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Introduction

In China, ESCC stays the most prevalent histological subtype of esophageal cancer,¹ with Hebei Province reporting particularly high incidence rates.^2–4 Despite notable progress in diagnostic approaches and therapeutic regimens, the five-year survival rate for ESCC continues to fall below 30%.⁵ Clarifying the underlying mechanisms of ESCC is therefore imperative, as its pathogenesis is widely recognized to be multifactorial and highly complex.

It is widely acknowledged that the development and progression of ESCC involve a multitude of factors; however, the precise mechanisms driving its pathogenesis remain incompletely understood. While numerous studies have focused on external environmental and genetic influences,^6–11 host intrinsic factors also play a critical role in disease pathogenesis, among which the gut microbiota represents a key component of the internal milieu.^12,13

The gut microbiota is integral to host homeostasis, and dysbiosis has been associated with a range of conditions including obesity, type 2 diabetes, hepatic steatosis, intestinal disorders, and various cancers.¹⁴ With respect to tumorigenesis, extensive evidence supports a link between intestinal microbial dysbiosis and cancer development.^15–18 Disruption of microbial equilibrium has been implicated in metabolic dysregulation, activation of oncogenic signaling, and promotion of ESCC progression.¹⁹ Characteristic microbial alterations in ESCC have been documented, including enrichment of carcinogenic or potentially pathogenic taxa and depletion of beneficial or commensal organisms.^19–21

Notably, existing studies on the gut microbiota in ESCC are often limited by small sample sizes and reliance on short-read second-generation 16S rRNA sequencing. Larger cohorts would improve the reliability of clinical insights,²² and third-generation full-length 16S rRNA sequencing offers superior taxonomic resolution for microbial community analysis.²³ With ongoing advancements in PacBio SMRT 16S rRNA sequencing technology, especially the introduction of circular consensus sequencing (CCS), which significantly enhances sequencing accuracy, such improvements have successfully resolved prior doubts about its reliability.²⁴ Moreover, previous studies have adopted this technology and achieved promising results.²⁵

In the present study, we applied PacBio SMRT sequencing to fecal samples collected from 171 individuals in a high-incidence region of China, including 93 patients diagnosed with ESCC and 78 healthy controls. Our overarching aim was to characterize structural and functional metabolic alterations in the gut microbiota associated with ESCC, thereby providing new insights for preventive strategies and personalized therapeutic interventions.

Material and Methods

Research Design

From September 2023 to September 2024, a total of 117 treatment-naïve ESCC patients and 78 HCs were enrolled from the Fourth Hospital of Hebei Medical University. All ESCC diagnoses were histopathologically confirmed and staged according to the 8th edition of the American Joint Committee on Cancer (AJCC) staging system.²⁶ Healthy volunteers were recruited from the health examination center, and all exhibited test results within normal ranges with no evident abnormalities. From the initial ESCC cohort, 93 patients with stage I–III disease were selected for inclusion in this study, whereas all 78 HCs were included. Demographic information was collected for all participants, encompassing gender, age, body mass index (BMI), smoking behavior history, and alcohol drinking history. For the ESCC group, tumor stage and location were also documented. Written informed consent was secured from every participant.

Participant Inclusion Criteria

The eligibility framework for this study was established based on prior evidence²⁷ and comprised the following: (1) age between 18 and 85 years; (2) histopathological confirmation of ESCC without any previous diagnosis of malignancy; (3) clinical stage I–IV; and (4) Eastern Cooperative Oncology Group (ECOG) performance status of ≤2. Exclusion criteria included: (1) any prior chemotherapy, radiotherapy, or tumor resection; (2) suspected concurrent malignancies; (3) major cardiovascular events, including myocardial infarction or cerebrovascular accident; (4) administration of probiotics, antibiotics, proton pump inhibitors (PPIs), or hormone-related drugs within two months before enrollment; (5) prior gastrointestinal surgery; (6) history of inflammatory bowel disease (IBD) or irritable bowel syndrome (IBS); and (7) diabetes or depressive disorder. Healthy controls were required to maintain regular bowel habits and to abstain from antibiotics, probiotics, prebiotics, or synthetic agents during the two months preceding sample collection. Furthermore, all participants had to be of Han ethnicity and residents of Hebei Province for a minimum of 10 years.

Collection of Fecal Samples From Participants

Each participant contributed no less than 1.5 g of fecal material, collected in sterile containers, immediately subjected to liquid nitrogen freezing for 10 minutes to preserve microbial viability, and subsequently stored at −80°C until analysis. In total, 171 fresh stool samples were obtained, comprising 93 from individuals with ESCC and 78 from healthy controls.

Bacterial DNA Extraction and Subsequent Sequencing Analysis of the 16S rRNA Gene

Genomic DNA extraction from fecal specimens was executed utilizing the TGuide S96 Magnetic Fecal DNA Kit (TIANGEN Biotech (Beijing) Co., Ltd) in accordance with the manufacturer’s instructions. DNA quality verification and concentration quantification were conducted via electrophoresis on a 1.8% agarose gel, while measurements of nucleic acid concentration and assessments of its purity were performed with a NanoDrop 2000 UV-Vis spectrophotometer (Thermo Scientific, Wilmington, USA). The complete 16S rRNA gene sequences underwent amplification with primers 27F: AGRGTTTGATYNTGGCTCAG and 1492R: TASGGHTACCTTGTTASGACTT. Sample-specific PacBio barcode sequences were incorporated in both forward and reverse 16S primers for multiplexed sequencing. The amplification process employed KOD One PCR premix (TOYOBO Life Science) with initial denaturation at 95°C for 2 minutes, succeeded by 25 cycles consisting of denaturation at 98°C for 10 seconds, annealing at 55°C for 30 seconds, and extension at 72°C for 1 minute 30 seconds, concluding with extension at 72°C for 2 minutes to generate amplified products. Purification of PCR amplicons was achieved using VAHTS TM DNA Clean Beads (Vazyme, Nanjing, China), and quantification utilized the Qubit dsDNA HS Assay Kit with a Qubit 3.0 Fluorometer (Invitrogen, Thermo Fisher Scientific, Oregon, USA). After individual quantification procedures, equivalent amounts of amplicons were combined. The SMRTbell library preparation from amplified DNA utilized the SMRTbell Express Template Prep Kit 2.0. The PacBio Sequel II platform (Beijing Biomarker Technologies Co., Ltd., Beijing, China) performed sequencing of the purified SMRT bell libraries from pooled and barcoded samples using the Sequel II Binding Kit 2.0.

Analysis of Sequencing Data

Bioinformatics analysis was conducted using BMKCloud (http://www.biocloud.net/). Raw sequencing data underwent initial processing with SMRT Link software (v8.0) for quality control and demultiplexing, producing CCS reads. Assignment of sample-specific CCS sequences was performed with Lima (v1.7.0) through barcode recognition. Cutadapt (v2.7) enabled primer detection and sequence filtration, removing CCS reads lacking primers or outside the target length range (1200–1650 bp). Chimeric sequences were excluded using the UCHIME algorithm (v8.1), yielding clean reads. Sequence clustering into operational taxonomic units (OTUs) at 97% similarity was executed with USEARCH (v10.0), discarding OTUs detected fewer than two times across samples. Amplicon sequence variants (ASVs) were generated with DADA2 (v1.20.0), removing variants present in fewer than two occurrences overall. Taxonomic classification of OTUs was performed with the QIIME2 naive Bayesian classifier against the SILVA database (v138.1) using a 70% confidence cutoff. Alpha diversity indices were calculated in QIIME2 (v2020.6) and visualized in R. Beta diversity patterns were examined through principal coordinate analysis (PCoA), comparing community composition with unweighted Jaccard, weighted Bray-Curtis, and weighted UniFrac distances. Group-level differences in beta diversity were statistically evaluated by permutational multivariate analysis of variance (PERMANOVA). Linear Discriminant Analysis (LDA) Effect Size (LEfSe) analysis identified taxa with significant intergroup variation, with a logarithmic LDA score cutoff set at 3.0¹⁹ to emphasize differences in the gut microbiota. BugBase (https://github.com/knights-lab/BugBase) was employed for bacterial phenotype prediction, while PICRUSt2 (https://huttenhower.sph.harvard.edu/picrust) was used to predict microbial community functions, including Cluster of Orthologous Groups of proteins (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.

