perdomo J, Ahmadi Z, Yan F, McKenzie SE, Chong BH. Inhibition of NADPH oxidase blocks NETosis and reduces thrombosis in heparin-induced thrombocytopenia. Blood Adv. 2021;5(23):5439–51. https://doi.org/10.1182/bloodadvances.2020003093.

Article
CAS
PubMed
PubMed Central

Google Scholar
Chen KW, Monteleone M, Boucher D, Sollberger G, Ramnath D, Condon ND, et al. Noncanonical inflammasome signaling elicits gasdermin D-dependent neutrophil extracellular traps. Sci Immunol. 2018;3(26):eaar6676. https://doi.org/10.1126/sciimmunol.aar6676.

Article
PubMed

Google Scholar
Chi Z, Chen S, Yang D, Cui W, Lu Y, Wang Z, et al. Gasdermin d-mediated metabolic crosstalk promotes tissue repair. Nature. 2024;634(8036):1168–77. https://doi.org/10.1038/s41586-024-08022-7.

Article
CAS
PubMed

Google Scholar
Zhang J, Yu Q, Jiang D, Yu K, Yu W, Chi Z, Chen S, Li M, Yang D, Wang Z, Xu T, Guo X, Zhang K, Fang H, Ye Q, He Y, Zhang X, Wang D. Epithelial Gasdermin D shapes the host-microbial interface by driving mucus layer formation. Sci Immunol. 2022;7(68):eabk2092. https://doi.org/10.1126/sciimmunol.abk2092. Epub 2022 Feb 4. PMID: 35119941.

Article
CAS

Google Scholar
Fontana P, Du G, Zhang Y, Zhang H, Vora SM, Hu JJ, Shi M, Tufan AB, Healy LB, Xia S, Lee DJ, Li Z, Baldominos P, Ru H, Luo HR, Agudo J, Lieberman J, Wu H. Small-molecule GSDMD agonism in tumors stimulates antitumor immunity without toxicity. Cell. 2024;187(22):6165–e618122. Epub 2024 Sep 6. PMID: 39243763.

Article
CAS
PubMed
PubMed Central

Google Scholar
Bianchi M, Hakkim A, Brinkmann V, Siler U, Seger RA, Zychlinsky A, et al. Restoration of NET formation by gene therapy in CGD controls aspergillosis. Blood. 2009;114(13):2619–22. https://doi.org/10.1182/blood-2009-05-221606.

Article
CAS
PubMed
PubMed Central

Google Scholar
Ermert D, Urban CF, Laube B, Goosmann C, Zychlinsky A, Brinkmann V. Mouse neutrophil extracellular traps in microbial infections. J Innate Immun. 2009;1(3):181–93. https://doi.org/10.1159/000205281.

Article
CAS
PubMed
PubMed Central

Google Scholar
Pilsczek FH, Salina D, Poon KK, Fahey C, Yipp BG, Sibley CD, et al. A novel mechanism of rapid nuclear neutrophil extracellular trap formation in response to Staphylococcus aureus. J Immunol. 2010;185(12):7413–25. https://doi.org/10.4049/jimmunol.1000675.

Article
CAS
PubMed

Google Scholar
Byrd AS, O’Brien XM, Johnson CM, Lavigne LM, Reichner JS. An extracellular matrix-based mechanism of rapid neutrophil extracellular trap formation in response to Candida albicans. J Immunol. 2013;190(8):4136–48. https://doi.org/10.4049/jimmunol.1202671.

Article
CAS
PubMed

Google Scholar
Parker H, Dragunow M, Hampton MB, Kettle AJ, Winterbourn CC. Requirements for NADPH oxidase and myeloperoxidase in neutrophil extracellular trap formation differ depending on the stimulus. J Leukoc Biol. 2012;92(4):841–9. https://doi.org/10.1189/jlb.1211601.

Article
CAS
PubMed

Google Scholar
Douda DN, Khan MA, Grasemann H, Palaniyar N. SK3 channel and mitochondrial ROS mediate NADPH oxidase-independent NETosis induced by calcium influx. Proc Natl Acad Sci U S A. 2015;112(9):2817–22. https://doi.org/10.1073/pnas.1414055112.

Article
CAS
PubMed
PubMed Central

Google Scholar
Hosseinzadeh A, Thompson PR, Segal BH, Urban CF. Nicotine induces neutrophil extracellular traps. J Leukoc Biol. 2016;100(5):1105–12. https://doi.org/10.1189/jlb.3AB0815-379RR.

Article
CAS
PubMed
PubMed Central

Google Scholar
Castanheira FVS, Kubes P. Neutrophils and NETs in modulating acute and chronic inflammation. Blood. 2019;133(20):2178–85. https://doi.org/10.1182/blood-2018-11-844530.

Article
CAS
PubMed

Google Scholar
Li M, Lin C, Deng H, Strnad J, Bernabei L, Vogl DT, et al. A novel peptidylarginine deiminase 4 (PAD4) inhibitor BMS-P5 blocks formation of neutrophil extracellular traps and delays progression of multiple myeloma. Mol Cancer Ther. 2020;19(7):1530–8. https://doi.org/10.1158/1535-7163.MCT-19-1020.

Article
CAS
PubMed
PubMed Central

Google Scholar
Papayannopoulos V, Metzler KD, Hakkim A, Zychlinsky A. Neutrophil elastase and myeloperoxidase regulate the formation of neutrophil extracellular traps. J Cell Biol. 2010;191(3):677–91. https://doi.org/10.1083/jcb.201006052.

Article
CAS
PubMed
PubMed Central

Google Scholar
Metzler KD, Fuchs TA, Nauseef WM, Reumaux D, Roesler J, Schulze I, et al. Myeloperoxidase is required for neutrophil extracellular trap formation: implications for innate immunity. Blood. 2011;117(3):953–9. https://doi.org/10.1182/blood-2010-06-290171.

Article
CAS
PubMed
PubMed Central

Google Scholar
Knight JS, Zhao W, Luo W, Subramanian V, O’Dell AA, Yalavarthi S, et al. Peptidylarginine deiminase inhibition is immunomodulatory and vasculoprotective in murine lupus. J Clin Invest. 2013;123(7):2981–93. https://doi.org/10.1172/JCI67390.

Article
CAS
PubMed
PubMed Central

Google Scholar
Guiducci E, Lemberg C, Küng N, Schraner E, Theocharides APA, LeibundGut-Landmann S. Candida albicans-induced NETosis is independent of peptidylarginine deiminase 4. Front Immunol. 2018;9:1573. https://doi.org/10.3389/fimmu.2018.01573.

Article
CAS
PubMed
PubMed Central

Google Scholar
Claushuis TAM, van der Donk LEH, Luitse AL, van Veen HA, van der Wel NN, van Vught LA, Roelofs JJTH, de Boer OJ, Lankelma JM, Boon L, de Vos AF, van ‘t Veer C, van der Poll T. Role of peptidylarginine deiminase 4 in neutrophil extracellular trap formation and host defense during Klebsiella pneumoniae-Induced Pneumonia-Derived sepsis. J Immunol. 2018;201(4):1241–52. https://doi.org/10.4049/jimmunol.1800314. Epub 2018 Jul 9. PMID: 29987161.

Article
CAS
PubMed

Google Scholar
Warnatsch A, Ioannou M, Wang Q, Papayannopoulos V. Inflammation. Neutrophil extracellular traps license macrophages for cytokine production in atherosclerosis. Science. 2015;349(6245):316–20. https://doi.org/10.1126/science.aaa8064. Epub 2015 Jul 16. PMID: 26185250; PMCID: PMC4854322.

Article
CAS
PubMed
PubMed Central

Google Scholar
Branzk N, Lubojemska A, Hardison SE, Wang Q, Gutierrez MG, Brown GD, et al. Neutrophils sense microbe size and selectively release neutrophil extracellular traps in response to large pathogens. Nat Immunol. 2014;15(11):1017–25. https://doi.org/10.1038/ni.2987.

Article
CAS
PubMed
PubMed Central

Google Scholar
Hakkim A, Fuchs TA, Martinez NE, Hess S, Prinz H, Zychlinsky A, Waldmann H. Activation of the Raf-MEK-ERK pathway is required for neutrophil extracellular trap formation. Nat Chem Biol. 2011;7(2):75–7. https://doi.org/10.1038/nchembio.496. Epub 2010 Dec 19. PMID: 21170021.

Article
CAS
PubMed

Google Scholar
Yipp BG, Petri B, Salina D, Jenne CN, Scott BN, Zbytnuik LD, Pittman K, Asaduzzaman M, Wu K, Meijndert HC, Malawista SE, de Boisfleury Chevance A, Zhang K, Conly J, Kubes P. Infection-induced NETosis is a dynamic process involving neutrophil multitasking in vivo. Nat Med. 2012;18(9):1386–93. https://doi.org/10.1038/nm.2847. PMID: 22922410; PMCID: PMC4529131.

Article
CAS
PubMed
PubMed Central

Google Scholar
Yipp BG, Kubes P. NETosis: how vital is it? Blood. 2013;122(16):2784–94. https://doi.org/10.1182/blood-2013-04-457671.

Article
CAS
PubMed

Google Scholar
Wang Y, Li M, Stadler S, Correll S, Li P, Wang D, et al. Histone hypercitrullination mediates chromatin decondensation and neutrophil extracellular trap formation. J Cell Biol. 2009;184(2):205–13. https://doi.org/10.1083/jcb.200806072.

Article
CAS
PubMed
PubMed Central

Google Scholar
McDonald B, Urrutia R, Yipp BG, Jenne CN, Kubes P. Intravascular neutrophil extracellular traps capture bacteria from the bloodstream during sepsis. Cell Host Microbe. 2012;12(3):324–33. https://doi.org/10.1016/j.chom.2012.06.011.

Article
CAS
PubMed

Google Scholar
Jorch SK, Kubes P. An emerging role for neutrophil extracellular traps in noninfectious disease. Nat Med. 2017;23(3):279–87. https://doi.org/10.1038/nm.4294.

Article
CAS
PubMed

Google Scholar
Kim J, Kim HS, Chung JH. Molecular mechanisms of mitochondrial DNA release and activation of the cGAS-STING pathway. Exp Mol Med. 2023;55(3):510–9. https://doi.org/10.1038/s12276-023-00965-7.

Article
CAS
PubMed
PubMed Central

Google Scholar
Yousefi S, Gold JA, Andina N, Lee JJ, Kelly AM, Kozlowski E, et al. Catapult-like release of mitochondrial DNA by eosinophils contributes to antibacterial defense. Nat Med. 2008;14(9):949–53. https://doi.org/10.1038/nm.1855.

Article
CAS
PubMed

Google Scholar
McIlroy DJ, Jarnicki AG, Au GG, Lott N, Smith DW, Hansbro PM, Balogh ZJ. Mitochondrial DNA neutrophil extracellular traps are formed after trauma and subsequent surgery. J Crit Care. 2014;29(6):1133.e1-5. https://doi.org/10.1016/j.jcrc.2014.07.013. Epub 2014 Jul 22. PMID: 25128442.

Article
CAS
PubMed

Google Scholar
Lood C, Blanco LP, Purmalek MM, Carmona-Rivera C, De Ravin SS, Smith CK, et al. Neutrophil extracellular traps enriched in oxidized mitochondrial DNA are interferogenic and contribute to lupus-like disease. Nat Med. 2016;22(2):146–53. https://doi.org/10.1038/nm.4027.

Article
CAS
PubMed
PubMed Central

Google Scholar
Yousefi S, Mihalache C, Kozlowski E, Schmid I, Simon HU. Viable neutrophils release mitochondrial DNA to form neutrophil extracellular traps. Cell Death Differ. 2009;16(11):1438–44. https://doi.org/10.1038/cdd.2009.96.

Article
CAS
PubMed

Google Scholar
Zhang Y, Guo F, Wang Y. Hypoxic tumor microenvironment: destroyer of natural killer cell function. Chin J Cancer Res. 2024;36(2):138–50. https://doi.org/10.21147/j.issn.1000-9604.2024.02.04.

Article
PubMed
PubMed Central

Google Scholar
Guillotin F, Fortier M, Portes M, Demattei C, Mousty E, Nouvellon E, et al. Vital NETosis vs. suicidal NETosis during normal pregnancy and preeclampsia. Front Cell Dev Biol. 2023;10:1099038. https://doi.org/10.3389/fcell.2022.1099038.

Article
PubMed
PubMed Central

Google Scholar
Zhu D, Lu Y, Yang S, Hu T, Tan C, Liang R, et al. PAD4 inhibitor-functionalized layered double hydroxide nanosheets for synergistic sonodynamic therapy/immunotherapy of tumor metastasis. Adv Sci. 2024;11(26):e2401064. https://doi.org/10.1002/advs.202401064.

Article
CAS

Google Scholar
Moiana M, Aranda F, de Larrañaga G. A focus on the roles of histones in health and diseases. Clin Biochem. 2021;94:12–9. https://doi.org/10.1016/j.clinbiochem.2021.04.019.

Article
CAS
PubMed

Google Scholar
Xie W, Yu X, Yang Q, Ke N, Wang P, Kong H, Wu X, Ma P, Chen L, Yang J, Feng X, Wang Y, Shi H, Chen L, Liu YH, Ding BS, Wei Q, Jiang H. An immunomechanical checkpoint PYK2 governs monocyte-to-macrophage differentiation in pancreatic cancer. Cancer Discov. 2025. https://doi.org/10.1158/2159-8290.CD-24-1712. Epub ahead of print. PMID: 40338055.
Yang C, Wang Z, Li L, Zhang Z, Jin X, Wu P, et al. Aged neutrophils form mitochondria-dependent vital NETs to promote breast cancer lung metastasis. J Immunother Cancer. 2021;9(10):e002875. https://doi.org/10.1136/jitc-2021-002875.

Article
PubMed
PubMed Central

Google Scholar
Hao X, Gu H, Chen C, Huang D, Zhao Y, Xie L, Zou Y, Shu HS, Zhang Y, He X, Lai X, Zhang X, Zhou BO, Zhang CC, Chen GQ, Yu Z, Yang Y, Zheng J. Metabolic imaging reveals a unique preference of symmetric cell division and homing of Leukemia-Initiating cells in an endosteal niche. Cell Metab. 2019;29(4):950–e9656. Epub 2018 Dec 20. PMID: 30581117.

Article
CAS
PubMed

Google Scholar
Wu Q, Hu G. A genetic portrait of metastatic seeds in lung adenocarcinoma. Cancer Cell. 2023;41(5):828–30. https://doi.org/10.1016/j.ccell.2023.04.004.

Article
CAS
PubMed

Google Scholar
West AP, Khoury-Hanold W, Staron M, Tal MC, Pineda CM, Lang SM, et al. Mitochondrial DNA stress primes the antiviral innate immune response. Nature. 2015;520(7548):553–7. https://doi.org/10.1038/nature14156.

Article
CAS
PubMed
PubMed Central

Google Scholar
Yamazaki T, Kirchmair A, Sato A, Buqué A, Rybstein M, Petroni G, Bloy N, Finotello F, Stafford L, Navarro Manzano E, de la Ayala F, García-Martínez E, Formenti SC, Trajanoski Z, Galluzzi L. Mitochondrial DNA drives abscopal responses to radiation that are inhibited by autophagy. Nat Immunol. 2020;21(10):1160–71. https://doi.org/10.1038/s41590-020-0751-0. Epub 2020 Aug 3. PMID: 32747819.

Article
CAS
PubMed

Google Scholar
Ahn J, Xia T, Konno H, Konno K, Ruiz P, Barber GN. Inflammation-driven carcinogenesis is mediated through STING. Nat Commun. 2014;5(1):5166. https://doi.org/10.1038/ncomms6166.

Article
CAS
PubMed

Google Scholar
Bakhoum SF, Ngo B, Laughney AM, Cavallo JA, Murphy CJ, Ly P, et al. Chromosomal instability drives metastasis through a cytosolic DNA response. Nature. 2018;553(7689):467–72. https://doi.org/10.1038/nature25432.

Article
CAS
PubMed
PubMed Central

Google Scholar
Cheng AN, Cheng LC, Kuo CL, Lo YK, Chou HY, Chen CH, et al. Mitochondrial lon-induced mtDNA leakage contributes to PD-L1-mediated immunoescape via STING-IFN signaling and extracellular vesicles. J Immunother Cancer. 2020;8(2):e001372. https://doi.org/10.1136/jitc-2020-001372.

Article
PubMed
PubMed Central

Google Scholar
Xiong S, Dong L, Cheng L. Neutrophils in cancer carcinogenesis and metastasis. J Hematol Oncol. 2021;14(1):173. https://doi.org/10.1186/s13045-021-01187-y.

Article
CAS
PubMed
PubMed Central

Google Scholar
Yang LY, Luo Q, Lu L, Zhu WW, Sun HT, Wei R, Lin ZF, Wang XY, Wang CQ, Lu M, Jia HL, Chen JH, Zhang JB, Qin LX. Increased neutrophil extracellular traps promote metastasis potential of hepatocellular carcinoma via provoking tumorous inflammatory response. J Hematol Oncol. 2020;13(1):3. https://doi.org/10.1186/s13045-019-0836-0. PMID: 31907001; PMCID: PMC6945602.