Statistical Methods

Data were analyzed using SPSS 26.0 (IBM, USA). Demographic traits were contrasted between the ESCC and HC groups: categorical variables were analyzed via the Chi-square test, and continuous variables were examined using the Student’s t-test. Microbial alpha and beta diversity were assessed by the Student’s t-test and PERMANOVA, respectively. Non-parametric tests were employed to analyze differential microbial abundance: the Wilcoxon rank-sum test was used for comparing two groups, and the Kruskal–Wallis test combined with Dunn’s post-hoc test was applied for multi-group comparisons. Bacterial phenotypes predicted by BugBase were compared using the Mann–Whitney U-test. Predicted COG and KEGG pathway abundances were evaluated using the Student’s t-test (corrected). Across all analytical procedures, statistical significance was defined as a two-sided p-value < 0.05.

Results

Clinical Features of the ESCC and HCs Groups

All participants were of Han ethnicity from Hebei Province, China. Statistical analyses revealed no notable variations in demographic and clinical baseline parameters between the ESCC and control groups (p > 0.05), confirming comparability across cohorts (Table 1).

A Comprehensive Exploration of 16S rRNA Sequencing Outcomes Across All the Participants

From 171 fecal samples, a total of 1,274,715 CCS reads were generated through barcode-based identification, with a per-sample minimum of 4,533 and an average of 7,454 CCS reads. Subsequent taxonomic classification yielded 8,805 OTUs (Supplementary Figure 1), which were annotated to 25 phyla, 763 genera, and 1,960 species. Venn diagram analysis (Figure 1A) identified 6,072 OTUs shared between groups, with 1,238 unique to HCs and 1,495 unique to ESCC patients. Rarefaction curves (Supplementary Figure 2), Shannon index curves (Supplementary Figure 3), and species accumulation curves (Supplementary Figure 4) verified adequate sequencing depth, while rank-abundance distribution curves (Supplementary Figure 5) detailed species richness and evenness across all samples.

Figure 1 Analysis of OTUs and Diversity Differences Between HCs and ESCC Patients. Venn diagram (A) illustrates the shared and distinct characteristics of the intestinal microbiota in two groups. Whereas box plots show the α-diversity indices of the two groups, including ACE index (B), Chao 1 index (C), Simpson index (D), Shannon index (E), and PD_whole_tree index (F). Statistical evaluations were performed using the Student’s t test. PCoA plots derived from unweighted Binary Jaccard (G), weighted Bray‒Curtis (H), and weighted UniFrac (I) distance matrices were assessed via PERMANOVA testing.

Exploring Gut Microbiota Diversity in ESCC Patients and Healthy Individuals

Alpha diversity analysis (HC group vs ESCC group) showed: ACE index (1885.70±78.14 vs 1390.82±70.69, p = 5.6e-06),Chao 1 index (1080.18±35.45 vs 862.80±35.02, p = 2.2e-05), Shannon index (5.59±0.09 vs 5.13±0.12, p = 0.0035), Simpson index (0.93±0.01 vs 0.90±0.01, p = 0.027), and PD_whole_tree index (17.92±0.38 vs 16.57±0.42, p = 0.018) (Figure 1B–F). PCoA plots of unweighted Jaccard, weighted Bray‒Curtis, and weighted UniFrac uncovered the distinct features of the two groups of samples (Figure 1G–I). PERMANOVA tests for unweighted binary Jaccard (R²=0.011, p=0.001), weighted Bray‒Curtis (R²=0.021, p=0.001), and weighted UniFrac (R²=0.028, p=0.001) demonstrated variations that are statistically significant in beta diversity between the two groups.

Analysis of the Intestinal Microbiota Composition Between the ESCC and HCs Groups

Phylum-level profiling revealed Firmicutes as the most abundant phylum in both groups, representing 84.99% in HCs and 71.73% in ESCC. The five predominant bacterial phyla were consistent between the two groups (Firmicutes, Bacteroidota, Proteobacteria, Actinobacteriota, Verrucomicrobiota), but significant changes in relative abundances were observed. Compared with HCs, the ESCC cohort displayed higher proportions of Bacteroidota (11.82% vs 8.21%), Proteobacteria (9.37% vs 3.84%), Actinobacteriota (4.65% vs 1.78%), and Verrucomicrobiota (0.98% vs 0.23%), while Firmicutes was relatively enriched in HCs (84.99% vs 71.73%) (Figure 2A).

Figure 2 Composition Distribution of Intestinal Microbiota at Multiple Taxonomic Levels Between the HC Group and the ESCC Group. Bar charts illustrate the top 10 most abundant phyla (A), genera (B), species (C) in the gut microbiota of both groups. Different colors represent distinct species, with the vertical extent of each colored segment indicating the relative abundance of that species. Species outside the top 10 are grouped as “Others”, unannotated taxa are classified as “Unknown”, and unclassified taxa are marked as “Unassigned”.

At the genus level, Blautia predominated in both cohorts (16.85% in HCs vs 12.74% in ESCC). The ESCC group showed increased representation of Bacteroides (8.82% vs 6.11%), Ruminococcus (3.27% vs 2.42%), Escherichia_Shigella (3.80% vs 1.40%), and Streptococcus (2.94% vs 2.25%), whereas HCs were characterized by higher levels of Blautia (16.85%), Faecalibacterium (4.53%), Romboutsia (6.16%), Eubacterium_hallii_group (5.96%), Dorea (4.26%), and Agathobacter (4.02%) (Figure 2B).

Species-level comparison identified Romboutsia_timonensis as most abundant in HCs (6.02%) and Eubacterium_hallii in ESCC (4.26%). Escherichia_coli was notably increased in ESCC (3.80% vs 1.39%), while HCs exhibited enrichment of Faecalibacterium_prausnitzii (5.27%), Romboutsia_timonensis (6.02%), Eubacterium_hallii (5.92%), Eubacterium_rectale (3.82%), Lachnospiraceae_bacterium (3.28%), Dorea_longicatena (3.44%), and multiple Blautia subspecies, including B. obeum (4.08%), B. massiliensis (2.68%), and B. wexlerae (3.21%) (Figure 2C).

Differential Microbiota Analysis Between the ESCC and HCs Groups

Comparative microbial community structures were evaluated using Wilcoxon rank-sum tests across multiple taxonomic levels.

At the phylum level, 8 phyla displayed significant intergroup differences. Firmicutes (p = 0.0004) showed higher relative abundance in HCs, whereas Actinobacteriota (p = 0.0004) and Proteobacteria (p = 0.0290) were significantly elevated in ESCC (Figure 3A).

Figure 3 Comparison of Intestinal Microbiota Between HCs and ESCC Patients at Different Taxonomic Levels. All notable variations at the phylum level (A) are displayed, and the top 20 intestinal bacteria exhibiting differences at the genus (B) and species (C) levels are presented (Wilcoxon rank test, p < 0.05). *p < 0.05, **p < 0.01, ***p < 0.001.

At the genus level, 155 genera differed markedly between groups. Blautia (p = 0.0021), Eubacterium_hallii_group (p = 0.0002), Faecalibacterium (p = 0.0021), Dorea (p = 0.0019), and Agathobacter (p = 0.0005) were enriched in HCs, while Streptococcus (p = 0.0368) predominated in ESCC (Figure 3B).

At the species level, 331 species exhibited significant divergence. Blautia_massiliensis (p = 0.0035), Lachnospiraceae_bacterium (p = 1.22e-05), Eubacterium_hallii (p = 0.0002), Eubacterium_rectale (p = 0.0002), Blautia_obeum (p = 0.0003), Blautia_wexlerae (p = 0.0007), Dorea_longicatena (p = 0.0021), and Faecalibacterium_prausnitzii (p = 0.0044) showed enrichment in HCs, whereas Streptococcus_thermophilus (p = 0.0013) was significantly more abundant in ESCC (Figure 3C).

LEfSe Analysis Reveals Significantly Enriched Differential Intestinal Microbiota Between ESCC Group and HCs

Analysis of the intestinal microbiota associated with ESCC was performed using LEfSe with an LDA threshold >3 to identify taxa displaying significant differential enrichment between the ESCC and HC groups (Figure 4). A total of 23 taxa demonstrated higher abundance in the ESCC group, whereas 54 taxa predominated in the HCs. At the genus level, ranked by descending LDA scores, Bifidobacterium, Ligilactobacillus, Lactococcus, Intestinibacter, Paucibacter, Acinetobacter, and Mogibacterium were significantly enriched in ESCC (LDA > 3). In contrast, enrichment in HCs was observed for Blautia, Holdemanella, Eubacterium_hallii_group, Anaerostipes, Dialister, Ruminococcus_torques_group, Faecalibacterium, Agathobacter, Dorea, Subdoligranulum, and Erysipelotrichaceae_UCG_003 (LDA > 3.5).

Figure 4 LEfSe Application for Detecting Taxonomic Groups with Significant Abundance Differences in Gut Microbiota of HC and ESCC Patients (Wilcoxon rank sum test, p < 0.05). The LDA histogram (A) illustrates microbial classifications exhibiting LDA scores greater than 3 within the intestinal microbiota of both groups. The phylogenetic tree of bacterial indicators (B) ranging from phylum (the innermost ring) to species (the outermost ring), emphasizing microbial taxa that exhibit an LDA score exceeding 3. Unique bacterial groups are marked with lowercase letters. At each taxonomic level, every small circle stands for a single taxonomic unit, and the circle’s size is proportional to its relative abundance. Colors distinguish between the HC and ESCC groups, while nodes of different colors indicate key microbial communities specific to each group.

Potential Biomarkers for Distinguishing ESCC Patients From Healthy Subjects

LEfSe and LDA analyses revealed seven genera with significant enrichment in the gut microbiota of ESCC patients, with Bifidobacterium displaying the highest LDA score. Receiver Operating Characteristic (ROC) curve analysis for this genus yielded an AUC of 0.64 (95% CI: 0.556–0.723) (Supplementary Figure 6). To enhance diagnostic accuracy, a composite model was constructed by integrating all seven genera, which produced an improved AUC of 0.726 (95% CI: 0.650–0.801), indicating its potential value in distinguishing ESCC patients from healthy individuals (Figure 5).

Figure 5 ROC Curve for Significantly Enriched Bacterial Genera in the Gut Microbiota of Patients Diagnosed with ESCC. This diagnostic model includes seven bacterial genera, namely Bifidobacterium, Ligilactobacillus, Lactococcus, Intestinibacter, Paucibacter, Acinetobacter, and Mogibacterium. The AUC was 0.726 (95% CI: 0.650–0.801), with a cutoff value of 0.471 (Sensitivity = 0.692, Specificity = 0.667).

Associations Between Microbial Taxa and Clinicodemographic Features in ESCC

Associations between microbial taxa and clinicodemographic features were further evaluated in patients with ESCC. Significant variations in microbial abundance were observed according to clinical stage: Acinetobacter levels differed between HCs and patients with stage T1 (p = 0.0070) or T3 (p = 0.0250) ESCC; Bifidobacterium abundance varied between HCs and T1 (p = 0.0090) or T3 (p = 0.0160) stages; and Mogibacterium was significantly different specifically between HCs and T2-stage ESCC (p = 0.0020) (Supplementary Table 1).

Tumor location was also a determining factor: Bifidobacterium was enriched in the middle thoracic segment of ESCC tumors relative to HCs (p = 0.0001) and the lower thoracic segment (p = 0.0240). Intestinibacter was more abundant in ESCC upper thoracic segments compared to HCs (p = 0.0004), lower thoracic segments (p = 0.0030), and middle thoracic segments (p = 0.0150). Ligilactobacillus was elevated in ESCC upper thoracic segments relative to both HCs (p = 0.0060) and lower thoracic segments (p = 0.0110), while Mogibacterium was higher in the middle thoracic segment than in HCs (p = 0.0080). Conversely, Paucibacter was reduced in ESCC lower thoracic segments compared to HCs (p = 0.0030) (Supplementary Table 2).

Sex-specific differences were also observed: Intestinibacter was higher in female than male ESCC patients (p = 0.0002). Compared to female HCs, ESCC females had elevated Bifidobacterium (p = 0.0480), while ESCC males showed reduced Lactococcus relative to male HCs (p = 0.0040). Older ESCC patients exhibited higher Intestinibacter levels than younger patients (p = 0.021). Low-BMI ESCC patients had more Bifidobacterium (p = 0.005) and Intestinibacter (p = 0.006) than low-BMI HCs (Supplementary Tables 3–5).

Lifestyle factors influenced microbial abundance: non-smoking ESCC patients had higher Bifidobacterium and Intestinibacter than smoking HCs (p = 0.0003 and p = 0.0009, respectively). Similarly, non-drinking ESCC patients showed elevated levels of these genera compared to drinking HCs (p = 0.0030 and p = 0.0008) (Supplementary Tables 6 and 7).

Prediction of Gut Microbiota Functions in ESCC and HC Groups

BugBase-based microbial phenotype prediction was applied to delineate the functional attributes of microbial communities and their relevance to health and disease.²⁸ Relative to the HC group, the ESCC group exhibited a marked increase in facultative anaerobes (p = 0.0046, Figure 6A) and gram-negative bacteria (p = 0.0066, Figure 6B), accompanied by a reduction in anaerobic bacteria (p = 0.0009, Figure 6C) and gram-positive bacteria (p = 0.0066, Figure 6D). Functional inference from 16S rRNA data was subsequently conducted using PICRUSt2. COG annotation indicated significant enrichment in the ESCC group for categories including “Posttranslational modification, protein turnover, chaperones”, “Intracellular trafficking, secretion, and vesicular transport”, “Inorganic ion transport and metabolism”, “Cell wall/membrane/envelope biogenesis”, “Secondary metabolites biosynthesis, transport and catabolism”, “RNA processing and modification”, “Lipid transport and metabolism”, “Function unknown”, “Extracellular structures”, and “Chromatin structure and dynamics”, whereas “Transcription”, “Signal transduction mechanisms”, “Defense mechanisms”, and “Cell cycle control, cell division, chromosome partitioning” were diminished (Figure 6E). KEGG annotation further demonstrated significant enrichment in the ESCC group for pathways related to “digestive system”, “other amino acid metabolism”, “cancer: overview”, “endocrine system”, “infectious diseases: parasitic diseases”, “aging”, “transport and catabolism”, “antitumor drug resistance”, “biodegradation and metabolism of xenobiotics”, “glycan biosynthesis and metabolism”, “bacterial infectious diseases”, and “lipid metabolism”, while activity in “cofactor and vitamin metabolism” was significantly reduced (Figure 6F).