Article
CAS
PubMed
PubMed Central

Google Scholar
Demers M, Krause DS, Schatzberg D, Martinod K, Voorhees JR, Fuchs TA, et al. Cancers predispose neutrophils to release extracellular DNA traps that contribute to cancer-associated thrombosis. Proc Natl Acad Sci U S A. 2012;109(32):13076–81. https://doi.org/10.1073/pnas.1200419109.

Article
PubMed
PubMed Central

Google Scholar
Tohme S, Yazdani HO, Al-Khafaji AB, Chidi AP, Loughran P, Mowen K, et al. Neutrophil extracellular traps promote the development and progression of liver metastases after surgical stress. Cancer Res. 2016;76(6):1367–80. https://doi.org/10.1158/0008-5472.CAN-15-1591.

Article
CAS
PubMed
PubMed Central

Google Scholar
Antonio N, Bønnelykke-Behrndtz ML, Ward LC, Collin J, Christensen IJ, Steiniche T, et al. The wound inflammatory response exacerbates growth of pre-neoplastic cells and progression to cancer. EMBO J. 2015;34(17):2219–36. https://doi.org/10.15252/embj.201490147.

Article
CAS
PubMed
PubMed Central

Google Scholar
Hedrick CC, Malanchi I. Neutrophils in cancer: heterogeneous and multifaceted. Nat Rev Immunol. 2022;22(3):173–87. https://doi.org/10.1038/s41577-021-00571-6.

Article
CAS
PubMed

Google Scholar
Liu S, Wu W, Du Y, Yin H, Chen Q, Yu W, et al. The evolution and heterogeneity of neutrophils in cancers: origins, subsets, functions, orchestrations and clinical applications. Mol Cancer. 2023;22(1):148. https://doi.org/10.1186/s12943-023-01843-6.

Article
PubMed
PubMed Central

Google Scholar
Butin-Israeli V, Bui TM, Wiesolek HL, Mascarenhas L, Lee JJ, Mehl LC, Knutson KR, Adam SA, Goldman RD, Beyder A, Wiesmuller L, Hanauer SB, Sumagin R. Neutrophil-induced genomic instability impedes resolution of inflammation and wound healing. J Clin Invest. 2019;129(2):712–26. Epub 2019 Jan 14. PMID: 30640176; PMCID: PMC6355304.

Article
PubMed
PubMed Central

Google Scholar
Hsu YL, Hung JY, Chang WA, Lin YS, Pan YC, Tsai PH, et al. Hypoxic lung cancer-secreted exosomal miR-23a increased angiogenesis and vascular permeability by targeting prolyl hydroxylase and tight junction protein ZO-1. Oncogene. 2017;36(34):4929–42. https://doi.org/10.1038/onc.2017.105.

Article
CAS
PubMed

Google Scholar
Shang M, Weng L, Wu S, Liu B, Yin X, Wang Z, Mao A. HP1BP3 promotes tumor growth and metastasis by upregulating miR-23a to target TRAF5 in esophageal squamous cell carcinoma. Am J Cancer Res. 2021;11(6):2928–43. PMID: 34249436; PMCID: PMC8263663.
Zhou X, Yan T, Huang C, Xu Z, Wang L, Jiang E, Wang H, Chen Y, Liu K, Shao Z, Shang Z. Melanoma cell-secreted Exosomal miR-155-5p induce proangiogenic switch of cancer-associated fibroblasts via SOCS1/JAK2/STAT3 signaling pathway. J Exp Clin Cancer Res. 2018;37(1):242. https://doi.org/10.1186/s13046-018-0911-3. PMID: 30285793; PMCID: PMC6169013.

Article
CAS
PubMed
PubMed Central

Google Scholar
Zheng Z, Sun R, Zhao HJ, Fu D, Zhong HJ, Weng XQ, et al. MiR155 sensitized B-lymphoma cells to anti-PD-L1 antibody via PD-1/PD-L1-mediated lymphoma cell interaction with CD8 + T cells. Mol Cancer. 2019;18(1):54. https://doi.org/10.1186/s12943-019-0977-3.

Article
PubMed
PubMed Central

Google Scholar
Carmody RN, Sarkar A, Reese AT. Gut microbiota through an evolutionary lens. Science. 2021;372(6541):462–3. https://doi.org/10.1126/science.abf0590.

Article
CAS
PubMed

Google Scholar
Ternes D, Tsenkova M, Pozdeev VI, Meyers M, Koncina E, Atatri S, et al. The gut microbial metabolite formate exacerbates colorectal cancer progression. Nat Metab. 2022;4(4):458–75. https://doi.org/10.1038/s42255-022-00558-0.

Article
CAS
PubMed
PubMed Central

Google Scholar
Saitoh T, Komano J, Saitoh Y, Misawa T, Takahama M, Kozaki T, et al. Neutrophil extracellular traps mediate a host defense response to human immunodeficiency virus-1. Cell Host Microbe. 2012;12(1):109–16. https://doi.org/10.1016/j.chom.2012.05.015.

Article
CAS
PubMed

Google Scholar
Ouyang D, Xiang T, Chen Y, Song M, Zhao J, Chen H, Li S, Zhang L, Xu C, Ren Y, Tao Y, Wang Q, He J, Li Y, Xie S, Liu Y, Wang Y, Yang X, You J, Xie S, Li Y, Weng D, Pan Q, Yang Q, Xia J. The CXCL10-CXCR3 axis induces Tumor-Associated neutrophils to interfere with CTLs-Mediated antitumor activity in EBV-Associated epithelial Cancers. Adv sci (Weinh). 2025;e00950. https://doi.org/10.1002/advs.202500950. Epub ahead of print. PMID: 40686457.
Deng Z, Mei S, Ouyang Z, Wang R, Wang L, Zou B, Dai J, Mao K, Li Q, Guo Q, Yi C, Meng F, Xie M, Zhang X, Wang R, Deng T, Wang Z, Li X, Wang Q, Liu B, Tian X. Dysregulation of gut microbiota stimulates NETs-driven HCC intrahepatic metastasis: therapeutic implications of healthy faecal microbiota transplantation. Gut Microbes. 2025;17(1):2476561. Epub 2025 Mar 18. PMID: 40099491; PMCID: PMC11925110.

Article
PubMed
PubMed Central

Google Scholar
Fu A, Yao B, Dong T, Chen Y, Yao J, Liu Y, Li H, Bai H, Liu X, Zhang Y, Wang C, Guo Y, Li N, Cai S. Tumor-resident intracellular microbiota promotes metastatic colonization in breast cancer. Cell. 2022;185(8):1356–e137226. https://doi.org/10.1016/j.cell.2022.02.027. Epub 2022 Apr 7. PMID: 35395179.

Article
CAS
PubMed

Google Scholar
Jin C, Lagoudas GK, Zhao C, Bullman S, Bhutkar A, Hu B, Ameh S, Sandel D, Liang XS, Mazzilli S, Whary MT, Meyerson M, Germain R, Blainey PC, Fox JG, Jacks T. Commensal microbiota promote lung cancer development via γδ T cells. Cell. 2019;176(5):998–e101316. Epub 2019 Jan 31. PMID: 30712876; PMCID: PMC6691977.

Article
CAS
PubMed
PubMed Central

Google Scholar
Branzk N, Papayannopoulos V. Molecular mechanisms regulating NETosis in infection and disease. Semin Immunopathol. 2013;35(4):513–30. https://doi.org/10.1007/s00281-013-0384-6.

Article
CAS
PubMed
PubMed Central

Google Scholar
Liu C, Qi J, Shan B, Gao R, Gao F, Xie H, et al. Pretreatment with cathelicidin-BF ameliorates Pseudomonas aeruginosa pneumonia in mice by enhancing NETosis and the autophagy of recruited neutrophils and macrophages. Int Immunopharmacol. 2018;65:382–91. https://doi.org/10.1016/j.intimp.2018.10.030.

Article
CAS
PubMed

Google Scholar
Garley M, Jabłońska E, Dąbrowska D. NETs in cancer. Tumour Biol. 2016;37(11):14355–61. https://doi.org/10.1007/s13277-016-5328-z.

Article
CAS
PubMed

Google Scholar
Xiao Y, Cong M, Li J, He D, Wu Q, Tian P, Wang Y, Yang S, Liang C, Liang Y, Wen J, Liu Y, Luo W, Lv X, He Y, Cheng DD, Zhou T, Zhao W, Zhang P, Zhang X, Xiao Y, Qian Y, Wang H, Gao Q, Yang QC, Yang Q, Hu G. Cathepsin C promotes breast cancer lung metastasis by modulating neutrophil infiltration and neutrophil extracellular trap formation. Cancer Cell. 2021;39(3):423–e4377. https://doi.org/10.1016/j.ccell.2020.12.012. Epub 2021 Jan 14. PMID: 33450198.

Article
CAS
PubMed

Google Scholar
Qi JL, He JR, Liu CB, Jin SM, Gao RY, Yang X, et al. Pulmonary Staphylococcus aureus infection regulates breast cancer cell metastasis via neutrophil extracellular traps (NETs) formation. MedComm. 2020;1(2):188–201. https://doi.org/10.1002/mco2.22.

Article
PubMed
PubMed Central

Google Scholar
Munir H, Jones JO, Janowitz T, Hoffmann M, Euler M, Martins CP, et al. Stromal-driven and amyloid β-dependent induction of neutrophil extracellular traps modulates tumor growth. Nat Commun. 2021;12(1):683. https://doi.org/10.1038/s41467-021-20982-2.

Article
CAS
PubMed
PubMed Central

Google Scholar
Xie M, Lin Z, Ji X, Luo X, Zhang Z, Sun M, et al. FGF19/FGFR4-mediated elevation of ETV4 facilitates hepatocellular carcinoma metastasis by upregulating PD-L1 and CCL2. J Hepatol. 2023;79(1):109–25. https://doi.org/10.1016/j.jhep.2023.02.036.

Article
CAS
PubMed

Google Scholar
Kim YC, Seok S, Zhang Y, Ma J, Kong B, Guo G, et al. Intestinal FGF15/19 physiologically repress hepatic lipogenesis in the late fed-state by activating SHP and DNMT3A. Nat Commun. 2020;11(1):5969. https://doi.org/10.1038/s41467-020-19803-9.

Article
CAS
PubMed
PubMed Central

Google Scholar
Li C, Chen T, Liu J, Wang Y, Zhang C, Guo L, et al. FGF19-induced inflammatory CAF promoted neutrophil extracellular trap formation in the liver metastasis of colorectal cancer. Adv Sci. 2023;10(24):e2302613. https://doi.org/10.1002/advs.202302613.

Article
CAS

Google Scholar
Cheng Y, Li H, Deng Y, Tai Y, Zeng K, Zhang Y, et al. Cancer-associated fibroblasts induce PDL1 + neutrophils through the IL6-STAT3 pathway that foster immune suppression in hepatocellular carcinoma. Cell Death Dis. 2018;9(4):422. https://doi.org/10.1038/s41419-018-0458-4.

Article
CAS
PubMed
PubMed Central

Google Scholar
Takesue S, Ohuchida K, Shinkawa T, Otsubo Y, Matsumoto S, Sagara A, Yonenaga A, Ando Y, Kibe S, Nakayama H, Iwamoto C, Shindo K, Moriyama T, Nakata K, Miyasaka Y, Ohtsuka T, Toma H, Tominaga Y, Mizumoto K, Hashizume M, Nakamura M. Neutrophil extracellular traps promote liver micrometastasis in pancreatic ductal adenocarcinoma via the activation of cancer–associated fibroblasts. Int J Oncol. 2020;56(2):596–605. https://doi.org/10.3892/ijo.2019.4951. Epub 2019 Dec 24. PMID: 31894273.

Article
CAS
PubMed

Google Scholar
Chen J, Huang Z, Chen Y, Tian H, Chai P, Shen Y, et al. Lactate and lactylation in cancer. Signal Transduct Target Ther. 2025;10(1):38. https://doi.org/10.1038/s41392-024-02082-x.

Article
CAS
PubMed
PubMed Central

Google Scholar
Wise AD, TenBarge EG, Mendonça JDC, Mennen EC, McDaniel SR, Reber CP, Holder BE, Bunch ML, Belevska E, Marshall MG, Vaccaro NM, Blakely CR, Wellawa DH, Ferris J, Sheldon JR, Bieber JD, Johnson JG, Burcham LR, Monteith AJ. Mitochondria sense bacterial lactate and drive release of neutrophil extracellular traps. Cell Host Microbe. 2025;33(3):341–e3579. Epub 2025 Feb 27. PMID: 40020664; PMCID: PMC11955204.

Article
CAS
PubMed

Google Scholar
Cao S, Liu P, Zhu H, Gong H, Yao J, Sun Y, et al. Extracellular acidification acts as a key modulator of neutrophil apoptosis and functions. PLoS One. 2015;10(9):e0139500. https://doi.org/10.1371/journal.pone.0139500.

Article
CAS
PubMed
PubMed Central

Google Scholar
Trevani AS, Andonegui G, Giordano M, López DH, Gamberale R, Minucci F, Geffner JR. Extracellular acidification induces human neutrophil activation. J Immunol. 1999;162(8):4849–57 PMID: 10202029.

Article
CAS
PubMed

Google Scholar
Díaz FE, Dantas E, Cabrera M, Benítez CA, Delpino MV, Duette G, et al. Fever-range hyperthermia improves the anti-apoptotic effect induced by low pH on human neutrophils promoting a proangiogenic profile. Cell Death Dis. 2016;7(10):e2437. https://doi.org/10.1038/cddis.2016.337.

Article
CAS
PubMed
PubMed Central

Google Scholar
Ashby BS. pH studies in human malignant tumours. Lancet. 1966;2(7458):312–5. https://doi.org/10.1016/s0140-6736(66)92598-0.

Article
CAS
PubMed

Google Scholar
Tannock IF, Rotin D. Acid pH in tumors and its potential for therapeutic exploitation. Cancer Res. 1989;49(16):4373–84. PMID: 2545340.

CAS
PubMed

Google Scholar
Estrella V, Chen T, Lloyd M, Wojtkowiak J, Cornnell HH, Ibrahim-Hashim A, Bailey K, Balagurunathan Y, Rothberg JM, Sloane BF, Johnson J, Gatenby RA, Gillies RJ. Acidity generated by the tumor microenvironment drives local invasion. Cancer Res. 2013;73(5):1524–35. https://doi.org/10.1158/0008-5472.CAN-12-2796. Epub 2013 Jan 3. PMID: 23288510; PMCID: PMC3594450.

Article
CAS
PubMed
PubMed Central

Google Scholar
Waldman AD, Fritz JM, Lenardo MJ. A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nat Rev Immunol. 2020;20(11):651–68. https://doi.org/10.1038/s41577-020-0306-5. Epub 2020 May 20. PMID: 32433532; PMCID: PMC7238960.

Article
CAS
PubMed
PubMed Central

Google Scholar
Ramezani-Ali Akbari K, Khaki-Bakhtiarvand V, Mahmoudian J, Asgarian-Omran H, Shokri F, Hojjat-Farsangi M, Jeddi-Tehrani M, Shabani M. Cloning, expression and characterization of a peptibody to deplete myeloid derived suppressor cells in a murine mammary carcinoma model. Protein Expr Purif. 2022;200:106153. https://doi.org/10.1016/j.pep.2022.106153. Epub 2022 Aug 19. PMID: 35995320.

Article
CAS
PubMed

Google Scholar
Qin H, Lerman B, Sakamaki I, Wei G, Cha SC, Rao SS, Qian J, Hailemichael Y, Nurieva R, Dwyer KC, Roth J, Yi Q, Overwijk WW, Kwak LW. Generation of a new therapeutic peptide that depletes myeloid-derived suppressor cells in tumor-bearing mice. Nat Med. 2014;20(6):676–81. https://doi.org/10.1038/nm.3560. Epub 2014 May 25. PMID: 24859530; PMCID: PMC4048321.

Article
CAS
PubMed
PubMed Central

Google Scholar
Tessier-Cloutier B, Twa DD, Marzban M, Kalina J, Chun HE, Pavey N, Tanweer Z, Katz RL, Lum JJ, Salina D. The presence of tumour-infiltrating neutrophils is an independent adverse prognostic feature in clear cell renal cell carcinoma. J Pathol Clin Res. 2021;7(4):385–96. Epub 2021 Mar 4. PMID: 33665979; PMCID: PMC8185362.

Article
CAS
PubMed
PubMed Central

Google Scholar
Xia X, Zhang Z, Zhu C, Ni B, Wang S, Yang S, Yu F, Zhao E, Li Q, Zhao G. Neutrophil extracellular traps promote metastasis in gastric cancer patients with postoperative abdominal infectious complications. Nat Commun. 2022;13(1):1017. https://doi.org/10.1038/s41467-022-28492-5. PMID: 35197446; PMCID: PMC8866499.

Article
CAS
PubMed
PubMed Central

Google Scholar
Guo M, Sheng W, Yuan X, Wang X. Neutrophils as promising therapeutic targets in pancreatic cancer liver metastasis. Int Immunopharmacol. 2024;140:112888. https://doi.org/10.1016/j.intimp.2024.112888. Epub 2024 Aug 11. PMID: 39133956.

Article
CAS
PubMed

Google Scholar
Zhang Y, Guo L, Dai Q, Shang B, Xiao T, Di X, Zhang K, Feng L, Shou J, Wang Y. A signature for pan-cancer prognosis based on neutrophil extracellular traps. J Immunother Cancer. 2022;10(6):e004210. https://doi.org/10.1136/jitc-2021-004210. PMID: 35688556; PMCID: PMC9189842.

Article
PubMed
PubMed Central

Google Scholar
Masucci MT, Minopoli M, Del Vecchio S, Carriero MV. The emerging role of neutrophil extracellular traps (NETs) in tumor progression and metastasis. Front Immunol. 2020;11:1749. https://doi.org/10.3389/fimmu.2020.01749. PMID: 33042107; PMCID: PMC7524869.

Article
CAS
PubMed
PubMed Central

Google Scholar
Gerstberger S, Jiang Q, Ganesh K, Metastasis. Cell. 2023;186(8):1564–79. https://doi.org/10.1016/j.cell.2023.03.003. PMID: 37059065; PMCID: PMC10511214.

Article
CAS
PubMed
PubMed Central

Google Scholar
Albrengues J, Shields MA, Ng D, Park CG, Ambrico A, Poindexter ME, Upadhyay P, Uyeminami DL, Pommier A, Küttner V, Bružas E, Maiorino L, Bautista C, Carmona EM, Gimotty PA, Fearon DT, Chang K, Lyons SK, Pinkerton KE, Trotman LC, Goldberg MS, Yeh JT, Egeblad M. Neutrophil extracellular traps produced during inflammation awaken dormant cancer cells in mice. Science. 2018;361(6409):eaao4227. https://doi.org/10.1126/science.aao4227. PMID: 30262472; PMCID: PMC6777850.

Article
CAS
PubMed
PubMed Central

Google Scholar
Fang Q, Stehr AM, Naschberger E, Knopf J, Herrmann M, Stürzl M. No NETs no TIME: crosstalk between neutrophil extracellular traps and the tumor immune microenvironment. Front Immunol. 2022;13:1075260. https://doi.org/10.3389/fimmu.2022.1075260. PMID: 36618417; PMCID: PMC9816414.

Article
CAS
PubMed
PubMed Central

Google Scholar
Ganesan R, Bhasin SS, Bakhtiary M, Krishnan U, Cheemarla NR, Thomas BE, Bhasin MK, Sukhatme VP. Taxane chemotherapy induces stromal injury that leads to breast cancer dormancy escape. PLoS Biol. 2023;21(9):e3002275. https://doi.org/10.1371/journal.pbio.3002275. PMID: 37699010; PMCID: PMC10497165.

Article
CAS
PubMed
PubMed Central

Google Scholar
Mousset A, Albrengues J. NETs unleashed: neutrophil extracellular traps boost chemotherapy against colorectal cancer. J Clin Invest. 2024;134(5):e178344. https://doi.org/10.1172/JCI178344. PMID: 38426501; PMCID: PMC10904039.

Article
CAS
PubMed
PubMed Central

Google Scholar
Teijeira Á, Garasa S, Gato M, Alfaro C, Migueliz I, Cirella A, de Andrea C, Ochoa MC, Otano I, Etxeberria I, Andueza MP, Nieto CP, Resano L, Azpilikueta A, Allegretti M, de Pizzol M, Ponz-Sarvisé M, Rouzaut A, Sanmamed MF, Schalper K, Carleton M, Mellado M, Rodriguez-Ruiz ME, Berraondo P, Perez-Gracia JL, Melero I. CXCR1 and CXCR2 chemokine receptor agonists produced by tumors induce neutrophil extracellular traps that interfere with immune cytotoxicity. Immunity. 2020;52(5):856–e8718. https://doi.org/10.1016/j.immuni.2020.03.001. Epub 2020 Apr 13. PMID: 32289253.

Article
CAS
PubMed

Google Scholar
Taifour T, Attalla SS, Zuo D, Gu Y, Sanguin-Gendreau V, Proud H, Solymoss E, Bui T, Kuasne H, Papavasiliou V, Lee CG, Kamle S, Siegel PM, Elias JA, Park M, Muller WJ. The tumor-derived cytokine Chi3l1 induces neutrophil extracellular traps that promote T cell exclusion in triple-negative breast cancer. Immunity. 2023;56(12):2755–e27728. Epub 2023 Nov 30. PMID: 38039967.

Article
CAS
PubMed

Google Scholar
Yang L, Liu Q, Zhang X, Liu X, Zhou B, Chen J, Huang D, Li J, Li H, Chen F, Liu J, Xing Y, Chen X, Su S, Song E. DNA of neutrophil extracellular traps promotes cancer metastasis via CCDC25. Nature. 2020;583(7814):133–8. https://doi.org/10.1038/s41586-020-2394-6. Epub 2020 Jun 11. PMID: 32528174.

Article
CAS
PubMed

Google Scholar
Najmeh S, Cools-Lartigue J, Rayes RF, Gowing S, Vourtzoumis P, Bourdeau F, Giannias B, Berube J, Rousseau S, Ferri LE, Spicer JD. Neutrophil extracellular traps sequester circulating tumor cells via β1-integrin mediated interactions. Int J Cancer. 2017;140(10):2321–30. https://doi.org/10.1002/ijc.30635. Epub 2017 Mar 2. PMID: 28177522.
Cools-Lartigue J, Spicer J, McDonald B, Gowing S, Chow S, Giannias B, Bourdeau F, Kubes P, Ferri L. Neutrophil extracellular traps sequester Circulating tumor cells and promote metastasis. J Clin Invest. 2013;123(8):3446–58. Epub ahead of print. PMID: 23863628; PMCID: PMC3726160.

Article
CAS
PubMed
PubMed Central

Google Scholar
Hu C, Long L, Lou J, Leng M, Yang Q, Xu X, Zhou X. CTC-neutrophil interaction: A key driver and therapeutic target of cancer metastasis. Biomed Pharmacother. 2024;180:117474. https://doi.org/10.1016/j.biopha.2024.117474. Epub 2024 Sep 23. PMID: 39316968.
Peinado H, Zhang H, Matei IR, Costa-Silva B, Hoshino A, Rodrigues G, Psaila B, Kaplan RN, Bromberg JF, Kang Y, Bissell MJ, Cox TR, Giaccia AJ, Erler JT, Hiratsuka S, Ghajar CM, Lyden D. Pre-metastatic niches: organ-specific homes for metastases. Nat Rev Cancer. 2017;17(5):302–17. https://doi.org/10.1038/nrc.2017.6. Epub 2017 Mar 17. PMID: 28303905.
Jia J, Wang Y, Li M, Wang F, Peng Y, Hu J, Li Z, Bian Z, Yang S. Neutrophils in the premetastatic niche: key functions and therapeutic directions. Mol Cancer. 2024;23(1):200. https://doi.org/10.1186/s12943-024-02107-7. PMID: 39277750; PMCID: PMC11401288.

Article
PubMed
PubMed Central

Google Scholar
Lee W, Ko SY, Mohamed MS, Kenny HA, Lengyel E, Naora H. Neutrophils facilitate ovarian cancer premetastatic niche formation in the omentum. J Exp Med. 2019;216(1):176–94. https://doi.org/10.1084/jem.20181170. Epub 2018 Dec 19. PMID: 30567719; PMCID: PMC6314534.

Article
CAS
PubMed
PubMed Central

Google Scholar
Lee W, Naora H. Neutrophils fertilize the pre-metastatic niche. Aging (Albany NY). 2019;11(17):6624–5. https://doi.org/10.18632/aging.102258. Epub 2019 Sep 10. PMID: 31509520. PMCID: PMC6756893.

Article
CAS
PubMed
PubMed Central

Google Scholar
Perego M, Tyurin VA, Tyurina YY, Yellets J, Nacarelli T, Lin C, Nefedova Y, Kossenkov A, Liu Q, Sreedhar S, Pass H, Roth J, Vogl T, Feldser D, Zhang R, Kagan VE, Gabrilovich DI. Reactivation of dormant tumor cells by modified lipids derived from stress-activated neutrophils. Sci Transl Med. 2020;12(572):eabb5817. https://doi.org/10.1126/scitranslmed.abb5817. PMID: 33268511; PMCID: PMC8085740.

Article
CAS
PubMed
PubMed Central

Google Scholar
Wculek SK, Malanchi I. Neutrophils support lung colonization of metastasis-initiating breast cancer cells. Nature. 2015;528(7582):413–7. https://doi.org/10.1038/nature16140 Epub 2015 Dec 9. Erratum in: Nature. 2019;571(7763):E2. https://doi.org/10.1038/s41586-019-1328-7 . PMID: 26649828; PMCID: PMC4700594. .

Article
CAS
PubMed
PubMed Central

Google Scholar
Liu Y, Gu Y, Han Y, Zhang Q, Jiang Z, Zhang X, Huang B, Xu X, Zheng J, Cao X. Tumor Exosomal RNAs Promote Lung Pre-metastatic Niche Formation by Activating Alveolar Epithelial TLR3 to Recruit Neutrophils. Cancer Cell. 2016;30(2):243–56. https://doi.org/10.1016/j.ccell.2016.06.021. PMID: 27505671.
Bellomo G, Rainer C, Quaranta V, Astuti Y, Raymant M, Boyd E, Stafferton R, Campbell F, Ghaneh P, Halloran CM, Hammond DE, Morton JP, Palmer D, Vimalachandran D, Jones R, Mielgo A, Schmid MC. Chemotherapy-induced infiltration of neutrophils promotes pancreatic cancer metastasis via Gas6/AXL signalling axis. Gut. 2022;71(11):2284–99. https://doi.org/10.1136/gutjnl-2021-325272. Epub 2022 Jan 12. PMID: 35022267; PMCID: PMC9554050.

Article
CAS
PubMed

Google Scholar
Rys RN, Calcinotto A. Senescent neutrophils: a hidden role in cancer progression. Trends Cell Biol. 2024;S0962–8924(24):00187–9. https://doi.org/10.1016/j.tcb.2024.09.001. Epub ahead of print. PMID: 39362804.

Article
CAS

Google Scholar
Tyagi A, Sharma S, Wu K, Wu SY, Xing F, Liu Y, Zhao D, Deshpande RP, D’Agostino RB Jr, Watabe K. Nicotine promotes breast cancer metastasis by stimulating N2 neutrophils and generating pre-metastatic niche in lung. Nat Commun. 2021;12(1):474. https://doi.org/10.1038/s41467-020-20733-9. PMID: 33473115; PMCID: PMC7817836.

Article
CAS
PubMed
PubMed Central

Google Scholar
Wieland E, Rodriguez-Vita J, Liebler SS, Mogler C, Moll I, Herberich SE, Espinet E, Herpel E, Menuchin A, Chang-Claude J, Hoffmeister M, Gebhardt C, Brenner H, Trumpp A, Siebel CW, Hecker M, Utikal J, Sprinzak D, Fischer A. Endothelial Notch1 activity facilitates metastasis. Cancer Cell. 2017;31(3):355–67. Epub 2017 Feb 23. PMID: 28238683.

Article
CAS
PubMed

Google Scholar
Fang JH, Zhang ZJ, Shang LR, Luo YW, Lin YF, Yuan Y, Zhuang SM. Hepatoma cell-secreted Exosomal microRNA-103 increases vascular permeability and promotes metastasis by targeting junction proteins. Hepatology. 2018;68(4):1459–75. https://doi.org/10.1002/hep.29920. Epub 2018 Jul 25. PMID: 29637568.

Article
CAS
PubMed

Google Scholar
Kuang DM, Zhao Q, Wu Y, Peng C, Wang J, Xu Z, Yin XY, Zheng L. Peritumoral neutrophils link inflammatory response to disease progression by fostering angiogenesis in hepatocellular carcinoma. J Hepatol. 2011;54(5):948–55. Epub 2010 Nov 13. PMID: 21145847.

Article
CAS
PubMed

Google Scholar
Kuang DM, Peng C, Zhao Q, Wu Y, Zhu LY, Wang J, Yin XY, Li L, Zheng L. Tumor-activated monocytes promote expansion of IL-17-producing CD8 + T cells in hepatocellular carcinoma patients. J Immunol. 2010;185(3):1544–9. https://doi.org/10.4049/jimmunol.0904094. Epub 2010 Jun 25. PMID: 20581151.

Article
CAS
PubMed

Google Scholar
Jiang ZZ, Peng ZP, Liu XC, Guo HF, Zhou MM, Jiang D, Ning WR, Huang YF, Zheng L, Wu Y. Neutrophil extracellular traps induce tumor metastasis through dual effects on cancer and endothelial cells. Oncoimmunology. 2022;11(1):2052418. PMID: 35309732; PMCID: PMC8928819.

Article
PubMed
PubMed Central

Google Scholar
Lu K, Xia Y, Cheng P, Li Y, He L, Tao L, Wei Z, Lu Y. Synergistic potentiation of the anti-metastatic effect of a Ginseng-Salvia miltiorrhiza herbal pair and its biological ingredients via the suppression of CD62E-dependent neutrophil infiltration and NETformation. J Adv Res. 2024;S2090–1232(24)00490-9. https://doi.org/10.1016/j.jare.2024.10.036. Epub ahead of print. PMID: 39481643.
Martins-Cardoso K, Almeida VH, Bagri KM, Rossi MID, Mermelstein CS, König S, Monteiro RQ. Neutrophil extracellular traps (NETs) promote Pro-Metastatic phenotype in human breast cancer cells through Epithelial-Mesenchymal transition. Cancers (Basel). 2020;12(6):1542. https://doi.org/10.3390/cancers12061542. PMID: 32545405; PMCID: PMC7352979.

Article
CAS
PubMed

Google Scholar
Kajioka H, Kagawa S, Ito A, Yoshimoto M, Sakamoto S, Kikuchi S, Kuroda S, Yoshida R, Umeda Y, Noma K, Tazawa H, Fujiwara T. Targeting neutrophil extracellular traps with thrombomodulin prevents pancreatic cancer metastasis. Cancer Lett. 2021;497:1–13. Epub 2020 Oct 13. PMID: 33065249.

Article
CAS
PubMed

Google Scholar
Weide LM, Schedel F, Weishaupt C. Neutrophil extracellular traps correlate with tumor necrosis and size in human malignant melanoma metastases. Biology (Basel). 2023;12(6):822. https://doi.org/10.3390/biology12060822. PMID: 37372107; PMCID: PMC10295294.

Article
CAS
PubMed

Google Scholar
Yee PP, Wei Y, Kim SY, Lu T, Chih SY, Lawson C, Tang M, Liu Z, Anderson B, Thamburaj K, Young MM, Aregawi DG, Glantz MJ, Zacharia BE, Specht CS, Wang HG, Li W. Neutrophil-induced ferroptosis promotes tumor necrosis in glioblastoma progression. Nat Commun. 2020;11(1):5424. https://doi.org/10.1038/s41467-020-19193-y. PMID: 33110073; PMCID: PMC7591536.

Article
CAS
PubMed
PubMed Central

Google Scholar
Chen F, Tang H, Cai X, Lin J, Kang R, Tang D, Liu J. DAMPs in Immunosenescence and cancer. Semin Cancer Biol. 2024;106–107:123–42. Epub ahead of print. PMID: 39349230.

Article
PubMed

Google Scholar
Tang D, Kang R, Zeh HJ, Lotze MT. The multifunctional protein HMGB1: 50 years of discovery. Nat Rev Immunol. 2023;23(12):824–41. https://doi.org/10.1038/s41577-023-00894-6. Epub 2023 Jun 15. PMID: 37322174.

Article
CAS
PubMed

Google Scholar
Liu Y, Yan W, Tohme S, Chen M, Fu Y, Tian D, Lotze M, Tang D, Tsung A. Hypoxia induced HMGB1 and mitochondrial DNA interactions mediate tumor growth in hepatocellular carcinoma through Toll-like receptor 9. J Hepatol. 2015;63(1):114–21. Epub 2015 Feb 12. PMID: 25681553; PMCID: PMC4475488.

Article
CAS
PubMed
PubMed Central

Google Scholar
Yang Y, Yang J, Li L, Shao Y, Liu L, Sun B. Neutrophil chemotaxis score and chemotaxis-related genes have the potential for clinical application to prognosticate the survival of patients with tumours. BMC Cancer. 2024;24(1):1244. https://doi.org/10.1186/s12885-024-12993-1. PMID: 39379856; PMCID: PMC11463147.

Article
CAS
PubMed
PubMed Central

Google Scholar
Kamphorst AO, Wieland A, Nasti T, Yang S, Zhang R, Barber DL, Konieczny BT, Daugherty CZ, Koenig L, Yu K, Sica GL, Sharpe AH, Freeman GJ, Blazar BR, Turka LA, Owonikoko TK, Pillai RN, Ramalingam SS, Araki K, Ahmed R. Rescue of exhausted CD8 T cells by PD-1-targeted therapies is CD28-dependent. Science. 2017;355(6332):1423–7. https://doi.org/10.1126/science.aaf0683. Epub 2017 Mar 9. PMID: 28280249; PMCID: PMC5595217.

Article
CAS
PubMed
PubMed Central

Google Scholar
ten Hoeve J, Morris C, Heisterkamp N, Groffen J. Isolation and chromosomal localization of CRKL, a human crk-like gene. Oncogene. 1993;8(9):2469–74. PMID: 8361759.

PubMed

Google Scholar
Zhang J, Gao X, Schmit F, Adelmant G, Eck MJ, Marto JA, Zhao JJ, Roberts TM. CRKL mediates p110β-Dependent PI3K signaling in PTEN-Deficient cancer cells. Cell Rep. 2017;20(3):549–57. PMID: 28723560; PMCID: PMC5704918.