Figure 6 Phenotypic and Functional Prediction Analysis of the Gut Microbiome in the HC Group and the ESCC Group. Scatter plots illustrate the phenotypes of facultative anaerobes (A), gram-negative bacteria (B), anaerobic bacteria (C) and gram-positive bacteria (D) in each group, with significance statistically evaluated by the Mann‒Whitney U-test. The disparities in COG function (E) and the predictive analysis of the second-level metabolic pathways of the KEGG (F) between the two groups were analyzed using Student’s t test, corrected p < 0.05.

Discussion

In this study, full-length 16S rRNA sequencing identified marked alterations in the gut microbiota of ESCC patients relative to healthy controls, including both α- and β-diversity as well as enrichment of discriminatory bacterial genera determined through LEfSe. A diagnostic model constructed from these taxa exhibited potential diagnostic value. Furthermore, the abundance of some of these discriminative genera was significantly associated with clinicodemographic characteristics, suggesting host-microbe interactions may be influenced by patient demographics and disease status. Functional predictions via BugBase and PICRUSt2 suggested that these microbial shifts may be linked to altered bacterial traits and metabolic pathways. Collectively, these results may provide biological insights that could inform the development of microbiota-based screening and therapeutic approaches for ESCC, and could help advance our understanding of host–microbiota interactions in cancer.

Analysis of microbial diversity demonstrated a significant reduction in alpha diversity among ESCC patients compared with controls, in agreement with prior findings by Shen et al.²⁹ Variability in reported alpha diversity across studies may reflect heterogeneity in study populations, sequencing platforms, or potential confounders such as pharmacological exposure.^19–21 By contrast, alterations in beta diversity have been consistently documented,^{19–21,28–30} suggesting that restructured microbial community composition, rather than simple richness or evenness, constitutes a more reliable feature of ESCC and may capture signals associated with tumor burden.

Analysis of taxonomic composition emphasized alterations at the genus level. Unlike several earlier reports, significant enrichment of pathogen-associated genera frequently described in ESCC, including Streptococcus,¹⁹ Bacteroides²¹ and Escherichia_Shigella²⁸ was not detected. In contrast, enrichment of Bifidobacterium aligned with previous observations.³⁰ Importantly, this investigation provides, to the best of current knowledge, the first evidence of substantial enrichment of additional genera with pathogenic potential in ESCC, such as Intestinibacter, Acinetobacter, and Mogibacterium. These newly identified microbial features may be closely linked to region-specific factors: our samples were from Hebei Province (northern China), where high dietary sodium intake and arid climate are prevalent, and these conditions likely shape the distinctive gut microbiota composition in this population. Consistent with this, previous studies³¹ have explicitly shown that dietary and geographical differences have a significant, observable impact on gut microbiota.

Furthermore, our analysis showed that the abundance of these discriminative genera had heterogeneous patterns but varied significantly with clinicodemographic features, often in non-linear relationships. For instance, Acinetobacter and Bifidobacterium relative abundances did not increase continuously from normal controls to T3-stage ESCC patients. Patients with middle/lower thoracic tumors exhibited a significantly lower relative abundance of Intestinibacter than did patients with upper thoracic tumors, suggesting a specific association with tumor longitudinal localization. Beyond tumor stage and location, variables including sex, age, BMI, smoking, and alcohol consumption were also linked to specific microbial differences, though the clinical implications of these associations require further assessment. These nuanced associations support the premise that gut microbiota is closely tied to ESCC’s clinicopathological landscape.²¹

Further analysis demonstrated significant enrichment of Bifidobacterium in ESCC patients’ gut microbiota. Although consistent with previous observations,³⁰ this pattern presents a paradox regarding the functional implications of Bifidobacterium. Traditionally Bifidobacterium is regarded as a probiotic with health-promoting effects linked to bile acid metabolism.^32,33 Yet, earlier studies have demonstrated that bile acids may act as key mediators in esophageal carcinogenesis.^34,35 Thus, we must interpret this seemingly contradictory phenomenon with extreme caution.³⁶ Moreover, Bifidobacterium generates metabolites such as lactate and acetate.³⁷ Lactate accumulation has been associated with protumorigenic activities, including angiogenesis, migration, and metastasis.³⁸ A colorectal cancer study from Italy that integrated microbiome and metabolomic profiling reported a positive association between Bifidobacterium abundance and lactate levels,³⁹ emphasizing the necessity of validating this relationship in ESCC through metabolomic approaches. Given that acetate has been demonstrated to act as an energy source for tumor cells,⁴⁰ the hypothesis that microbiota-derived acetate functions similarly as an energy source for ESCC cells further warrants targeted metabolomic investigation. In addition, mounting preclinical and clinical evidence has linked Bifidobacterium to responsiveness to immune checkpoint therapy (ICT), including treatment with anti-PD-1/PD-L1 antibodies across multiple cancer types.^41–43 This emerging association reflects the intricate interplay between the gut microbiota and antitumor immunity and suggests significant translational and clinical potential for exploiting microbiome modulation in cancer therapeutics.

Beyond Bifidobacterium, other differentially enriched genera in our study also warrant mechanistic scrutiny, particularly those with known pathogenic traits. Acinetobacter, a genus of pathogenic bacteria characterized by complex and broad-spectrum antibiotic resistance,⁴⁴ can evade host innate immune defenses, proliferate rapidly to high biomass, and trigger significant inflammatory responses.^44,45 Emerging evidence further suggests its potential carcinogenicity: it has been reported to form biofilms on human epithelial surfaces, facilitating attachment, colonization, and infection.⁴⁶ Biofilms are thought to promote tumorigenesis through multiple mechanisms, including inflammation-mediated DNA damage, modulation of host immunity, production of carcinogenic toxins, alterations to local metabolic environments, and interactions with other microorganisms in the tumor microenvironment.⁴⁷ These features imply that Acinetobacter may adversely affect ESCC progression, justifying further in-depth studies into its clinical relevance and mechanistic roles. The esophageal mucosal biofilm may serve as a key mediator in gut microbiota-driven regulation of ESCC. As reviewed by Song et al⁴⁸ the esophageal biofilm is not a simple microbial aggregate but a functional complex: its dysbiosis can directly promote carcinogenesis by secreting toxins to activate the NF-κB pathway and producing metabolites such as lactate to induce epithelial cell proliferation. Concurrently, gut pathogens may colonize the esophagus retrogradely via gastroesophageal reflux, participate in biofilm formation, and thereby establish a “gut-biofilm-tumor” regulatory axis.⁴⁸

In the present study, LEfSe analyses identified seven bacterial genera with significant differential abundance: Bifidobacterium, Ligilactobacillus, Lactococcus, Intestinibacter, Paucibacter, Acinetobacter, and Mogibacterium, which represent potential microbial biomarkers to distinguish ESCC patients from HCs. A diagnostic model built using these candidate biomarkers exhibited good discriminatory performance (AUC = 0.726, 95% CI: 0.650–0.801), directly validating their diagnostic utility and supporting the model’s potential as an auxiliary ESCC screening tool. Overall, our work represents a beneficial preliminary step toward non-invasive diagnosis for patients with ESCC, and further external validation is needed to enhance the potential value of the diagnostic model.⁴⁹

Beyond diagnostic relevance, the observed microbial alterations may indicate functional disruptions within the gut ecosystem. Notably, ESCC patients exhibited marked depletion of several SCFA-producing genera, including Blautia, Eubacterium_hallii group, Anaerostipes, Ruminococcus torques group, and Faecalibacterium, in comparison with HCs. SCFAs such as acetate, propionate, and butyrate, generated through microbial fermentation,⁵⁰ serve as central metabolites influencing host cell differentiation and apoptosis, regulating immune responses, and sustaining intestinal barrier integrity.⁵¹ Evidence consistently demonstrates that SCFAs, particularly butyrate, exert anti-inflammatory, barrier-protective, and anti-neoplastic effects.^52–54 Accordingly, the depletion of SCFA-producing taxa in ESCC patients may correspond to reduced SCFA availability, thereby weakening their protective influence on the host. As this conclusion remains preliminary, subsequent studies incorporating serological or fecal metabolomic analyses will be required to establish whether gut microbiota-derived SCFA levels are significantly altered in ESCC.