Article
CAS
PubMed
PubMed Central

Google Scholar
Sattler M, Salgia R. Role of the adapter protein CRKL in signal transduction of normal hematopoietic and BCR/ABL-transformed cells. Leukemia. 1998;12(5):637–44. https://doi.org/10.1038/sj.leu.2401010. PMID: 9593259.
Xie P, Yu M, Zhang B, Yu Q, Zhao Y, Wu M, Jin L, Yan J, Zhou B, Liu S, Li X, Zhou C, Zhu X, Huang C, Xu Y, Xiao Y, Zhou J, Fan J, Hung MC, Ye Q, Guo L, Li H. CRKL dictates anti-PD-1 resistance by mediating tumor-associated neutrophil infiltration in hepatocellular carcinoma. J Hepatol. 2024;81(1):93–107. https://doi.org/10.1016/j.jhep.2024.02.009. Epub 2024 Feb 23. PMID: 38403027.

Article
CAS
PubMed

Google Scholar
Lee YH, Martin-Orozco N, Zheng P, Li J, Zhang P, Tan H, Park HJ, Jeong M, Chang SH, Kim BS, Xiong W, Zang W, Guo L, Liu Y, Dong ZJ, Overwijk WW, Hwu P, Yi Q, Kwak L, Yang Z, Mak TW, Li W, Radvanyi LG, Ni L, Liu D, Dong C. Inhibition of the B7-H3 immune checkpoint limits tumor growth by enhancing cytotoxic lymphocyte function. Cell Res. 2017;27(8):1034–45. https://doi.org/10.1038/cr.2017.90. Epub 2017 Jul 7. PMID: 28685773; PMCID: PMC5539354.

Article
CAS
PubMed
PubMed Central

Google Scholar
Suh WK, Gajewska BU, Okada H, Gronski MA, Bertram EM, Dawicki W, Duncan GS, Bukczynski J, Plyte S, Elia A, Wakeham A, Itie A, Chung S, Da Costa J, Arya S, Horan T, Campbell P, Gaida K, Ohashi PS, Watts TH, Yoshinaga SK, Bray MR, Jordana M, Mak TW. The B7 family member B7-H3 preferentially down-regulates T helper type 1-mediated immune responses. Nat Immunol. 2003;4(9):899–906. https://doi.org/10.1038/ni967. Epub 2003 Aug 17. PMID: 12925852.

Article
CAS
PubMed

Google Scholar
Xiong G, Chen Z, Liu Q, Peng F, Zhang C, Cheng M, Ling R, Chen S, Liang Y, Chen D, Zhou Q. CD276 regulates the immune escape of esophageal squamous cell carcinoma through CXCL1-CXCR2 induced NETs. J Immunother Cancer. 2024;12(5):e008662. https://doi.org/10.1136/jitc-2023-008662. PMID: 38724465; PMCID: PMC11086492.

Article
PubMed
PubMed Central

Google Scholar
Song M, Zhang C, Cheng S, Ouyang D, Ping Y, Yang J, Zhang Y, Tang Y, Chen H, Wang QJ, Li YQ, He J, Xiang T, Zhang Y, Xia JC. DNA of Neutrophil Extracellular Traps Binds TMCO6 to Impair CD8 + T-cell Immunity in Hepatocellular Carcinoma. Cancer Res. 2024;84(10):1613–29. https://doi.org/10.1158/0008-5472.CAN-23-2986. PMID: 38381538.
Teijeira A, Garasa S, Ochoa MC, Villalba M, Olivera I, Cirella A, Eguren-Santamaria I, Berraondo P, Schalper KA, de Andrea CE, Sanmamed MF, Melero I. IL8, Neutrophils, and NETs in a collusion against cancer immunity and immunotherapy. Clin Cancer Res. 2021;27(9):2383–93. https://doi.org/10.1158/1078-0432.CCR-20-1319. Epub 2020 Dec 29. PMID: 33376096.

Article
CAS
PubMed

Google Scholar
Zhu X, Heng Y, Ma J, Zhang D, Tang D, Ji Y, He C, Lin H, Ding X, Zhou J, Tao L, Lu L. Prolonged survival of neutrophils induced by Tumor-Derived G-CSF/GM-CSF promotes immunosuppression and progression in laryngeal squamous cell carcinoma. Adv Sci (Weinh). 2024;11(46):e2400836. https://doi.org/10.1002/advs.202400836. Epub 2024 Oct 24. PMID: 39447112; PMCID: PMC11633501.

Article
CAS
PubMed

Google Scholar
Meng Y, Ye F, Nie P, Zhao Q, An L, Wang W, Qu S, Shen Z, Cao Z, Zhang X, Jiao S, Wu D, Zhou Z, Wei L. Immunosuppressive CD10 + ALPL + neutrophils promote resistance to anti-PD-1 therapy in HCC by mediating irreversible exhaustion of T cells. J Hepatol. 2023;79(6):1435–49. Epub 2023 Sep 7. PMID: 37689322.

Article
CAS
PubMed

Google Scholar
Gungabeesoon J, Gort-Freitas NA, Kiss M, Bolli E, Messemaker M, Siwicki M, Hicham M, Bill R, Koch P, Cianciaruso C, Duval F, Pfirschke C, Mazzola M, Peters S, Homicsko K, Garris C, Weissleder R, Klein AM, Pittet MJ. A neutrophil response linked to tumor control in immunotherapy. Cell. 2023;186(7):1448–e146420. https://doi.org/10.1016/j.cell.2023.02.032. PMID: 37001504; PMCID: PMC10132778.

Article
CAS
PubMed
PubMed Central

Google Scholar
Hirschhorn D, Budhu S, Kraehenbuehl L, Gigoux M, Schröder D, Chow A, Ricca JM, Gasmi B, De Henau O, Mangarin LMB, Li Y, Hamadene L, Flamar AL, Choi H, Cortez CA, Liu C, Holland A, Schad S, Schulze I, Betof Warner A, Hollmann TJ, Arora A, Panageas KS, Rizzuto GA, Duhen R, Weinberg AD, Spencer CN, Ng D, He XY, Albrengues J, Redmond D, Egeblad M, Wolchok JD, Merghoub T. T cell immunotherapies engage neutrophils to eliminate tumor antigen escape variants. Cell. 2023;186(7):1432–e144717. https://doi.org/10.1016/j.cell.2023.03.007. PMID: 37001503; PMCID: PMC10994488.

Article
CAS
PubMed
PubMed Central

Google Scholar
Zeng W, Zhang R, Huang P, Chen M, Chen H, Zeng X, Liu J, Zhang J, Huang D, Lao L. Ferroptotic neutrophils induce immunosuppression and chemoresistance in breast cancer. Cancer Res. 2025;85(3):477–96. PMID: 39531510; PMCID: PMC11786957.

Article
CAS
PubMed

Google Scholar
Caronni N, La Terza F, Vittoria FM, Barbiera G, Mezzanzanica L, Cuzzola V, Barresi S, Pellegatta M, Canevazzi P, Dunsmore G, Leonardi C, Montaldo E, Lusito E, Dugnani E, Citro A, Ng MSF, Schiavo Lena M, Drago D, Andolfo A, Brugiapaglia S, Scagliotti A, Mortellaro A, Corbo V, Liu Z, Mondino A, Dellabona P, Piemonti L, Taveggia C, Doglioni C, Cappello P, Novelli F, Iannacone M, Ng LG, Ginhoux F, Crippa S, Falconi M, Bonini C, Naldini L, Genua M, Ostuni R. IL-1β + macrophages fuel pathogenic inflammation in pancreatic cancer. Nature. 2023;623(7986):415–22. https://doi.org/10.1038/s41586-023-06685-2. Epub 2023 Nov 1. PMID: 37914939.

Article
CAS
PubMed

Google Scholar
Kaler P, Augenlicht L, Klampfer L. Macrophage-derived IL-1beta stimulates Wnt signaling and growth of colon cancer cells: a crosstalk interrupted by vitamin D3. Oncogene. 2009;28(44):3892–902. https://doi.org/10.1038/onc.2009.247. Epub 2009 Aug 24. PMID: 19701245; PMCID: PMC2783659.

Article
CAS
PubMed
PubMed Central

Google Scholar
Mousset A, Lecorgne E, Bourget I, Lopez P, Jenovai K, Cherfils-Vicini J, Dominici C, Rios G, Girard-Riboulleau C, Liu B, Spector DL, Ehmsen S, Renault S, Hego C, Mechta-Grigoriou F, Bidard FC, Terp MG, Egeblad M, Gaggioli C, Albrengues J. Neutrophil extracellular traps formed during chemotherapy confer treatment resistance via TGF-β activation. Cancer Cell. 2023;41(4):757–e77510. https://doi.org/10.1016/j.ccell.2023.03.008. PMID: 37037615; PMCID: PMC10228050.

Article
CAS
PubMed
PubMed Central

Google Scholar
Nakazawa D, Kumar SV, Marschner J, Desai J, Holderied A, Rath L, Kraft F, Lei Y, Fukasawa Y, Moeckel GW, Angelotti ML, Liapis H, Anders HJ. Histones and neutrophil extracellular traps enhance tubular necrosis and remote organ injury in ischemic AKI. J Am Soc Nephrol. 2017;28(6):1753–68. Epub 2017 Jan 10. PMID: 28073931; PMCID: PMC5461800.

Article
CAS
PubMed
PubMed Central

Google Scholar
Mousset A, Bellone L, Gaggioli C, Albrengues J. NETscape or nethance: tailoring anti-cancer therapy. Trends Cancer. 2024;10(7):655–67. https://doi.org/10.1016/j.trecan.2024.03.007. Epub 2024 Apr 24. PMID: 38664080.

Article
CAS
PubMed

Google Scholar
Teijeira A, Garasa S, Ochoa MC, Sanchez-Gregorio S, Gomis G, Luri-Rey C, Martinez-Monge R, Pinci B, Valencia K, Palencia B, Barbés B, Bolaños E, Azpilikueta A, García-Cardosa M, Burguete J, Eguren-Santamaría I, Garate-Soraluze E, Berraondo P, Perez-Gracia JL, de Andrea CE, Rodriguez-Ruiz ME, Melero I. Low-Dose ionizing γ-Radiation elicits the extrusion of neutrophil extracellular traps. Clin Cancer Res. 2024;30(18):4131–42. https://doi.org/10.1158/1078-0432.CCR-23-3860. PMID: 38630754; PMCID: PMC11393545.

Article
CAS
PubMed
PubMed Central

Google Scholar
Takeshima T, Pop LM, Laine A, Iyengar P, Vitetta ES, Hannan R. Key role for neutrophils in radiation-induced antitumor immune responses: potentiation with G-CSF. Proc Natl Acad Sci U S A. 2016;113(40):11300–5. https://doi.org/10.1073/pnas.1613187113. Epub 2016 Sep 20. PMID: 27651484; PMCID: PMC5056034.

Article
CAS
PubMed
PubMed Central

Google Scholar
Elvington M, Scheiber M, Yang X, Lyons K, Jacqmin D, Wadsworth C, Marshall D, Vanek K, Tomlinson S. Complement-dependent modulation of antitumor immunity following radiation therapy. Cell Rep. 2014;8(3):818–30. https://doi.org/10.1016/j.celrep.2014.06.051. Epub 2014 Jul 24. PMID: 25066124; PMCID: PMC4137409.

Article
CAS
PubMed
PubMed Central

Google Scholar
Nolan E, Bridgeman VL, Ombrato L, Karoutas A, Rabas N, Sewnath CAN, Vasquez M, Rodrigues FS, Horswell S, Faull P, Carter R, Malanchi I. Radiation exposure elicits a neutrophil-driven response in healthy lung tissue that enhances metastatic colonization. Nat Cancer. 2022;3(2):173–87. https://doi.org/10.1038/s43018-022-00336-7 Epub 2022 Feb 24. Erratum in: Nat Cancer. 2022;3(4):519. https://doi.org/10.1038/s43018-022-00373-2. PMID: 35221334; PMCID: PMC7612918 .

Article
CAS
PubMed
PubMed Central

Google Scholar
de Ruiz-Fernández B, Moreno H, Valencia K, Perurena N, Ruedas P, Walle T, Pezonaga-Torres A, Hinojosa J, Guruceaga E, Pineda-Lucena A, Abengózar-Muela M, Cochonneau D, Zandueta C, Martínez-Canarias S, Teijeira Á, Ajona D, Ortiz-Espinosa S, Morales X, de Ortiz C, Santisteban M, Ramos-García LI, Guembe L, Strnad V, Heymann D, Hervás-Stubbs S, Pío R, Rodríguez-Ruiz ME, de Andrea CE, Vicent S, Melero I, Lecanda F, Martínez-Monge R. Tumor ENPP1 (CD203a)/Haptoglobin axis exploits Myeloid-Derived suppressor cells to promote Post-Radiotherapy local recurrence in breast cancer. Cancer Discov. 2022;12(5):1356–77. https://doi.org/10.1158/2159-8290.CD-21-0932. PMID: 35191482; PMCID: PMC7612709.

Article

Google Scholar
Xia X, Zhang Z, Zhu C, Ni B, Wang S, Yang S, Yu F, Zhao E, Li Q, Zhao G. Neutrophil extracellular traps promote metastasis in gastric cancer patients with postoperative abdominal infectious complications. Nat Commun. 2022;13(1):1017. https://doi.org/10.1038/s41467-022-28492-5. Erratum in: Nat Commun. 2025;16(1):2381. https://doi.org/10.1038/s41467-025-57805-7. PMID: 35197446; PMCID: PMC8866499.
Shinde-Jadhav S, Mansure JJ, Rayes RF, Marcq G, Ayoub M, Skowronski R, Kool R, Bourdeau F, Brimo F, Spicer J, Kassouf W. Role of neutrophil extracellular traps in radiation resistance of invasive bladder cancer. Nat Commun. 2021;12(1):2776. https://doi.org/10.1038/s41467-021-23086-z. PMID: 33986291; PMCID: PMC8119713.

Article
CAS
PubMed
PubMed Central

Google Scholar
Metzler KD, Goosmann C, Lubojemska A, Zychlinsky A, Papayannopoulos V. A myeloperoxidase-containing complex regulates neutrophil elastase release and actin dynamics during NETosis. Cell Rep. 2014;8(3):883–96. https://doi.org/10.1016/j.celrep.2014.06.044. Epub 2014 Jul 24. PMID: 25066128; PMCID: PMC4471680.

Article
CAS
PubMed
PubMed Central

Google Scholar
Kawabata K, Hagio T, Matsuoka S. The role of neutrophil elastase in acute lung injury. Eur J Pharmacol. 2002;451(1):1–10. https://doi.org/10.1016/s0014-2999(02)02182-9. PMID: 12223222.
Hunter MG, Druhan LJ, Massullo PR, Avalos BR. Proteolytic cleavage of granulocyte colony-stimulating factor and its receptor by neutrophil elastase induces growth inhibition and decreased cell surface expression of the granulocyte colony-stimulating factor receptor. Am J Hematol. 2003;74(3):149–55. https://doi.org/10.1002/ajh.10434. PMID: 14587040.
Valenzuela-Fernández A, Planchenault T, Baleux F, Staropoli I, Le-Barillec K, Leduc D, Delaunay T, Lazarini F, Virelizier JL, Chignard M, Pidard D, Arenzana-Seisdedos F. Leukocyte elastase negatively regulates stromal cell-derived factor-1 (SDF-1)/CXCR4 binding and functions by amino-terminal processing of SDF-1 and CXCR4. J Biol Chem. 2002;277(18):15677–89. https://doi.org/10.1074/jbc.M111388200. Epub 2002 Feb 26. PMID: 11867624.

Article
CAS
PubMed

Google Scholar
Shang Y, Jiang YX, Ding ZJ, Shen AL, Xu SP, Yuan SY, Yao SL. Valproic acid attenuates the multiple-organ dysfunction in a rat model of septic shock. Chin Med J (Engl). 2010;123(19):2682–7. PMID: 21034653.

CAS
PubMed

Google Scholar
Aldabbous L, Abdul-Salam V, McKinnon T, Duluc L, Pepke-Zaba J, Southwood M, Ainscough AJ, Hadinnapola C, Wilkins MR, Toshner M, Wojciak-Stothard B. Neutrophil extracellular traps promote angiogenesis: evidence from vascular pathology in pulmonary hypertension. Arterioscler Thromb Vasc Biol. 2016;36(10):2078–87. https://doi.org/10.1161/ATVBAHA.116.307634. Epub 2016 Jul 28. PMID: 27470511.

Article
CAS
PubMed

Google Scholar
Houghton AM, Rzymkiewicz DM, Ji H, Gregory AD, Egea EE, Metz HE, Stolz DB, Land SR, Marconcini LA, Kliment CR, Jenkins KM, Beaulieu KA, Mouded M, Frank SJ, Wong KK, Shapiro SD. Neutrophil elastase-mediated degradation of IRS-1 accelerates lung tumor growth. Nat Med. 2010;16(2):219–23. Epub 2010 Jan 17. PMID: 20081861; PMCID: PMC2821801.

Article
CAS
PubMed
PubMed Central

Google Scholar
Caruso JA, Akli S, Pageon L, Hunt KK, Keyomarsi K. The Serine protease inhibitor Elafin maintains normal growth control by opposing the mitogenic effects of neutrophil elastase. Oncogene. 2015;34(27):3556–67. https://doi.org/10.1038/onc.2014.284. Epub 2014 Sep 8. PMID: 25195861; PMCID: PMC4362782.