Predictive functional profiling revealed a markedly altered gut microbiota in ESCC patients, characterized by enrichment of gram-negative bacteria and facultative anaerobes, with concomitant upregulation of pathways related to membrane biogenesis, intracellular transport, and metabolic reprogramming. Such alterations indicate a microenvironment conducive to tumor progression.⁵⁵ Nevertheless, as these results are based on in silico predictions, metabolomic and mechanistic validation is needed to confirm their relevance to ESCC pathogenesis.

Future research will adopt a multicenter framework to overcome the restricted generalizability of single-region recruitment, apply stringent control of confounding variables such as diet and lifestyle, and incorporate longitudinal sampling across disease stages. It will also integrate comprehensive documentation of dietary habits, lifestyle, demographics, and extended clinical indicators (beyond tumor stage/location). Subsequent analyses will examine associations between differentially enriched genera (eg, Acinetobacter, Bifidobacterium) and additional clinical parameters (eg, age, sex, comorbidities) to identify microbiota-clinical trait correlations with prognostic relevance. This optimized design is anticipated to generate more broadly applicable insights into the interactions between gut microbiota and ESCC and to support the translation of microbial biomarkers into clinical practice.

Several limitations should be acknowledged: (1) A cross-sectional, single-center design compromises sample representativeness; multicenter studies are needed to validate findings in diverse populations. (2) Results indicate correlation, not causality—mechanistic validation (eg, animal studies) and clinical-basic research integration are required. (3) Inclusion of only treatment-naïve, stage I–III ESCC patients excludes advanced cases, limiting insight into dynamic microbiota changes during progression; future studies should enroll patients across all stages/treatment statuses.

Conclusion

Our findings demonstrate that ESCC patients exhibit significant gut microbiota dysbiosis in structure, composition, and function. Among the significantly differential bacterial genera identified by LEfSe, these taxa not only hold potential diagnostic value, but some also exhibit differential abundance patterns that correlate with clinicodemographic characteristics. These specific microbial perturbations provide new insights into ESCC etiology and reinforce its close link to ESCC development. This study lays a preliminary theoretical foundation for the future development of microbiota-based strategies for ESCC prevention or adjuvant therapy.

Data Sharing Statement

All original data sequences are accessible through the Sequence Read Archive (SRA) database at the National Center for Biotechnology Information (NCBI). The accession number is PRJNA1255730 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1255730). The raw data for the samples involved in this study are presented in Supplementary Table 8.

Ethical Statement

This investigation represents a prospective descriptive investigation carried out per the Declaration of Helsinki and sanctioned by the Ethics Committee of the Fourth Hospital of Hebei Medical University (2022KY057). All participants gave written informed consent and agreed to participate in the study.

Acknowledgments

The authors thank Shanghai Biotree Biomedical Technology Co., Ltd for its assistance in sample testing for all subjects in this study.

Author Contributions

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agreed to be accountable for all aspects of the work.

Funding

This research was supported by the National Natural Science Foundation of China (Project No. 82274593), the S&T Program of Hebei (Project No. 223777122D), the Natural Science Foundation Project of Hebei Province (Project No. H2023206137), and the Administration of Traditional Chinese Medicine of Hebei Province (Project No. 2024283).

Disclosure

The authors state that they have no competing interests in this work.

References

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Introduction

Breast cancer (BC) is the most prevalent cancer among women globally. According to the 2020 Global Cancer Statistics, BC accounts for 11.7% of newly diagnosed cancer cases and 6.9% of cancer-related deaths.¹ BC exhibits high heterogeneity and can be classified into multiple subtypes based on receptor status, with the most common subtypes being luminal A and luminal B, characterized by estrogen receptor (ER) positivity, accounting for 65–70% of all invasive BC cases.^2–4 Despite significant advances in endocrine and targeted therapies, luminal BC remains clinically challenging: more than 40% of patients eventually relapse or lose their luminal/epithelial characteristics, leading to poorly differentiated tumors with enhanced invasiveness and metastatic potential.^1,5,6 Furthermore, the disease course in luminal BC is often prolonged, and late recurrence contributes to the fact that the 20-year survival rate is not markedly superior to that of other subtypes.⁷ Given its high prevalence, distinctive pattern of late recurrence, and tendency toward therapeutic resistance, it is critical to investigate the molecular mechanisms driving luminal BC progression to develop more precise and durable treatment strategies.

Increasing evidence has shown that the tumor microenvironment (TME) plays a crucial role in the progression of BC, with immune regulation in the TME being particularly important. Programmed death ligand-1 (PD-L1), as a major inhibitory checkpoint molecule, plays a critical role in this process. The PD-L1 expressed on tumor cells binds to the PD-1 receptor on immune cells, inhibiting T-cell-mediated anti-tumor responses, thereby helping tumor cells escape immune surveillance.⁸ Although luminal BC is typically considered to have a moderate or low immune profile compared to other subtypes,⁹ Deici et al reported that 9% of luminal A and 42% of luminal B cases exhibited PD-L1 immune reactivity.¹⁰ Ni et al further pointed out that the expression of PD-L1 in tumor-infiltrating immune cells is associated with better prognosis in luminal B BC.¹¹ These findings suggest that immune regulation in luminal BC is more dynamic than previously thought. Post-translational modifications, especially ubiquitination, significantly affect the stability and function of PD-L1, thereby regulating its immune inhibitory effect.¹² Zhu et al found that deubiquitinating enzymes promote immune evasion in the BC cell line MCF-7 cells by preventing the degradation of PD-L1.¹³ However, the precise molecular mechanisms by which ubiquitination regulates PD-L1 in luminal BC remain unclear.

C-type lectin domain family 3 member A (CLEC3A) is a cell surface protein involved in various immune processes. Previous studies have demonstrated that high expression of CLEC3A in invasive ductal carcinoma of the breast is associated with poor prognosis and promotes tumor progression.¹⁴ More recently, CLEC3A has been identified as a driver of aggressiveness in both ER+ and ER− breast cancers.¹⁵ In addition, Ni et al reported that CLEC3A overexpression enhances the proliferation, migration, and invasion of BT474 breast cancer cells.¹⁶ Another study demonstrated that CLEC3A is an immune-related hub gene in BC.¹⁷ However, despite these findings, the specific mechanisms by which CLEC3A promotes luminal BC progression, particularly its role within the TME, remain largely unknown. Based on prior evidence, we hypothesized that CLEC3A may regulate PD-L1 stability through ubiquitination, thereby contributing to immune evasion in luminal BC.

Therefore, the present study aimed to explore the molecular interactions between CLEC3A and PD-L1 and elucidate the potential role of CLEC3A as a therapeutic target for enhancing anti-tumor immunity in luminal BC.

Methods

Differentially Expressed Genes (DEGs) Screening

Expression profiles from the GEO dataset GSE115144 (total 42 samples, including 21 luminal BC tumor samples and 21 adjacent normal samples) and the TCGA-BC datasets (total 1180 samples, including 614 luminal BC and 88 normal breast tissue samples) were used in this study. DEGs were identified by using the limma R package. First, DEGs between luminal BC and normal samples were screened in the GSE115144 dataset with a threshold of p<0.05 and |log2 fold change (FC)|>1.2. Subsequently, the upregulated genes were further validated for differential expression between luminal BC and normal samples in the TCGA cohort, applying a threshold of p<0.05 and |log2 FC|>0.2. The results were visualized in the volcano plot and heatmap using the ggplot2 package.

Clustering Analysis

Consensus clustering based on Non-negative Matrix Factorization (NMF) was performed using the expression profiles of DEGs to classify luminal BC samples into different molecular subgroups. The optimal number of clusters was determined according to the cophenetic correlation coefficient, dispersion, and silhouette width. Subsequently, NMF feature weight matrices were used to identify key contributors within each cluster. Kaplan-Meier (KM) survival analysis was conducted using survminer and survival R packages to compare the overall survival (OS) between different clusters. A heatmap for hub gene expression in different clusters was generated using the pheatmap R package, and violin plots comparing the expression of hub genes across clusters were generated using ggplot2.

Evaluation of Prognostic Implications and Immune Correlation

The expression levels of hub genes in normal and tumor tissues were evaluated using the TCGA-BC dataset. Statistical analysis was performed using the Wilcoxon rank-sum test. Survival analysis for hub genes was conducted based on high and low expression levels. Hazard ratios (HR) were calculated using the Cox proportional hazards model, and KM survival curves were plotted. The correlation between hub genes and the immune checkpoint gene CD274 (PD-L1) was analyzed using Spearman correlation and visualized using scatter plots.

Immunohistochemistry (IHC) Assay

IHC was performed to evaluate CLEC3A expression in paraffin-embedded tissues. Sections (4 μm) were deparaffinized, rehydrated, and subjected to antigen retrieval in Tris-EDTA buffer. Endogenous peroxidase activity was blocked with 0.3% hydrogen peroxide for 10 min, followed by incubation with 5% normal goat serum for 30 min. Slides were then incubated overnight at 4°C with a rabbit anti-CLEC3A antibody, washed, and treated with HRP-conjugated secondary antibody for 30 min at room temperature. Signal detection was performed using 3,3′-diaminobenzidine and counterstained with hematoxylin. The expression of CLEC3A was evaluated according to the percentage of positive cells.

Cell Culture and Treatment

Human normal mammary epithelial cells MCF 10A and Human luminal BC cell lines MCF-7 (luminal A) and BT-474 (luminal B) were purchased from the Wuhan Pricella Biotechnology Co., Ltd. (Wuhan, China). All cells were cultured in DMEM (Sigma-Aldrich, MO, USA) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin at 37°C in a 5% CO₂ incubator.

Cell Transfection

For CLEC3A stable knockdown in MCF-7 and BT-474 cells, lentiviral vectors carrying short hairpin RNA targeting CLEC3A (sh-CLEC3A) and the negative control (shNC) were obtained from GenePharma (Shanghai, China), and transfected cells were selected using puromycin (Clontech, USA). To achieve CLEC3A overexpression in MCF-7 and BT-474 cells, the pcDNA3.1-CLEC3A plasmid was transfected into the cells by Lipofectamine 2000. Expression levels were verified by real-time quantitative PCR (RT-qPCR) analysis after 48 h of transfection.

Cell Viability Analysis

Cell viability was assessed using the cell counting kit-8 (CCK-8) assay (Beyotime, Shanghai, China). MCF-7 and BT-474 cells were seeded at a density of 5 × 10³ cells per well in 96-well plates. Following the treatment for 12 h, 24 h, 48 h, or 72 h, 10 µL of CCK-8 solution was added to each well and the cells were incubated for an additional 2 h at 37°C. The absorbance at 450 nm was measured using a microplate reader (BioTek, USA).

Tunnel Assay for Cell Apoptosis

The TUNEL assay was performed to detect apoptotic cells in MCF-7 and BT-474 cells. Cells were fixed with 4% paraformaldehyde for 30 min at room temperature and washed with PBS. Cells were then permeabilized with 0.1% Triton X-100 in PBS for 5 min. Subsequently, the TUNEL reaction mixture, containing terminal deoxynucleotidyl transferase (TdT) and biotinylated dUTP, was added to the cells and incubated for 60 min at 37°C in a humidified chamber. Afterward, the cells were washed with PBS and incubated with streptavidin-HRP (Horseradish peroxidase) for 30 min at room temperature. The apoptotic cells were visualized by adding the DAB substrate, which turns the apoptotic cells brown due to the enzymatic reaction. The number of TUNEL-positive cells was counted under a light microscope, and the apoptosis rate was calculated as the percentage of TUNEL-positive cells relative to the total number of cells.

Colony Formation Assay

Transfected cells were seeded at a density of 2.5 × 10⁵ cells per well in 24-well plates. Every three days, the medium was refreshed. Colony formation was monitored under an optical microscope (AE2000, Motic, China). After two weeks, cells were fixed with 4% paraformaldehyde at 4°C for 1 h and then washed with PBS. Crystal violet staining solution was added to the wells to stain the colonies. Following a thorough wash with PBS, colony images were captured using a camera (Canon Ltd., Japan). The number of colonies was counted microscopically.

Wound Healing Assay

MCF-7 and BT-474 cells were seeded into 6-well plates at a density of 1 × 10⁶ cells per well. A sterile 200 µL pipette tip was used to create a straight scratch in the center of the cell monolayer. After wounding, the medium was replaced with serum-free DMEM to prevent further cell proliferation. The cells were then incubated at 37°C with 5% CO₂. Images of the wound area were taken at 0 and 24 h under a microscope to observe cell migration into the wound area. The wound closure was quantified by measuring the distance between the wound’s edges using image analysis software.

Transwell Assay for Cell Invasion

Cells were starved overnight in a serum-free medium and then resuspended in the same medium. The upper chamber of the Transwell insert (Corning, NY, USA) was coated with Matrigel (BD Biosciences, NJ, USA). A total of 1 × 10⁴ cells in 200 µL serum-free medium were added to the upper chamber, while the lower chamber contained 600 µL of DMEM with 10% FBS. The cells were incubated for 24 h at 37°C with 5% CO₂. After incubation, non-invading cells were removed from the upper surface of the membrane. The invading cells on the lower surface of the membrane were fixed with 4% paraformaldehyde for 30 min, stained with crystal violet for 30 min, and then washed with PBS. The invaded cells were counted under a microscope. The invasive ability was quantified by calculating the average number of invaded cells.

Cycloheximide (CHX) Chase Assay

For the CHX chase assay, MCF-7 and BT-474 cells transfected with sh-CLEC3A were treated with 50 μg/mL CHX and harvested at 0, 4, 8, and 12 h post-treatment. The resulting lysates were analyzed by Western blotting using anti-CLEC3A and anti-GAPDH antibodies to assess protein degradation and stability.

RT-qPCR Assay

Total RNA was extracted using TRIzol reagent (Invitrogen, CA, USA) following the manufacturer’s protocol. Reverse transcription was performed using the PrimeScript™ RT Reagent Kit (Takara, Japan) to synthesize complementary DNA (cDNA) from 1 µg of total RNA in a 20 µL reaction volume. The resulting cDNA was then used for qPCR, which was conducted using SYBR™ Green PCR Master Mix (Thermo Fisher, CA, USA) on a QuantStudio™ 7 Flex Real-Time PCR System (Applied Biosystems, CA, USA). The reaction was performed in a 20 µL volume containing 10 µL SYBR Green PCR Master Mix, 0.5 µL of each primer (10 µM), 2 µL of cDNA, and 7 µL of RNase-free water. The PCR conditions included an initial denaturation step at 95°C for 2 min, followed by 40 cycles of 95°C for 5 s and 60°C for 30s. The relative gene expression was calculated using the 2^−ΔΔCt method, with GAPDH as the internal control. The primer sequences are shown in Table S1.