Article
CAS
PubMed

Google Scholar
Ho AS, Chen CH, Cheng CC, Wang CC, Lin HC, Luo TY, Lien GS, Chang J. Neutrophil elastase as a diagnostic marker and therapeutic target in colorectal cancers. Oncotarget. 2014;5(2):473–80. https://doi.org/10.18632/oncotarget.1631. PMID: 24457622; PMCID: PMC3964222.

Article
PubMed
PubMed Central

Google Scholar
Foekens JA, Ries C, Look MP, Gippner-Steppert C, Klijn JG, Jochum M. The prognostic value of polymorphonuclear leukocyte elastase in patients with primary breast cancer. Cancer Res. 2003;63(2):337–41. PMID: 12543785.

CAS
PubMed

Google Scholar
Lerman I, Garcia-Hernandez ML, Rangel-Moreno J, Chiriboga L, Pan C, Nastiuk KL, Krolewski JJ, Sen A, Hammes SR. Infiltrating myeloid cells exert protumorigenic actions via neutrophil elastase. Mol Cancer Res. 2017;15(9):1138–52. https://doi.org/10.1158/1541-7786.MCR-17-0003. Epub 2017 May 16. PMID: 28512253; PMCID: PMC5581693.

Article
CAS
PubMed
PubMed Central

Google Scholar
Zeng W, Song Y, Wang R, He R, Wang T. Neutrophil elastase: from mechanisms to therapeutic potential. J Pharm Anal. 2023;13(4):355–66. https://doi.org/10.1016/j.jpha.2022.12.003. Epub 2023 Jan 7. PMID: 37181292; PMCID: PMC10173178.

Article
CAS
PubMed
PubMed Central

Google Scholar
Wada Y, Yoshida K, Tsutani Y, Shigematsu H, Oeda M, Sanada Y, Suzuki T, Mizuiri H, Hamai Y, Tanabe K, Ukon K, Hihara J. Neutrophil elastase induces cell proliferation and migration by the release of TGF-alpha, PDGF and VEGF in esophageal cell lines. Oncol Rep. 2007;17(1):161–7. PMID: 17143494.

CAS
PubMed

Google Scholar
Wada Y, Yoshida K, Hihara J, Konishi K, Tanabe K, Ukon K, Taomoto J, Suzuki T, Mizuiri H. Sivelestat, a specific neutrophil elastase inhibitor, suppresses the growth of gastric carcinoma cells by preventing the release of transforming growth factor-alpha. Cancer Sci. 2006;97(10):1037–43. https://doi.org/10.1111/j.1349-7006.2006.00278.x. Epub 2006 Aug 17. PMID: 16918998; PMCID: PMC11158772.

Article
CAS
PubMed
PubMed Central

Google Scholar
Lerman I, Hammes SR. Neutrophil elastase in the tumor microenvironment. Steroids. 2018;133:96–101. https://doi.org/10.1016/j.steroids.2017.11.006. Epub 2017 Nov 16. PMID: 29155217; PMCID: PMC5870895.

Article
CAS
PubMed

Google Scholar
Pivetta E, Danussi C, Wassermann B, Modica TM, Del Bel Belluz L, Canzonieri V, Colombatti A, Spessotto P. Neutrophil elastase-dependent cleavage compromises the tumor suppressor role of EMILIN1. Matrix Biol. 2014;34:22–32. Epub 2014 Feb 7. PMID: 24513040.

Article
CAS
PubMed

Google Scholar
Maiorani O, Pivetta E, Capuano A, Modica TM, Wassermann B, Bucciotti F, Colombatti A, Doliana R, Spessotto P. Neutrophil elastase cleavage of the gC1q domain impairs the EMILIN1-α4β1 integrin interaction, cell adhesion and anti-proliferative activity. Sci Rep. 2017;7:39974. https://doi.org/10.1038/srep39974. PMID: 28074935; PMCID: PMC5225433.

Article
CAS
PubMed
PubMed Central

Google Scholar
El Rayes T, Catena R, Lee S, Stawowczyk M, Joshi N, Fischbach C, Powell CA, Dannenberg AJ, Altorki NK, Gao D, Mittal V. Lung inflammation promotes metastasis through neutrophil protease-mediated degradation of Tsp-1. Proc Natl Acad Sci U S A. 2015;112(52):16000–5. https://doi.org/10.1073/pnas.1507294112. Epub 2015 Dec 14. PMID: 26668367; PMCID: PMC4703007.

Article
CAS
PubMed
PubMed Central

Google Scholar
Tamakuma S, Ogawa M, Aikawa N, Kubota T, Hirasawa H, Ishizaka A, Taenaka N, Hamada C, Matsuoka S, Abiru T. Relationship between neutrophil elastase and acute lung injury in humans. Pulm Pharmacol Ther. 2004;17(5):271–9. https://doi.org/10.1016/j.pupt.2004.05.003. PMID: 15477122.
Matsuzaki K, Hiramatsu Y, Homma S, Sato S, Shigeta O, Sakakibara Y. Sivelestat reduces inflammatory mediators and preserves neutrophil deformability during simulated extracorporeal circulation. Ann Thorac Surg. 2005;80(2):611–7. https://doi.org/10.1016/j.athoracsur.2005.02.038. PMID: 16039215.
Aikawa N, Kawasaki Y. Clinical utility of the neutrophil elastase inhibitor Sivelestat for the treatment of acute respiratory distress syndrome. Ther Clin Risk Manag. 2014;10:621–9. https://doi.org/10.2147/TCRM.S65066. PMID: 25120368; PMCID: PMC4130327.

Article
CAS
PubMed
PubMed Central

Google Scholar
Okamoto M, Mizuno R, Kawada K, Itatani Y, Kiyasu Y, Hanada K, Hirata W, Nishikawa Y, Masui H, Sugimoto N, Tamura T, Inamoto S, Sakai Y, Obama K. Neutrophil extracellular traps promote metastases of colorectal cancers through activation of ERK signaling by releasing neutrophil elastase. Int J Mol Sci. 2023;24(2):1118. https://doi.org/10.3390/ijms24021118. PMID: 36674635; PMCID: PMC9867023.

Article
CAS
PubMed
PubMed Central

Google Scholar
Rayes RF, Mouhanna JG, Nicolau I, Bourdeau F, Giannias B, Rousseau S, Quail D, Walsh L, Sangwan V, Bertos N, Cools-Lartigue J, Ferri LE, Spicer JD. Primary tumors induce neutrophil extracellular traps with targetable metastasis promoting effects. JCI Insight. 2019;5(16):e128008. https://doi.org/10.1172/jci.insight.128008. PMID: 31343990; PMCID: PMC6777835.

Article
PubMed

Google Scholar
Silva CMS, Wanderley CWS, Veras FP, Sonego F, Nascimento DC, Gonçalves AV, Martins TV, Cólon DF, Borges VF, Brauer VS, Damasceno LEA, Silva KP, Toller-Kawahisa JE, Batah SS, Souza ALJ, Monteiro VS, Oliveira AER, Donate PB, Zoppi D, Borges MC, Almeida F, Nakaya HI, Fabro AT, Cunha TM, Alves-Filho JC, Zamboni DS, Cunha FQ. Gasdermin D Inhibition prevents multiple organ dysfunction during sepsis by blocking NET formation. Blood. 2021;138(25):2702–13. https://doi.org/10.1182/blood.2021011525. PMID: 34407544; PMCID: PMC8703366.

Article
CAS
PubMed
PubMed Central

Google Scholar
Jia W, Mao Y, Luo Q, Wu J, Guan Q. Targeting neutrophil elastase is a promising direction for future cancer treatment. Discov Oncol. 2024;15(1):167. https://doi.org/10.1007/s12672-024-01010-3. PMID: 38750338; PMCID: PMC11096153.

Article
CAS
PubMed
PubMed Central

Google Scholar
Cui C, Chakraborty K, Tang XA, Zhou G, Schoenfelt KQ, Becker KM, Hoffman A, Chang YF, Blank A, Reardon CA, Kenny HA, Vaisar T, Lengyel E, Greene G, Becker L. Neutrophil elastase selectively kills cancer cells and attenuates tumorigenesis. Cell. 2021;184(12):3163–e317721. Epub 2021 May 7. PMID: 33964209; PMCID: PMC10712736.

Article
CAS
PubMed
PubMed Central

Google Scholar
Eruslanov EB, Singhal S, Albelda SM. Mouse versus human neutrophils in cancer: A major knowledge gap. Trends Cancer. 2017;3(2):149–60. https://doi.org/10.1016/j.trecan.2016.12.006. Epub 2017 Jan 19. PMID: 28718445; PMCID: PMC5518602.

Article
CAS
PubMed
PubMed Central

Google Scholar
Kessenbrock K, Plaks V, Werb Z. Matrix metalloproteinases: regulators of the tumor microenvironment. Cell. 2010;141(1):52–67. https://doi.org/10.1016/j.cell.2010.03.015. PMID: 20371345; PMCID: PMC2862057.

Article
CAS
PubMed
PubMed Central

Google Scholar
Mondal S, Adhikari N, Banerjee S, Amin SA, Jha T. Matrix metalloproteinase-9 (MMP-9) and its inhibitors in cancer: A minireview. Eur J Med Chem. 2020;194:112260. https://doi.org/10.1016/j.ejmech.2020.112260. Epub 2020 Mar 21. Erratum in: Eur J Med Chem. 2020;205:112642. https://doi.org/10.1016/j.ejmech.2020.112642. PMID: 32224379.
Cai N, Cheng K, Ma Y, Liu S, Tao R, Li Y, Li D, Guo B, Jia W, Liang H, Zhao J, Xia L, Ding ZY, Chen J, Zhang W. Targeting MMP9 in CTNNB1 mutant hepatocellular carcinoma restores CD8 + T cell-mediated antitumour immunity and improves anti-PD-1 efficacy. Gut. 2024;73(6):985–99. https://doi.org/10.1136/gutjnl-2023-331342. PMID: 38123979; PMCID: PMC11103337.

Article
CAS
PubMed

Google Scholar
Lee CJ, Jang TY, Jeon SE, Yun HJ, Cho YH, Lim DY, Nam JS. The dysadherin/MMP9 axis modifies the extracellular matrix to accelerate colorectal cancer progression. Nat Commun. 2024;15(1):10422. https://doi.org/10.1038/s41467-024-54920-9. PMID: 39613801; PMCID: PMC11607440.

Article
CAS
PubMed
PubMed Central

Google Scholar
Bories D, Raynal MC, Solomon DH, Darzynkiewicz Z, Cayre YE. Down-regulation of a serine protease, myeloblastin, causes growth arrest and differentiation of promyelocytic leukemia cells. Cell. 1989;59(6):959–68. https://doi.org/10.1016/0092-8674(89)90752-6. PMID: 2598267.

Article
CAS
PubMed

Google Scholar
Campanelli D, Detmers PA, Nathan CF, Gabay JE. Azurocidin and a homologous Serine protease from neutrophils. Differential antimicrobial and proteolytic properties. J Clin Invest. 1990;85(3):904–15. PMID: 2312733; PMCID: PMC296509.

Article
CAS
PubMed
PubMed Central

Google Scholar
Coeshott C, Ohnemus C, Pilyavskaya A, Ross S, Wieczorek M, Kroona H, Leimer AH, Cheronis J. Converting enzyme-independent release of tumor necrosis factor alpha and IL-1beta from a stimulated human monocytic cell line in the presence of activated neutrophils or purified proteinase 3. Proc Natl Acad Sci U S A. 1999;96(11):6261–6. https://doi.org/10.1073/pnas.96.11.6261. PMID: 10339575; PMCID: PMC26869.

Article
CAS
PubMed
PubMed Central

Google Scholar
Mehlen P, Puisieux A. Metastasis: a question of life or death. Nat Rev Cancer. 2006;6(6):449–58. https://doi.org/10.1038/nrc1886. PMID: 16723991.

Article
CAS
PubMed

Google Scholar
Schneider C, Fehr MK, Steiner RA, Hagen D, Haller U, Fink D. Frequency and distribution pattern of distant metastases in breast cancer patients at the time of primary presentation. Arch Gynecol Obstet. 2003;269(1):9–12. https://doi.org/10.1007/s00404-002-0445-x. Epub 2002 Nov 15. PMID: 14605816.

Article
PubMed

Google Scholar
Baggiolini M, Bretz U, Dewald B, Feigenson ME. The polymorphonuclear leukocyte. Agents Actions. 1978;8(1–2):3–10. https://doi.org/10.1007/BF01972395. PMID: 345782.

Article
CAS
PubMed

Google Scholar
Pham CT. Neutrophil serine proteases: specific regulators of inflammation. Nat Rev Immunol. 2006;6(7):541–50. https://doi.org/10.1038/nri1841. PMID: 16799473.
Cox TR. The matrix in cancer. Nat Rev Cancer. 2021;21(4):217–38. https://doi.org/10.1038/s41568-020-00329-7. Epub 2021 Feb 15. PMID: 33589810.

Article
CAS
PubMed

Google Scholar
Guan X, Lu Y, Zhu H, Yu S, Zhao W, Chi X, Xie C, Yin Z. The crosstalk between cancer cells and neutrophils enhances hepatocellular carcinoma metastasis via neutrophil extracellular Traps-Associated cathepsin G component: A potential therapeutic target. J Hepatocell Carcinoma. 2021;8:451–65. PMID: 34046369; PMCID: PMC8144903.

Article
PubMed
PubMed Central

Google Scholar
Fu Z, Akula S, Thorpe M, Hellman L. Potent and broad but not unselective cleavage of cytokines and chemokines by human neutrophil elastase and proteinase 3. Int J Mol Sci. 2020;21(2):651. https://doi.org/10.3390/ijms21020651. PMID: 31963828; PMCID: PMC7014372.

Article
CAS
PubMed
PubMed Central

Google Scholar
Burster T, Knippschild U, Molnár F, Zhanapiya A. Cathepsin G and its Dichotomous Role in Modulating Levels of MHC Class I Molecules. Arch Immunol Ther Exp (Warsz). 2020;68(4):25. https://doi.org/10.1007/s00005-020-00585-3. PMID: 32815043.
Kudo T, Kigoshi H, Hagiwara T, Takino T, Yamazaki M, Yui S, Cathepsin G. A neutrophil protease, induces compact cell-cell adhesion in MCF-7 human breast cancer cells. Mediators Inflamm. 2009;2009:850940. https://doi.org/10.1155/2009/850940. Epub 2009 Nov 10. PMID: 19920860; PMCID: PMC2775934.

Article
CAS
PubMed
PubMed Central

Google Scholar
Song Y, Jin D, Chen J, Luo Z, Chen G, Yang Y, Liu X. Identification of an immune-related long non-coding RNA signature and nomogram as prognostic target for muscle-invasive bladder cancer. Aging. 2020;12(12):12051–73. https://doi.org/10.18632/aging.103369. Epub 2020 Jun 24. PMID: 32579540; PMCID: PMC7343518.

Article
CAS
PubMed
PubMed Central

Google Scholar
Chan S, Wang X, Wang Z, Du Y, Zuo X, Chen J, Sun R, Zhang Q, Lin L, Yang Y, Yu Z, Zhao H, Zhang H, Chen W. CTSG suppresses colorectal cancer progression through negative regulation of Akt/mTOR/Bcl2 signaling pathway. Int J Biol Sci. 2023;19(7):2220–33. https://doi.org/10.7150/ijbs.82000. PMID: 37151875; PMCID: PMC10158020.

Article
CAS
PubMed
PubMed Central

Google Scholar
Shi L, Yao H, Liu Z, Xu M, Tsung A, Wang Y. Endogenous PAD4 in breast cancer cells mediates cancer extracellular chromatin network formation and promotes lung metastasis. Mol Cancer Res. 2020;18(5):735–47. https://doi.org/10.1158/1541-7786.MCR-19-0018. Epub 2020 Mar 19. PMID: 32193354; PMCID: PMC7668292.

Article
CAS
PubMed
PubMed Central

Google Scholar
Chang X, Han J. Expression of peptidylarginine deiminase type 4 (PAD4) in various tumors. Mol Carcinog. 2006;45(3):183 – 96. https://doi.org/10.1002/mc.20169. PMID: 16355400.
Chang X, Han J, Pang L, Zhao Y, Yang Y, Shen Z. Increased PADI4 expression in blood and tissues of patients with malignant tumors. BMC Cancer. 2009;9:40. https://doi.org/10.1186/1471-2407-9-40. PMID: 19183436; PMCID: PMC2637889.
Yuzhalin AE, Gordon-Weeks AN, Tognoli ML, Jones K, Markelc B, Konietzny R, Fischer R, Muth A, O’Neill E, Thompson PR, Venables PJ, Kessler BM, Lim SY, Muschel RJ. Colorectal cancer liver metastatic growth depends on PAD4-driven citrullination of the extracellular matrix. Nat Commun. 2018;9(1):4783. https://doi.org/10.1038/s41467-018-07306-7. PMID: 30429478; PMCID: PMC6235861.

Article
CAS
PubMed
PubMed Central

Google Scholar
Wang Y, Lyu Y, Tu K, Xu Q, Yang Y, Salman S, Le N, Lu H, Chen C, Zhu Y, Wang R, Liu Q, Semenza GL. Histone citrullination by PADI4 is required for HIF-dependent transcriptional responses to hypoxia and tumor vascularization. Sci Adv. 2021;7(35):eabe3771. https://doi.org/10.1126/sciadv.abe3771. PMID: 34452909; PMCID: PMC8397272.