Western Blotting Analysis

Cells were lysed using RIPA buffer (Thermo Fisher, CA, USA). The total protein concentration was determined using the BCA Protein Assay Kit (Thermo Fisher, CA, USA). Equal amounts of protein (20 µg) were separated by 10% SDS-PAGE and transferred to PVDF membranes (Beyotime, Shanghai, China). The membranes were blocked with 5% non-fat dry milk in TBST buffer for 1 h at room temperature. Membranes were then incubated overnight at 4°C with primary antibodies: anti-PD-L1 (Abcam, Cambridge, UK) and anti-GAPDH (Abcam, Cambridge, UK) as the loading control. After washing with TBST, the membranes were incubated with HRP-conjugated secondary antibodies for 1 h at room temperature. The protein bands were visualized using the ECL elements (Thermo Fisher, CA, USA) and captured with a chemiluminescence imaging system (Bio-Rad, CA, USA). The band intensities were quantified using ImageJ software, and the relative expression levels of PD-L1 were normalized to GAPDH.

Co-Culture for CD8⁺ T Cells with BC Cell Lines

To evaluate the interaction between CD8⁺ T cells and BC cell lines, a co-culture system was established using activated human CD8⁺ T cells and MCF-7 or BT-474 cells. CD8⁺ T cells were isolated from peripheral blood using a CD8⁺ T Cell Isolation Kit (Miltenyi Biotec, Germany) and activated with anti-CD3 and anti-CD28 antibodies for 24 h in a complete medium. After activation, the CD8⁺ T cells were co-cultured with the BC cell lines at a ratio of 1:1 in a Transwell system, allowing for indirect interaction between the T cells and tumor cells while preventing direct cell contact.

Crystal Violet Assay for Cell Viability

The Crystal Violet assay was used to assess the viability of BC cells after different treatments. Cells were seeded in 6-well plates at a density of 1 × 10⁵ cells per well and allowed to grow for 24 h. After treatment, the cells were washed with PBS and fixed with 4% paraformaldehyde for 30–90 s at room temperature. The fixed cells were stained with 0.1% crystal violet solution (Sigma-Aldrich, USA) for 10–20 min at room temperature, followed by washing with PBS to remove excess dye. The number of alive tumor cells was quantified by counting the crystal violet-stained cells under a microscope. The stain was solubilized using 10% acetic acid for 1 h, and the absorbance was measured at 570 nm using a microplate reader.

Enzyme-Linked Immunosorbent Assay

The levels of IL-2 and TNF-γ in the supernatant of co-cultured cells were measured using ELISA kits (BioLegend, CA, USA), following the manufacturer’s instructions. For the ELISA procedure, 96-well plates were coated overnight at 4°C with capture antibodies specific to IL-2 or TNF-γ. The plates were then blocked with 1% BSA in PBS for 1 h at room temperature. After washing, culture supernatant was added to the wells in duplicate, and standard curves were generated using recombinant IL-2 or TNF-γ. Plates were incubated for 2 h, followed by washing and the addition of a secondary HRP-conjugated detection antibody specific for IL-2 or TNF-γ, which was incubated for 1 h. After washing, the substrate solution (TMB) was added and incubated for 10 min in the dark. The reaction was stopped using the stop solution, and absorbance was measured at 450 nm using a microplate reader.

Flow Cytometry Assay

CD8⁺ T cells were collected after different treatments, washed with PBS, and stained with specific fluorochrome-conjugated antibodies to evaluate their functional status. T cell activation was assessed using anti-CD25-PE and anti-CD69-FITC antibodies. Proliferation was determined by CFSE labeling and analyzed by fluorescence dilution. Cytotoxic function was evaluated by intracellular staining with anti–Granzyme B-PC5.5 and anti–IFN-γ-PE antibodies. T cell exhaustion was assessed using anti–PD-1-FITC and anti–TIM3-PE antibodies. Samples were analyzed on a BD FACSCanto II flow cytometer, and data were processed with FlowJo software.

Statistical Analysis

Statistical analysis was performed using R (version 4.3.2) or GraphPad Prism (10.1.2). All experiments were performed at least in triplicates and data are presented as means ± standard deviation. Student’s t-test was used for comparisons between two groups, and one-way ANOVA followed by Tukey’s test was used for multiple comparisons. p<0.05 was considered statistically significant.

Results

Key Roles of CLEC3A and KLHDC7B in Molecular Subtyping of Luminal BC and Their Association with OS

Based on expression profiles from the GEO and TCGA databases, this study conducted DEG screening and clustering analysis for Luminal-type BC. First, 2958 DEGs were identified from the GSE115144 dataset, including 1665 upregulated and 1293 downregulated genes (Figure S1A and B). Then, the expression profiles of the 1665 upregulated genes were extracted from TCGA-BC data, and differential analysis was performed again, resulting in 25 DEGs, including 12 upregulated and 13 downregulated genes (Figure S1C and D).

Based on the 25 DEGs expression data, consensus clustering analysis was applied to classify the samples. The analysis indicated that BC samples could be stably divided into two clusters (C1 and C2), and the consensus matrix showed high stability in clustering results (Figure 1A). Survival analysis indicated that patients in C2 had significantly lower OS than those in C1 (p<0.01, Figure 1B). Additionally, the clustering results revealed two representative genes, CLEC3A and KLHDC7B, with distinct expression patterns across the two clusters. CLEC3A expression was higher in C2 than in C1, whereas KLHDC7B exhibited the opposite expression pattern (Figure 1C). The violin plots further confirmed the opposite expression patterns of CLEC3A and KLHDC7B between the two clusters, with CLEC3A being significantly upregulated and KLHDC7B modestly downregulated in C2 (p<0.001, Figure 1D). These findings suggest that CLEC3A and KLHDC7B may play important roles in luminal BC subtyping. Therefore, CLEC3A and KLHDC7B were selected for further analyses.

Prognostic Implications and Immune Correlation of CLEC3A and KLHDC7B in Luminal BC

Next, the expression characteristics and prognostic significance of CLEC3A and KLHDC7B in BC were further explored. The results showed that both CLEC3A and KLHDC7B were significantly overexpressed in luminal BC compared to normal tissues (p<0.05, Figure 2A and B). Survival analysis indicated that patients with high CLEC3A expression had significantly worse OS compared to those with low expression (p=0.011, Figure 2C), and patients with high KLHDC7B expression exhibited better survival but without significance (p=0.37, Figure 2D). To further evaluate the role of CLEC3A and KLHDC7B in immune regulation, their correlation with the immune checkpoint gene CD274 (PD-L1) was analyzed. The results showed that CLEC3A had no significant correlation with PD-L1 (R²=0.01, p=0.324, Figure 2E), while KLHDC7B was positively correlated with CD274 (R²=0.23, p<0.001, Figure 2F).

Figure 2 Prognostic implications and immune correlation of CLEC3A and KLHDC7B in BC. (A and B) Expression of CLEC3A and KLHDC7B in tumor and normal samples. (C and D) Overall survival of breast cancer patients in different expression levels of CLEC3A and KLHDC7B. (E and F) Correlation of CD274 with CLEC3A and KLHDC7B. ****p<0.0001.