Article
CAS
PubMed
PubMed Central

Google Scholar
Liu K, Zhang Y, Du G, Chen X, Xiao L, Jiang L, Jing N, Xu P, Zhao C, Liu Y, Zhao H, Sun Y, Wang J, Cheng C, Wang D, Pan J, Xue W, Zhang P, Zhang ZG, Gao WQ, Jiang SH, Zhang K, Zhu HH. 5-HT orchestrates histone serotonylation and citrullination to drive neutrophil extracellular traps and liver metastasis. J Clin Invest. 2025;135(8):e183544. https://doi.org/10.1172/JCI183544. PMID: 39903533; PMCID: PMC11996869.

Article
CAS
PubMed
PubMed Central

Google Scholar
Zhu D, Lu Y, Yan Z, Deng Q, Hu B, Wang Y, Wang W, Wang Y, Wang Y. A β-Carboline derivate PAD4 inhibitor reshapes neutrophil phenotype and improves the tumor immune microenvironment against Triple-Negative breast cancer. J Med Chem. 2024;67(10):7973–94. https://doi.org/10.1021/acs.jmedchem.4c00030. Epub 2024 May 10. PMID: 38728549.

Article
CAS
PubMed

Google Scholar
Tan H, Jiang Y, Shen L, Nuerhashi G, Wen C, Gu L, Wang Y, Qi H, Cao F, Huang T, Liu Y, Xie W, Deng W, Fan W. Cryoablation-induced neutrophil Ca2 + elevation and NET formation exacerbate immune escape in colorectal cancer liver metastasis. J Exp Clin Cancer Res. 2024;43(1):319. https://doi.org/10.1186/s13046-024-03244-z. PMID: 39648199; PMCID: PMC11626751.

Article
CAS
PubMed
PubMed Central

Google Scholar
Luo Y, Arita K, Bhatia M, Knuckley B, Lee YH, Stallcup MR, Sato M, Thompson PR. Inhibitors and inactivators of protein arginine deiminase 4: functional and structural characterization. Biochemistry. 2006;45(39):11727–36. https://doi.org/10.1021/bi061180d. PMID: 17002273; PMCID: PMC1808342.

Article
CAS
PubMed

Google Scholar
Cedervall J, Dragomir A, Saupe F, Zhang Y, Ärnlöv J, Larsson E, Dimberg A, Larsson A, Olsson AK. Pharmacological targeting of peptidylarginine deiminase 4 prevents cancer-associated kidney injury in mice. Oncoimmunology. 2017;6(8):e1320009. PMID: 28919990; PMCID: PMC5593702.

Article
PubMed
PubMed Central

Google Scholar
Zhou Y, An LL, Chaerkady R, Mittereder N, Clarke L, Cohen TS, Chen B, Hess S, Sims GP, Mustelin T. Evidence for a direct link between PAD4-mediated citrullination and the oxidative burst in human neutrophils. Sci Rep. 2018;8(1):15228. https://doi.org/10.1038/s41598-018-33385-z. PMID: 30323221; PMCID: PMC6189209.

Article
CAS
PubMed
PubMed Central

Google Scholar
Mollica Poeta V, Massara M, Capucetti A, Bonecchi R. Chemokines and chemokine receptors: new targets for cancer immunotherapy. Front Immunol. 2019;10:379. https://doi.org/10.3389/fimmu.2019.00379. PMID: 30894861; PMCID: PMC6414456.

Article
CAS
PubMed
PubMed Central

Google Scholar
Nie M, Yang L, Bi X, Wang Y, Sun P, Yang H, Liu P, Li Z, Xia Y, Jiang W. Neutrophil extracellular traps induced by IL8 promote diffuse large B-cell lymphoma progression via the TLR9 signaling. Clin Cancer Res. 2019;25(6):1867–79. https://doi.org/10.1158/1078-0432.CCR-18-1226. Epub 2018 Nov 16. PMID: 30446590.

Article
CAS
PubMed

Google Scholar
Bullock K, Richmond A. Suppressing MDSC recruitment to the tumor microenvironment by antagonizing CXCR2 to enhance the efficacy of immunotherapy. Cancers (Basel). 2021;13(24):6293. https://doi.org/10.3390/cancers13246293. PMID: 34944914; PMCID: PMC8699249.

Article
CAS
PubMed

Google Scholar
Sano M, Ijichi H, Takahashi R, Miyabayashi K, Fujiwara H, Yamada T, Kato H, Nakatsuka T, Tanaka Y, Tateishi K, Morishita Y, Moses HL, Isayama H, Koike K. Blocking CXCLs-CXCR2 axis in tumor-stromal interactions contributes to survival in a mouse model of pancreatic ductal adenocarcinoma through reduced cell invasion/migration and a shift of immune-inflammatory microenvironment. Oncogenesis. 2019;8(2):8. https://doi.org/10.1038/s41389-018-0117-8. PMID: 30659170; PMCID: PMC6338726.

Article
CAS
PubMed
PubMed Central

Google Scholar
Gulhati P, Schalck A, Jiang S, Shang X, Wu CJ, Hou P, Ruiz SH, Soto LS, Parra E, Ying H, Han J, Dey P, Li J, Deng P, Sei E, Maeda DY, Zebala JA, Spring DJ, Kim M, Wang H, Maitra A, Moore D, Clise-Dwyer K, Wang YA, Navin NE, DePinho RA. Targeting T cell checkpoints 41BB and LAG3 and myeloid cell CXCR1/CXCR2 results in antitumor immunity and durable response in pancreatic cancer. Nat Cancer. 2023;4(1):62–80. https://doi.org/10.1038/s43018-022-00500-z. Epub 2022 Dec 30. PMID: 36585453; PMCID: PMC9925045.

Article
CAS
PubMed

Google Scholar
Nywening TM, Belt BA, Cullinan DR, Panni RZ, Han BJ, Sanford DE, Jacobs RC, Ye J, Patel AA, Gillanders WE, Fields RC, DeNardo DG, Hawkins WG, Goedegebuure P, Linehan DC. Targeting both tumour-associated CXCR2 + neutrophils and CCR2 + macrophages disrupts myeloid recruitment and improves chemotherapeutic responses in pancreatic ductal adenocarcinoma. Gut. 2018;67(6):1112–23. https://doi.org/10.1136/gutjnl-2017-313738. Epub 2017 Dec 1. PMID: 29196437; PMCID: PMC5969359.

Article
CAS
PubMed

Google Scholar
Cheng Y, Mo F, Li Q, Han X, Shi H, Chen S, Wei Y, Wei X. Targeting CXCR2 inhibits the progression of lung cancer and promotes therapeutic effect of cisplatin. Mol Cancer. 2021;20(1):62. https://doi.org/10.1186/s12943-021-01355-1. PMID: 33814009; PMCID: PMC8019513.

Article
CAS
PubMed
PubMed Central

Google Scholar
Pant A, Hwa-Lin Bergsneider B, Srivastava S, Kim T, Jain A, Bom S, Shah P, Kannapadi N, Patel K, Choi J, Cho KB, Verma R, Yu-Ju Wu C, Brem H, Tyler B, Pardoll DM, Jackson C, Lim M. CCR2 and CCR5 co-inhibition modulates immunosuppressive myeloid milieu in glioma and synergizes with anti-PD-1 therapy. Oncoimmunology. 2024;13(1):2338965. PMID: 38590799; PMCID: PMC11000615.

Article
PubMed
PubMed Central

Google Scholar
Chao CC, Lee CW, Chang TM, Chen PC, Liu JF. CXCL1/CXCR2 paracrine axis contributes to lung metastasis in osteosarcoma. Cancers (Basel). 2020;12(2):459. https://doi.org/10.3390/cancers12020459. PMID: 32079335; PMCID: PMC7072404.

Article
CAS
PubMed

Google Scholar
Li YM, Liu ZY, Wang JC, Yu JM, Li ZC, Yang HJ, Tang J, Chen ZN. Receptor-Interacting protein kinase 3 deficiency recruits Myeloid-Derived suppressor cells to hepatocellular carcinoma through the chemokine (C-X-C Motif) ligand 1-Chemokine (C-X-C Motif) receptor 2 axis. Hepatology. 2019;70(5):1564–81. Epub 2019 Jul 17. PMID: 31021443; PMCID: PMC6900048.

Article
CAS
PubMed

Google Scholar
Dahmani A, Delisle JS. TGF-β in T cell biology: implications for cancer immunotherapy. Cancers (Basel). 2018;10(6):194. https://doi.org/10.3390/cancers10060194. PMID: 29891791; PMCID: PMC6025055.

Article
CAS
PubMed

Google Scholar
Suzuki E, Kim S, Cheung HK, Corbley MJ, Zhang X, Sun L, Shan F, Singh J, Lee WC, Albelda SM, Ling LE. A novel small-molecule inhibitor of transforming growth factor beta type I receptor kinase (SM16) inhibits murine mesothelioma tumor growth in vivo and prevents tumor recurrence after surgical resection. Cancer Res. 2007;67(5):2351–9. https://doi.org/10.1158/0008-5472.CAN-06-2389. PMID: 17332368.

Article
CAS
PubMed

Google Scholar
Fridlender ZG, Sun J, Kim S, Kapoor V, Cheng G, Ling L, Worthen GS, Albelda SM. Polarization of tumor-associated neutrophil phenotype by TGF-beta: N1 versus N2 TAN. Cancer Cell. 2009;16(3):183–94. PMID: 19732719; PMCID: PMC2754404.

Article
CAS
PubMed
PubMed Central

Google Scholar
Chung JY, Chan MK, Li JS, Chan AS, Tang PC, Leung KT, To KF, Lan HY, Tang PM. TGF-β signaling: from tissue fibrosis to tumor microenvironment. Int J Mol Sci. 2021;22(14):7575. https://doi.org/10.3390/ijms22147575. PMID: 34299192; PMCID: PMC8303588.

Article
CAS
PubMed
PubMed Central

Google Scholar
Chan MK, Chung JY, Tang PC, Chan AS, Ho JY, Lin TP, Chen J, Leung KT, To KF, Lan HY, Tang PM. TGF-β signaling networks in the tumor microenvironment. Cancer Lett. 2022;550:215925. https://doi.org/10.1016/j.canlet.2022.215925. Epub 2022 Sep 29. PMID: 36183857.

Article
CAS
PubMed

Google Scholar
Chung JY, Tang PC, Chan MK, Xue VW, Huang XR, Ng CS, Zhang D, Leung KT, Wong CK, Lee TL, Lam EW, Nikolic-Paterson DJ, To KF, Lan HY, Tang PM. Smad3 is essential for polarization of tumor-associated neutrophils in non-small cell lung carcinoma. Nat Commun. 2023;14(1):1794. https://doi.org/10.1038/s41467-023-37515-8. PMID: 37002229; PMCID: PMC10066366.

Article
CAS
PubMed
PubMed Central

Google Scholar
Tang PM, Zhou S, Meng XM, Wang QM, Li CJ, Lian GY, Huang XR, Tang YJ, Guan XY, Yan BP, To KF, Lan HY. Smad3 promotes cancer progression by inhibiting E4BP4-mediated NK cell development. Nat Commun. 2017;8:14677. https://doi.org/10.1038/ncomms14677. PMID: 28262747; PMCID: PMC5343519.

Article
PubMed
PubMed Central

Google Scholar
Peng H, Shen J, Long X, Zhou X, Zhang J, Xu X, Huang T, Xu H, Sun S, Li C, Lei P, Wu H, Zhao J. Local release of TGF-β inhibitor modulates Tumor-Associated neutrophils and enhances pancreatic cancer response to combined irreversible electroporation and immunotherapy. Adv Sci (Weinh). 2022;9(10):e2105240. https://doi.org/10.1002/advs.202105240. Epub 2022 Feb 7. PMID: 35128843; PMCID: PMC8981446.

Article
CAS
PubMed

Google Scholar
Pei Y, Chen L, Huang Y, Wang J, Feng J, Xu M, Chen Y, Song Q, Jiang G, Gu X, Zhang Q, Gao X, Chen J. Sequential targeting TGF-β signaling and KRAS mutation increases therapeutic efficacy in pancreatic cancer. Small. 2019;15(24):e1900631. https://doi.org/10.1002/smll.201900631. Epub 2019 Apr 29. PMID: 31033217.

Article
CAS
PubMed

Google Scholar
Chen Z, Zhang H, Qu M, Nan K, Cao H, Cata JP, Chen W, Miao C. Review: the emerging role of neutrophil extracellular traps in sepsis and sepsis-Associated thrombosis. Front Cell Infect Microbiol. 2021;11:653228. PMID: 33816356; PMCID: PMC8010653.

Article
CAS
PubMed
PubMed Central

Google Scholar
Hisada Y, Grover SP, Maqsood A, Houston R, Ay C, Noubouossie DF, Cooley BC, Wallén H, Key NS, Thålin C, Farkas ÁZ, Farkas VJ, Tenekedjiev K, Kolev K, Mackman N. Neutrophils and neutrophil extracellular traps enhance venous thrombosis in mice bearing human pancreatic tumors. Haematologica. 2020;105(1):218–25. Epub 2019 May 2. PMID: 31048354; PMCID: PMC6939515.

Article
CAS
PubMed
PubMed Central

Google Scholar
Kim SW, Lee JK. Role of HMGB1 in the interplay between NETosis and thrombosis in ischemic stroke: A review. Cells. 2020;9(8):1794. https://doi.org/10.3390/cells9081794. PMID: 32731558; PMCID: PMC7464684.

Article
CAS
PubMed
PubMed Central

Google Scholar
Spicer JD, McDonald B, Cools-Lartigue JJ, Chow SC, Giannias B, Kubes P, Ferri LE. Neutrophils promote liver metastasis via Mac-1-mediated interactions with Circulating tumor cells. Cancer Res. 2012;72(16):3919–27. https://doi.org/10.1158/0008-5472.CAN-11-2393. Epub 2012 Jul 2. PMID: 22751466.

Article
CAS
PubMed

Google Scholar
Szczerba BM, Castro-Giner F, Vetter M, Krol I, Gkountela S, Landin J, Scheidmann MC, Donato C, Scherrer R, Singer J, Beisel C, Kurzeder C, Heinzelmann-Schwarz V, Rochlitz C, Weber WP, Beerenwinkel N, Aceto N. Neutrophils escort Circulating tumour cells to enable cell cycle progression. Nature. 2019;566(7745):553–7. https://doi.org/10.1038/s41586-019-0915-y. Epub 2019 Feb 6. PMID: 30728496.

Article
CAS
PubMed

Google Scholar
Bianchi M, Sun M, Jeldres C, Shariat SF, Trinh QD, Briganti A, Tian Z, Schmitges J, Graefen M, Perrotte P, Menon M, Montorsi F, Karakiewicz PI. Distribution of metastatic sites in renal cell carcinoma: a population-based analysis. Ann Oncol. 2012;23(4):973–80. https://doi.org/10.1093/annonc/mdr362. Epub 2011 Sep 2. PMID: 21890909.

Article
CAS
PubMed

Google Scholar

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Introduction

Coronary heart disease is the leading cause of death worldwide, accounting for approximately 30% of all deaths.¹ Percutaneous coronary intervention (PCI) can directly implant stents in the most severely narrowed coronary arteries to improve the degree of coronary artery stenosis. It has the characteristics of rapidly restoring myocardial blood and oxygen supply and thereby improving the clinical symptoms of patients, and has become the most effective means of treating coronary heart disease.^2,3 However, in-stent restenosis (ISR) reduces the overall effectiveness of PCI. Although the incidence of ISR in bare-metal stents is 30% at 6 months, and that in drug-eluting stents (DES) drops to 7% at 4 years,⁴ ISR remains the main cause of long-term failure after PCI.⁵ A 10-year data from a DES randomized trial showed that ISR led to a target lesion revascularization rate of approximately 20% at 10 years.⁶ A study by Seiler et al demonstrated that at 1-year follow-up, the MACE rate after ISR treatment was 19.7%; 15.4% of patients experienced TVF (target vessel failure), with myocardial infarction (MI) and stent thrombosis occurring in 5.9% and 2.1% of patients, respectively.⁷ Another study has demonstrated that the rate of target vessel revascularization due to in-stent restenosis three years after drug-eluting stent implantation reached 5.2%.⁸ Therefore, early identification and control of related risk factors are key approaches to reducing the incidence of ISR and improving patient prognosis.

Fractional Flow Reserve (FFR) is an invasive diagnostic technique widely regarded as the “gold standard” for assessing the hemodynamic significance of coronary artery lesions. It effectively quantifies the extent of myocardial ischemia, offers clinical guidance on the necessity of stent implantation during PCI, and aids in predicting patient outcomes following PCI.⁹ Previous studies have shown that FFR has certain value in the diagnosis and prognosis assessment of ISR.¹⁰ However, its invasive nature, high cost, large radiation dose and adverse reactions have limited its clinical application. Therefore, a non-invasive CT-based fractional flow reserve (CT-FFR) has emerged. CT-FFR is obtained through computational fluid dynamics (CFD) simulation,¹¹ and in recent years, CT-FFR software based on machine learning (ML) algorithms has gradually matured.¹² Existing studies have demonstrated that CT-FFR has a good correlation with invasive FFR in identifying ISR (OR = 0.84).¹³

Inflammation plays a significant role in the occurrence and development of ISR. Pericoronary adipose tissue (PCAT) refers to the adipose tissue that surrounds the coronary arteries and exhibits a significant bidirectional interaction with the vascular wall.¹⁴ Fat attenuation index (FAI), a novel and non-invasive imaging biomarker based on coronary computed tomography angiography (CCTA), visualizes and quantifies the inflammation around coronary arteries by mapping the attenuation gradient of PCAT and tracking the changes in the size of local adipocytes and lipid content around coronary arteries.¹⁵ Previous studies have shown that FAI is associated with high-risk and vulnerable plaques and has high diagnostic value for coronary artery stenosis and myocardial ischemia, and is significantly superior to CCTA alone.^16–18 Further studies have confirmed that FAI is significantly associated with adverse cardiac events and ISR, and has high predictive value.^19,20 However, the predictive value of CT-FFR combined with FAI based on deep learning for ISR after PCI and its prognostic assessment have not been systematically verified.

To explore how the fractional flow reserve and fat attenuation index around the stent affect ISR, this study proposes the following verifiable hypothesis: The CT-FFR and FAI values derived from the CCTA, are correlated with the occurrence of ISR. Therefore, this study aims to investigate the correlation between CT-FFR and FAI values derived from the CCTA deep learning method and ISR following percutaneous coronary intervention, as well as to evaluate their predictive value for ISR by integrating the functional and inflammatory parameters around the stent.

Materials and Methods

Patient Population

This was a single-center retrospective study. A retrospective collection was made of patients with coronary heart disease who underwent coronary stent implantation at Linyi Central Hospital from January 2019 to December 2024 and were readmitted for treatment due to recurrent angina pectoris and other symptoms. All patients underwent coronary CT angiography (CCTA) and invasive coronary angiography (CAG) simultaneously. Inclusion criteria were: (1) Patients who underwent both CCTA and CAG after coronary stent implantation, with an interval of ≥ 90 days, The interval between CCTA and CAG is less than two weeks; (2) Good image quality, suitable for image analysis and measurement of FAI and CT-FFR values. Exclusion criteria were: (1) Patients with incomplete clinical data; (2) Patients who did not undergo CAG or the time interval between CAG and CCTA < 90 days; (3) Patients with stents located only in the left main trunk or branches, except for the three main arteries, or stents located within 10 mm of the proximal right coronary artery; (4) Patients with congenital coronary artery anomalies or congenital heart disease; (5) Patients who underwent coronary artery bypass grafting (CABG) surgery; (6) Patients with poor image quality due to various reasons, making assessment impossible. The clinical data of patients were collected from the hospital’s electronic medical record system, outpatient records, and telephone follow-up interviews. Record the patient’s age, gender, height, weight, BMI, hypertension, hyperlipidemia, diabetes, smoking, drinking and laboratory test data during the CCTA examination. Laboratory tests included lipid analysis, myocardial enzyme spectrum analysis, C-reactive protein and erythrocyte sedimentation rate analysis. If a patient had multiple laboratory tests, the results closest to CCTA were selected. Record whether the patient has achieved complete revascularization, the pharmacological therapy following revascularization, and the clinical presentation of patients, LV functions and comorbidities. Record the characteristics of the stent, including the vessel and segment where the stent is located, the diameter of the stent, the total length of the stent, the number of stents, and the overlapping situation.

CT Acquisition and Reconstruction

CCTA was performed with a third-generation dual-source CT device (Siemens SOMATOM Force). All patients signed informed consent forms. Scanning range: The upper boundary starts 2cm below the tracheal bifurcation, and the lower boundary ends above the diaphragm. The contrast dose tracking and triggering technique was used to select the ROI at the ascending aorta root, set the triggering threshold to 100 Hu, and delay 5 seconds for automatic scanning. Scanning parameters: Tube current modulation technology was adopted, and the tube voltage ranged from 70–120 kV. The interval is 0.5 mm, and the layer thickness is 0.75 mm. During the examination, the patient’s electrocardiogram, heart rate, blood pressure, clinical symptoms, and other relevant parameters are continuously monitored. The procedure will be terminated if any of the following abnormalities occur: arrhythmia, a sustained decrease in heart rate, a blood pressure drop exceeding 40 mmHg compared to baseline, acute chest pain, or new-onset ST-segment elevation or depression.

CCTA Image Analysis

All CCTA images were uploaded to the post-processing workstation (Syngo.Via, Siemens), and the best diastolic images were selected for image processing. Two radiologists with more than five years of experience in coronary CTA interpretation analyzed the CCTA measurements. The CCTA measurement parameters encompass the normal lumen diameter at the proximal stent segment, the minimum lumen diameter (MLD) within the stent, and the minimum lumen area (MLA) of the stent, all derived through standardized image post-processing techniques such as multiplanar reconstruction (MPR), curved planar reconstruction (CPR), maximum intensity projection (MIP), and volume rendering technique (VRT).

CT-FFR measurement: All patients’ CCTA data were uploaded to the artificial intelligence analysis software (Coronary Doc. Shukun Technology, China) in DICOM format. The software leverages artificial intelligence technology based on neural network models to learn the relationship between computational fluid dynamics (CFD) and anatomical structures, enabling the calculation of CT-FFR values for blood vessels with diameters exceeding 2 millimeters.^21,22 CT-FFR measurement includes the CT-FFR value at the proximal edge of the stent (CT-FFR_pro), the CT-FFR value at the minimum area of the stent (CT-FFR_min), the CT-FFR value at the distal edge of the stent (CT-FFR_dis), the CT-FFR value 2 cm from the distal edge of the stent (CT-FFR_2cm). Additionally, the ΔCT-FFR value was calculated as the difference between CT-FFR_pro and CT-FFR_dis. The study also recorded the rate of change of CT-FFR values relative to stent length, expressed as ΔCT-FFR per unit length (ΔCT-FFR/length).

FAI measurement: The fat area value range is from −190HU to −30HU. The radial distance of the outer wall of the coronary artery is manually modified to the length of the target vessel diameter. Based on the vessel level, the software can automatically calculate the FAI value within 40 mm of the proximal end of the three main coronary arteries of the patient. To minimize the influence of the aortic wall, the stent area placed within the 10 mm segment at the distal end of the right coronary artery was excluded in this study.¹⁵ Based on the vessel level, the FAI value of the target vessel where the stent is located is recorded. Based on the lesion level, the length range of the stent from the proximal to the distal end (including 5 mm within the proximal and distal edges) is manually defined, and the radial distance is manually adjusted to make the diameter equal to the diameter of the stent, for the measurement of lesion-specific FAI (Lesion-specific FAI, FAI_lesion) around the stent.²³ Two senior radiologists, each with more than five years of experience in cardiovascular imaging diagnosis, independently performed CT-FFR and FAI analyses in a double-blind fashion, without access to clinical data or ICA results. In the event of discrepancies, a senior physician was consulted to resolve differences through consensus evaluation.

ICA Acquisition

The examination was conducted using the Philips Azurion 7 M20 angiography machine. Conventional angiography of the left and right coronary arteries was performed in various positions. Two senior cardiologists made the diagnosis of in-stent restenosis (ISR) without knowledge of the CCTA results, and the results were recorded and stored in the hospital’s electronic medical record system. ISR was defined as a lumen diameter stenosis of ≥ 50% in the stent segment or its proximal or distal edge (a segment adjacent to the stent with a length of 5 mm).²⁴ According to ICA measurements, all patients who underwent coronary stenting were divided into two groups: vessels with ISR (ISR group) and vessels without ISR (non-ISR group).

Statistical Analysis

SPSS 26.0 and R (4.2.1) software were used for statistical analysis. Continuous data conforming to a normal distribution are presented as the means ± standard deviations and were compared between groups with the independent sample t test. Continuous data that were not normally distributed are expressed as medians (interquartile intervals) and were compared between groups with the Wilcoxon rank-sum test. Categorical variables are expressed as frequencies and percentages and were compared with the chi-square test. Spearman correlation analysis was employed to explore the relationships between various risk factors and ISR. Following the collinearity analysis of the research parameters, both univariate and multivariate logistic regression analyses were performed to identify independent risk factors for ISR. Subsequently, nomogram models and various clinical models were developed. The predictive performance of these models was assessed using the area under the curve (AUC) of the receiver operating characteristic curve and the concordance index (C-index). Additionally, calibration curves were employed to evaluate the agreement between predicted probabilities and observed outcomes, while decision curve analysis was conducted to determine the clinical utility of the models. A P value <0.05 was considered statistically significant.

Results

Patient Characteristics and Clinical Outcomes

A total of 378 patients were ultimately included in this study. Among them, 120 cases were included in the ISR group (31.7%), 88 were male (73.3%), and the age was 65.05 ± 8.58 (years). A total of 258 cases were included in the non-ISR group (68.3%), with 176 males (68.2%) and an age of 63.36 ± 8.71 (years). The flow chart is shown in Figure 1. There were statistically significant differences between the two patient groups with respect to hyperlipidemia, lipoprotein(a), hydroxybutyrate dehydrogenase, troponin, NT-proBNP levels and ACEI/ARB. The results of the blood vessels where the stents were located showed that there were 218 cases (57.7%) of LAD, 69 cases (18.2%) of LCX, and 91 cases (24.1%) of RCA. The results of the stent segments showed that there were 143 cases (37.8%) in the proximal segment, 90 cases (23.8%) in the proximal and middle segments, 82 cases (21.7%) in the middle segment, and 63 cases (16.7%) in the distal segment. There were statistically significant differences in stent length, number of stents, stent diameter, minimum stent lumen area, and minimum lumen diameter between the two groups (P < 0.05). There was no statistically significant difference in the diameter of normal blood vessels in the proximal segment of the stent and the overlapping stents. There were statistically significant differences in CT-FFR_pro, CT-FFR_min, CT-FFR_dis, CT-FFR_2cm, ΔCT-FFR, and ΔCT-FFR/length between the two groups (P < 0.05). The FAI values of the target vessels and the FAI values (FAI_lesion) around the stents showed statistically significant differences between the two groups (P < 0.05). The patient and stent characteristics as well as relevant results of CCTA, CT-FFR and FAI measurements are displayed in Table 1.

Table 1 Comparison of General Data Between the Two Groups

Figure 1 Flow chart of inclusion and exclusion criteria for the study population.

Correlation Analysis of CT-FFR, FAI and ISR

According to the Spearman correlation analysis, CT-FFR_2cm showed a moderate negative correlation with ISR (R = −0.60, P < 0.001). ΔCT-FFR demonstrated a moderate positive correlation with ISR (R = 0.684, P < 0.001). FAI_lesion also exhibited a moderate positive association with ISR (R = 0.576, P < 0.001). Furthermore, ΔCT-FFR/length was moderately positively correlated with ISR (R = 0.635, P < 0.001) (Figure 2).

Figure 2 The correlation between various factors and in-stent restenosis after PCI.

Univariate and Multivariate Analyses for ISR

Univariate logistic regression analysis demonstrated that CT-FFR_pro, CT-FFR_min, CT-FFR_dis, CT-FFR_2cm, ΔCT-FFR, FAI, FAI_lesion, stent length, ΔCT-FFR/length, stents number, MLD and MLA, brain natriuretic peptide precursor, hyperlipidemia, ACEI/ARB and lipoprotein(a) were all associated with ISR. Following collinearity assessment, CT-FFR_pro and CT-FFR_dis were excluded, due to multicollinearity. The remaining variables were entered into a multivariate logistic regression model. The results showed that CT-FFR_2cm, ΔCT-FFR, FAI_lesion, ΔCT-FFR/length, hyperlipidemia and lipoprotein a were independent predictors of ISR (Table 2).

Table 2 Univariate and Multivariate Logistic Regression Analysis for ISR

Nomogram Construction and Prediction Performance of ISR Test Parameters

A nomogram model was developed based on the aforementioned independent influencing factors to predict the occurrence of in-stent restenosis (ISR) in patients who underwent percutaneous coronary intervention (PCI) (Figure 3). The model demonstrated high predictive accuracy, as evidenced by a C-index of 0.966. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the performance of the predictive model. The results indicated that ΔCT-FFR exhibited the highest predictive value for ISR, with an AUC of 0.923 (95% CI: 0.889–0.957), outperforming both FAI_lesion (AUC=0.857, 95% CI: 0.817–0.896) and clinical data (AUC=0.688, 95% CI: 0.628–0.748). This difference was statistically significant (P < 0.05) (Figure 4). Several clinical models were constructed, and calibration and decision curve analyses were conducted to further validate the ISR prediction model. Model 1 only includes ΔCT-FFR; Model 2 includes ΔCT-FFR and FAI_lesion; Model 3 includes ΔCT-FFR, FAI_lesion and clinical data. The ROC results showed that the area under the curve (AUC), cut-off value, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of Model 1 for predicting ISR were 0.923 (95% CI: 0.889–0.957), 0.08, 0.965, 0.783, 0.907, 0.905, and 0.913, respectively. For Model 2, the corresponding values were 0.955 (95% CI: 0.930–0.981), 0.439, 0.957, 0.858, 0.926, 0.936, and 0.904, respectively. For Model 3, these values were 0.958 (95% CI: 0.932–0.984), 0.337, 0.942, 0.892, 0.926, 0.949, and 0.877, respectively. Model 3 had the highest predictive value (AUC: 0.958, 95% CI, 0.932–0.984). There was a statistically significant difference between Model 1 and Model 2 (AUC: 0.923 vs 0.955, P < 0.05). The predictive value of ΔCT-FFR in combination with FAI is significantly greater than that of either parameter alone. There was no statistically significant difference between Model 2 and Model 3 (AUC: 0.955 vs 0.958, P > 0.05) (Figure 5 and Table 3). The calibration curve results show that most of the data points in the ISR prediction model are close to the ideal line, indicating that the model has a high degree of calibration (Figure 6). The results of the decision curve analysis show that in ISR prediction, the net gain of the model in most threshold probability ranges is higher than that under the assumption that all patients are positive or negative, indicating that the model has high clinical practicability (Figure 7).

Table 3 The Results of the Work Characteristic Curves of ISR Predicted by Each Model

Figure 3 A nomogram model was developed to predict the occurrence of in-stent restenosis.

Figure 4 The receiver operating characteristic curves of each factor for predicting in-stent restenosis (ISR).

Figure 5 The receiver operating characteristic curves of each model for predicting in-stent restenosis.

Figure 6 Calibration curves of each model for the prediction of in-stent restenosis.

Figure 7 Decision curves of each model for the prediction of in-stem restenosis.

Discussion

To the best of our knowledge, this is the first study to investigate the correlation between CT-FFR combined with FAI and its potential in predicting in-stent restenosis (ISR) in patients with coronary heart disease following percutaneous coronary intervention (PCI). Our findings demonstrate that ΔCT-FFR, CT-FFR_2cm, and peri-stent FAI derived from CCTA are moderately associated with ISR and may act as independent predictors for ISR after stent implantation. Notably, while the integration of imaging features and clinical data enhances the identification of ISR, imaging features exhibit greater predictive value compared to clinical data alone.

Percutaneous coronary intervention initiates two distinct pathological processes that ultimately lead to ISR. In the early phase, ISR is primarily attributed to excessive neointimal hyperplasia, whereas in the later phase, it is predominantly caused by neoatherosclerosis.^25,26 Plaque rupture may further precipitate acute coronary syndrome and potentially result in major adverse cardiovascular events (MACE), including sudden cardiac death, thereby posing a significant threat to patient safety.²⁷ The fractional flow reserve (FFR) measured after PCI reflects the hemodynamic alterations in the target vessel where the stent is deployed. A lower FFR value indicates a reduced maximum blood flow capacity in the stented vessel under hyperemic conditions compared to a normal vessel, which may result in impaired blood flow, promote platelet aggregation and vascular occlusion, and consequently increase the risk and severity of ISR.²⁸ Conversely, a higher FFR level suggests favorable myocardial perfusion, adequate subendocardial oxygen supply, and diminished ischemia-induced inflammatory responses. These factors contribute to stable cellular metabolism, protection against ischemia-reperfusion injury, promotion of collateral circulation development, mitigation of adverse effects associated with vascular stenosis, and inhibition of ISR progression.^29,30 Previous studies have indicated that the FFR can serve as a valuable tool for assessing prognosis following PCI, with a significant post-PCI improvement in FFR being linked to greater symptom relief and a reduced incidence of adverse cardiovascular events.^9,31 Onuma et al³² were the first to demonstrate the feasibility of using computational fluid dynamics (CFD)-based CT-derived FFR in patients undergoing PCI with bioabsorbable stents. Andreini et al³³ reported a case of severe ISR that was missed by coronary CT angiography but accurately detected by CT-FFR. In this case, CCTA did not detect significant stenosis within the RCA stent of the patient. On the contrary, CT-FFR analysis showed obvious distal stenosis of the RCA stent segment. Invasive coronary angiography confirmed severe ISR in RCA. Wang et al evaluated the predictive value of CT-FFR prior to PCI for target vessel failure (TVF) following stent implantation and found that CT-FFR, as an independent predictor of TVF, significantly improved risk reclassification compared with a clinical risk factor model.³⁴ Tang et al¹³ were the first to investigate the predictive performance of machine learning (ML)-based CT-FFR for ISR. Their results indicated that CT-FFR achieved an accuracy rate of 85% in identifying ISR. During the follow-up period, statistically significant differences were observed between the ISR and non-ISR groups in terms of ΔCT-FFR and ΔCT-FFR/length. Moreover, ΔCT-FFR/length was identified as an independent predictor of ISR. In this study, statistically significant differences in CT-FFR_2cm, ΔCT-FFR, and ΔCT-FFR/length were found at each measured location between the two groups (P < 0.05), and CT-FFR_2cm, ΔCT-FFR, and ΔCT-FFR/length were all independently associated with ISR. These findings are largely consistent with those of previous studies. Furthermore, this study revealed that the predictive values of CT-FFR_2cm, ΔCT-FFR, and ΔCT-FFR/length for ISR were significantly higher than those of traditional risk factors such as hyperlipidemia and lipoprotein(a) [Lp(a)]. Therefore, we believe that for patients undergoing follow-up after PCI, it is essential to measure CT-FFR values at multiple locations within the stent, with particular emphasis on the difference in CT-FFR between the proximal and distal ends. As an independent predictor of ISR, ΔCT-FFR demonstrates high predictive accuracy for ISR assessment, significantly outperforming conventional clinical data.

Previous studies have confirmed that the development of ISR is closely associated with inflammatory processes.³⁵ Following stent implantation, vascular endothelial cells are damaged, and mechanical injury to the vascular wall triggers an inflammatory response. This inflammation promotes the formation of neointimal hyperplasia (NIH) and contributes to the development of neoatherosclerosis. Peri-coronary adipose tissue (PCAT) is capable of directly releasing substantial amounts of pro-inflammatory adipokines, cytokines, and chemokines, which contribute to endothelial dysfunction, inflammatory cell infiltration, and smooth muscle cell migration.^36,37 Additionally, mediators secreted from the inflamed vascular wall, such as interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and plasminogen activator inhibitor-1 (PAI-1), can exert paracrine effects on PCAT. These mediators inhibit the proliferation and differentiation of human preadipocytes within PCAT, thereby suppressing lipid accumulation, reducing adipocyte numbers, and lowering overall lipid content.^38,39 Nogic et al initially examined the association between the mean attenuation of lesion-specific pericoronary adipose tissue (PCAT_lesion) prior to stent implantation and the occurrence of stent failure following PCI. Their findings indicated that PCAT_lesion values were significantly elevated in patients who experienced stent failure compared to those who did not, and an increased PCAT_lesion was identified as an independent predictor of stent failure.²³ The fat attenuation index, which is derived from CCTA, quantitatively assesses the CT attenuation gradient of PCAT, thereby reflecting the inflammatory activity of the coronary arteries and enabling non-invasive detection of coronary vascular inflammation. Adolf et al investigated the predictive significance of lesion-specific FAI (FAI_lesion) prior to stent placement with respect to in-stent restenosis (ISR). They found a significant correlation between FAI_lesion and ISR, with elevated FAI_lesion levels serving as an independent predictor of stent restenosis.²⁰ Qin et al first investigated the predictive value of peristent FAI for ISR and demonstrated that peristent FAI serves as an independent predictor of ISR, potentially functioning as a non-invasive biomarker for assessing the risk and severity of ISR following stent implantation.⁴⁰ These findings align with our research results. In our study, the lesion-specific peristent FAI (FAI_lesion) was significantly higher in the ISR group compared to the non-ISR group. As an independent predictor of ISR, FAI_lesion exhibited a moderate correlation with the occurrence of ISR. Moreover, the predictive value of FAI_lesion for ISR was found to be significantly greater than that of hyperlipidemia and lipoprotein(a) (Lp(a)). This study demonstrates that the FAI surrounding the stent, as a novel imaging biomarker of inflammation, holds significant clinical potential for the non-invasive assessment of ISR and may serve as a predictive tool for evaluating ISR risk following stent implantation. However, findings regarding the application value of FAI in ISR exhibit inconsistency. Another study evaluating the diagnostic value of radiomic features of pericoronary adipose tissue for in-stent restenosis reported no significant difference in peristent FAI between the ISR and non-ISR groups.⁴¹ Therefore, the clinical utility of FAI in ISR requires comprehensive validation through large-scale clinical trials.

Limitations of this study include the following: (1) This was a single-center retrospective study, and selection bias may have occurred during patient enrollment. (2) The subjective editing of the stent vessel lumen profile could potentially influence subsequent CT-FFR and FAI calculations. Therefore, larger-scale and more comprehensive studies are required to further validate the clinical application of these parameters in the context of ISR. (3) Previous research has indicated that early and late in-stent restenosis involve distinct pathophysiological mechanisms.^42,43 However, this study did not differentiate between early and late ISR.

In conclusion, ΔCT-FFR and peri-stent FAI are independent predictors of in-stent restenosis following percutaneous coronary intervention, and demonstrate superior predictive performance for ISR compared to clinical characteristics. The combined application of these two parameters further enhances the predictive performance for ISR. The integration of CT-FFR and FAI techniques derived from CCTA enables a comprehensive and systematic evaluation of ISR through a “one-stop” assessment encompassing functional and inflammatory data. This non-invasive approach provides additional diagnostic value for ISR risk assessment: it not only helps reduce unnecessary invasive examinations in some patients and optimize the clinical management pathway for post-PCI ISR, but also lays a crucial foundation for individualized treatment decisions.

Data Sharing Statement

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics Statement

This study was approved by the ethic committee of Linyi Central Hospital (LCH-LW-2025115) and it was carried out following the guidelines of the Helsinki Declaration (World Medical Association Declaration of Helsinki). Written informed consent was obtained from the patients for their participation in this study.

Disclosure

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

1. Khamis RY, Ammari T, Mikhail GW. Gender differences in coronary heart disease. Heart. 2016;102(14):1142–1149. doi:10.1136/heartjnl-2014-306463

2. Sulava EF, Johnson JC. Management of Coronary Artery Disease. Surg Clin North Am. 2022;102(3):449–464. doi:10.1016/j.suc.2022.01.005

3. Lawton JS, Tamis-Holland JE, Bangalore S, et al. Writing Committee Members. 2021 ACC/AHA/SCAI guideline for coronary artery revascularization: a report of the American college of cardiology/american heart association joint committee on clinical practice guidelines. J Am Coll Cardiol. 2022;79(2):e21–e129. doi:10.1016/j.jacc.2021.09.006

4. Madhavan MV, Kirtane AJ, Redfors B, et al. Stent-related adverse events >1 year after percutaneous coronary intervention. J Am Coll Cardiol. 2020;75(6):590–604. doi:10.1016/j.jacc.2019.11.058

5. Kawai K, Virmani R, Finn AV. In-Stent Restenosis. Interv Cardiol Clin. 2022;11(4):429–443. doi:10.1016/j.iccl.2022.02.005

6. Kufner S, Joner M, Thannheimer A, et al. Ten-year clinical outcomes from a trial of three limus-eluting stents with different polymer coatings in patients with coronary artery disease. Circulation. 2019;139:325–333. doi:10.1161/CIRCULATIONAHA.118.038065

7. Seiler T, Attinger-Toller A, Cioffi GM, et al. Treatment of in-stent restenosis using a dedicated super high-pressure balloon. Cardiovasc Revasc Med. 2023;46:29–35. doi:10.1016/j.carrev.2022.08.018

8. Cuesta J, Pérez-Vizcayno MJ, García Del Blanco B, et al. Long-term results of bioresorbable vascular scaffolds in patients with in-stent restenosis: the RIBS VI study. JACC Cardiovasc Interv. 2024;17(15):1825–1836.

9. Frøbert O, Götberg M. Fractional flow reserve-dichotomous decisions in myocardial ischemia. Circ Cardiovasc Interv. 2022;15(2):e011787. doi:10.1161/CIRCINTERVENTIONS.122.011787

10. BKrüger S, Koch KC, Kaumanns I, et al. Use of fractional flow reserve versus stress perfusion scintigraphy in stent restenosis. Eur J Intern Med. 2005;16(6):429–431. doi:10.1016/j.ejim.2005.01.022

11. Nørgaard BL, Leipsic J, Gaur S, et al. NXT Trial Study Group. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of coronary blood flow using CT angiography: next steps). J Am Coll Cardiol. 2014;63(12):1145–1155. doi:10.1016/j.jacc.2013.11.043

12. Kawase Y, Matsuo H, Kuramitsu S, et al. Clinical use of physiological lesion assessment using pressure guidewires: an expert consensus document of the Japanese association of cardiovascular intervention and therapeutics-update 2022. Cardiovasc Interv Ther. 2022;37(3):425–439. doi:10.1007/s12928-022-00863-1

13. Tang CX, Guo BJ, Schoepf JU, et al. Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation. Eur Radiol. 2021;31(9):6592–6604. doi:10.1007/s00330-021-07922-w

14. Farag SI, Mostafa SA, El-Rabbat KE, El-Kaffas SM, Awara DM. The relation between pericoronary fat thickness and density quantified by coronary computed tomography angiography with coronary artery disease severity. Indian Heart J. 2023;75(1):53–58. doi:10.1016/j.ihj.2023.01.006

15. Oikonomou EK, Marwan M, Desai MY, et al. Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data. Lancet. 2018;392(10151):929–939. doi:10.1016/S0140-6736(18)31114-0

16. Guo B, Jiang M, Guo X, et al. Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD. Sci Bull. 2024;69(10):1472–1485. doi:10.1016/j.scib.2024.03.053

17. Von Knebel Doeberitz PL, De Cecco CN, Schoepf UJ, et al. Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia. Eur Radiol. 2019;29(5):2378–2387. doi:10.1007/s00330-018-5834-z

18. Zhang R, Ju Z, Li Y, Gao Y, Gu H, Wang X. Pericoronary fat attenuation index is associated with plaque parameters and stenosis severity in patients with acute coronary syndrome: a cross-sectional study. J Thorac Dis. 2022;14(12):4865–4876. doi:10.21037/jtd-22-1536

19. Yu Y, Shan D, Wang X, et al. Predictive value of the perivascular fat attenuation index for MACE in young people suspected of CAD. BMC Cardiovasc Disord. 2025;25(1):80. doi:10.1186/s12872-024-04401-0

20. Adolf R, Krinke I, Datz J, et al. Specific calcium deposition on pre-procedural CCTA at the time of percutaneous coronary intervention predicts in-stent restenosis in symptomatic patients. J Cardiovasc Comput Tomogr. 2025;19(1):9–16. doi:10.1016/j.jcct.2024.09.010

21. Jensen JM, Bøtker HE, Mathiassen ON, et al. Computed tomography derived fractional flow reserve testing in stable patients with typical angina pectoris: influence on downstream rate of invasive coronary angiography. Eur Heart J Cardiovasc Imaging. 2018;19(4):405–414. doi:10.1093/ehjci/jex068

22. Kueh SH, Mooney J, Ohana M, et al. Fractional flow reserve derived from coronary computed tomography angiography reclassification rate using value distal to lesion compared to lowest value. J Cardiovasc Comput Tomogr. 2017;11(6):462–467. doi:10.1016/j.jcct.2017.09.009

23. Nogic J, Kim J, Layland J, et al. Peri-coronary adipose tissue is a predictor of stent failure in patients undergoing percutaneous coronary intervention. Cardiovasc Revasc Med. 2023;53:61–66. doi:10.1016/j.carrev.2023.02.022

24. Kuntz RE, Baim DS. Defining coronary restenosis. Newer clinical and angiographic paradigms. Circulation. 1993;88(3):1310–1323. doi:10.1161/01.cir.88.3.1310

25. Otsuka F, Byrne RA, Yahagi K, et al. Neoatherosclerosis: overview of histopathologic findings and implications for intravascular imaging assessment. Eur Heart J. 2015;36(32):2147–2159. doi:10.1093/eurheartj/ehv205

26. Pelliccia F, Zimarino M, Niccoli G, et al. In-stent restenosis after percutaneous coronary intervention: emerging knowledge on biological pathways. Eur Heart J Open. 2023;3(5):oead083. PMID: 37808526; PMCID: PMC10558044. doi:10.1093/ehjopen/oead083

27. Sakamoto A, Sato Y, Kawakami R, et al. Risk prediction of in-stent restenosis among patients with coronary drug-eluting stents: current clinical approaches and challenges. Expert Rev Cardiovasc Ther. 2021;19(9):801–816. doi:10.1080/14779072.2021.1856657

28. McInerney A, Travieso Gonzalez A, Castro Mejía A, et al. Long-term outcomes after deferral of revascularization of in-stent restenosis using fractional flow reserve. Catheter Cardiovasc Interv. 2022;99(3):723–729. doi:10.1002/ccd.29823

29. Erdoğan M, Erdöl MA, Öztürk S, Durmaz T. Systemic immune-inflammation index is a novel marker to predict functionally significant coronary artery stenosis. Biomarker Med. 2020;14(16):1553–1561. doi:10.2217/bmm-2020-0274

30. le Noble F, Kupatt C. Interdependence of angiogenesis and arteriogenesis in development and disease. Int J Mol Sci. 2022;23(7):3879. doi:10.3390/ijms23073879

31. Hirai K, Kawasaki T, Sakakura K, et al. Determinants of insufficient improvement in fractional flow reserve following percutaneous coronary intervention. Heart Vessels. 2020;35(12):1650–1656. doi:10.1007/s00380-020-01645-6

32. Onuma Y, Collet C, van Geuns RJ, et al. ABSORB investigators. Long-term serial non-invasive multislice computed tomography angiography with functional evaluation after coronary implantation of a bioresorbable everolimus-eluting scaffold: the ABSORB cohort B MSCT substudy. Eur Heart J Cardiovasc Imaging. 2017;18(8):870–879. doi:10.1093/ehjci/jex022

33. Andreini D, Mushtaq S, Pontone G, Rogers C, Pepi M, Bartorelli AL. Severe in-stent restenosis missed by coronary CT angiography and accurately detected with FFRCT. Int J Cardiovasc Imaging. 2017;33(1):119–120. doi:10.1007/s10554-016-0971-4

34. Wang Z, Tang C, Zuo R, et al. Pre-PCI CT-FFR predicts target vessel failure after stent implantation. J Thorac Imaging. 2024;39(4):232–240. doi:10.1097/RTI.0000000000000791

35. Pepe M, Napoli G, Carulli E, et al. Autoimmune diseases in patients undergoing percutaneous coronary intervention: a risk factor for in-stent restenosis? Atherosclerosis. 2021;333:24–31. doi:10.1016/j.atherosclerosis.2021.08.007

36. Fernández-Alfonso MS, Gil-Ortega M, García-Prieto CF, Aranguez I, Ruiz-Gayo M, Somoza B. Mechanisms of perivascular adipose tissue dysfunction in obesity. Int J Endocrinol. 2013;2013:402053. doi:10.1155/2013/402053

37. Oguz M, Akbulut T, Saylik F, Sipal A, Erdal E. Association of coronary artery severity and late in-stent restenosis: an angiographic imaging study. Angiology. 2024;75(2):122–130. doi:10.1177/00033197221150953

38. Antonopoulos AS, Simantiris S. Detecting the vulnerable patient: toward preventive imaging by coronary computed tomography angiography. Circ Cardiovasc Imaging. 2023;16(2):e015135. doi:10.1161/CIRCIMAGING.122.015135

39. Antonopoulos AS, Sanna F, Sabharwal N, et al. Detecting human coronary inflammation by imaging perivascular fat. Sci Transl Med. 2017;9(398):eaal2658. doi:10.1126/scitranslmed.aal2658

40. Qin B, Li Z, Zhou H, Liu Y, Wu H, Wang Z. The predictive value of the perivascular adipose tissue CT fat attenuation index for coronary in-stent restenosis. Front Cardiovasc Med. 2022;9:822308. doi:10.3389/fcvm.2022.822308

41. Cui K, Liang S, Hua M, et al. Diagnostic performance of machine learning-derived radiomics signature of pericoronary adipose tissue in coronary computed tomography angiography for coronary artery in-stent restenosis. Acad Radiol. 2023;30(12):2834–2843. doi:10.1016/j.acra.2023.04.006

42. Jinnouchi H, Kuramitsu S, Shinozaki T, et al. Difference of tissue characteristics between early and late restenosis after second-generation drug-eluting stents implantation- an optical coherence tomography study. Circ J. 2017;81(4):450–457. doi:10.1253/circj.CJ-16-1069

43. Feng C, Zhang P, Han B, et al. Optical coherence tomographic analysis of drug-eluting in-stent restenosis at different times: a STROBE compliant study. Medicine. 2018;97(34):e12117. PMID: 30142870; PMCID: PMC6372013. doi:10.1097/MD.0000000000012117

Category: 3. Business

The biological characteristics of Bambusa oldhamii Munro

The basic genome analysis of B. oldhamii Munro

Data mining of transcriptome and metabolome in multiple shoot developmental phases

Joint analysis between transcriptome and metabolome

Model of bitter flavor formation in the shoot of B. oldhamii

Introduction

Materials and Methods

Patient Population

CT Acquisition and Reconstruction

CCTA Image Analysis

ICA Acquisition

Statistical Analysis

Results

Patient Characteristics and Clinical Outcomes

Correlation Analysis of CT-FFR, FAI and ISR

Univariate and Multivariate Analyses for ISR

Nomogram Construction and Prediction Performance of ISR Test Parameters

Discussion

Data Sharing Statement

Ethics Statement

Disclosure

References

Study design

Data sources

Participants

Primary outcome

Secondary outcomes

Covariates

Data processing

Statistical analysis