CLEC3A Was Upregulated in Luminal BC Cells and Promoted BC Cell Malignant Features

Subsequently, CLEC3A was selected for further validation because it was consistently upregulated in tumor tissues and the poor-prognosis cluster C2, and its high expression was significantly associated with worse OS. First, the expression differences of CLEC3A in normal and BC tissues and cells were examined. Compared with adjacent noncancerous tissues, the positive expression of CLEC3A was significantly increased in luminal BC tissues (p < 0.001, Figure S2A). Additionally, CLEC3A mRNA expression was significantly increased in the BC cell lines MCF-7 (luminal A) and BT-474 (luminal B) compared to the normal cell line MCF10A (Figure S2B). Therefore, sh-CLEC3A-1 and sh-CLEC3A-2 were transfected into MCF-7 and BT-474 cells to knock down CLEC3A expression. Figure 3A and B shows that compared to the shNC group, CLEC3A mRNA expression was significantly reduced in the sh-CLEC3A-1 and sh-CLEC3A-2 groups, indicating successful transfection. Additionally, cell viability was significantly lower (p<0.05, Figure 3C and D), and apoptosis rate was significantly higher (p<0.01, Figure 3E and F) in the sh-CLEC3A groups compared to the shNC group. CLEC3A knockdown also significantly decreased colony formation, migration, and invasion abilities of MCF-7 and BT-474 cells (p<0.001, Figure 3G–L).

Figure 3 CLEC3A knockdown inhibited malignant behavior of BC cells. (A and B) mRNA expression of CLEC3A in MCF-7 and BT-474 cells.(C and D) Cell viability of MCF-7 and BT-474 cells. (E and F) Apoptosis of MCF-7 and BT-474 cells; scale bar = 100 μm. (G and H) Colony formation of MCF-7 and BT-474 cells. (I and J) Wound healing detected the migration of MCF-7 and BT-474 cells; scale bar = 500 μm. (K and L). Transwell detected the invasion of MCF-7 and BT-474 cells; scale bar = 200 μm. MCF-7 and BT-474 cells were transfected with sh-CLEC3A-1 or sh-CLEC3A-2. *p<0.05, **p<0.01, ***p<0.001.

To further validate the function of CLEC3A, CLEC3A was overexpressed in MCF-7 and BT-474 cells. After transfection with oe-CLEC3A, CLEC3A mRNA expression was successfully upregulated in MCF-7 and BT-474 cells (p<0.001, Figure 4A). Compared to the control group, the oe-CLEC3A group exhibited significantly enhanced cell proliferation, migration, and invasion (p<0.01, Figure 4B–D).

Figure 4 CLEC3A overexpression promoted malignant behavior of BC cells. (A) mRNA expression of CLEC3A in MCF-7 and BT-474 cells. (B) Colony formation of MCF-7 and BT-474 cells. (C) Wound healing detected the migration of MCF-7 and BT-474 cells; scale bar = 500 μm. (D) Transwell detected the invasion of MCF-7 and BT-474 cells; scale bar = 200 μm. MCF-7 and BT-474 cells were transfected with oe-CLEC3A. **p<0.01, ***p<0.001.

CLEC3A Regulated Expression of Cell Proliferation-Related Factors in Luminal BC Cells

Further investigation was conducted into how CLEC3A affects the expression of genes related to cell proliferation. CCND1, E2F1, MYC, and PCNA are associated with high proliferative capacity in cancer cells, while BCL2 is an anti-apoptotic factor and CDKN1A is a cell cycle inhibitor. The results showed that in MCF-7 cells, knockdown of CLEC3A (sh-CLEC3A-1 and sh-CLEC3A-2) significantly reduced the expression of CCND1, BCL2, E2F1, MYC, and PCNA, while markedly increasing the expression of CDKN1A, compared with the shNC control group (p<0.01, Figure 5A). Conversely, in the oe-CLEC3A group, compared to the control group, the expression of CCND1, BCL2, E2F1, MYC, and PCNA was significantly upregulated, while CDKN1A expression was significantly downregulated (p<0.01, Figure 5B). Similar results were observed in BT-474 cells, where CLEC3A knockdown inhibited the expression of CCND1, BCL2, E2F1, MYC, and PCNA, and promoted CDKN1A expression (p<0.05, Figure 5C), while CLEC3A overexpression had the opposite effect (Figure 5D).

Figure 5 CLEC3A regulated expression of cell proliferation-related factors in BC cells. (A) mRNA expression levels of CCND1, BCL2, CDKN1A, E2F1, MYC, and PCNA in MCF-7 cells; MCF-7 cells were transfected with shNC, sh-CLEC3A-1, or sh-CLEC3A-2. (B) mRNA expression levels of CCND1, BCL2, CDKN1A, E2F1, MYC, and PCNA in MCF-7 cells; MCF-7 cells were transfected with oe-CLEC3A. (C) mRNA expression levels of CCND1, BCL2, CDKN1A, E2F1, MYC, and PCNA in BT-474 cells; BT-474cells were transfected with shNC, sh-CLEC3A-1, or sh-CLEC3A-2. (D) mRNA expression levels of CCND1, BCL2, CDKN1A, E2F1, MYC, and PCNA in BT-474 cells; BT-474 cells were transfected with oe-CLEC3A. *p<0.05, **p<0.01, ***p<0.001.

CLEC3A Regulated Expression of Immune-Related Factors in Luminal BC Cells

To explore the role of CLEC3A in the BC immune microenvironment, expression levels of key immune-related molecules (PD-L1, CXCL10, CCL2, IL2, IFNG, and STAT1) were examined. In both MCF-7 and BT-474 cells, CLEC3A knockdown significantly increased the mRNA expression of CXCL10, CCL2, IL2, IFNG, and STAT1, while CLEC3A overexpression suppressed their mRNA expression (Figure 6A–D). Notably, the changes in CLEC3A expression had no impact on PD-L1 mRNA expression, which was consistent with the above-mentioned correlation analysis results.

Figure 6 CLEC3A regulated expression of immune-related factors in BC cells. (A) mRNA expression levels of PD-L1, CXCL10, CCL2, IL2, IFNG, and STAT1 in MCF-7 cells; MCF-7 cells were transfected with shNC, sh-CLEC3A-1, or sh-CLEC3A-1.(B) mRNA expression levels of PD-L1, CXCL10, CCL2, IL2, IFNG, and STAT1 in MCF-7 cells; MCF-7 cells were transfected with oe-CLEC3A. (C) mRNA expression levels of PD-L1, CXCL10, CCL2, IL2, IFNG, and STAT1 in BT-474 cells; BT-474cells were transfected with shNC, sh-CLEC3A-1, or sh-CLEC3A-2. (D) mRNA expression levels of PD-L1, CXCL10, CCL2, IL2, IFNG, and STAT1 in BT-474 cells; BT-474 cells were transfected with oe-CLEC3A. **p<0.01, ***p<0.001, ns: no significance.

CLEC3A Regulated the Stability of PD-L1 Protein in Luminal BC Cells

To verify our hypothesis that CLEC3A may regulate PD-L1 through ubiquitination, we examined the protein levels of PD-L1. As shown in Figure 7A and B, CLEC3A knockdown significantly reduced PD-L1 protein expression, while CLEC3A overexpression increased PD-L1 protein levels (p<0.01). A protein synthesis inhibition experiment showed that CLEC3A knockdown accelerated the degradation rate of PD-L1 in CHX-treated MCF-7 cells, indicating that CLEC3A can affect PD-L1 stability (Figure 7B). Further analysis revealed that in the absence of a proteasome inhibitor (MG132), PD-L1 expression was significantly reduced in the CLEC3A knockdown group, but was significantly restored upon the addition of MG132, suggesting that CLEC3A regulates PD-L1 stability through a proteasome-mediated ubiquitination degradation pathway (Figure 7C). Similar results were observed in BT-474 cells (Figure 7D–F), further supporting CLEC3A’s role in PD-L1 ubiquitination and degradation.

Figure 7 CLEC3A regulated the stability of PD-L1 protein in BC cells. (A–C) Protein expression of PD-L1 in MCF-7 cells; cells in (A) were transfected with sh-CLEC3A-1, sh-CLEC3A-2, or oe-CLEC3A; cells in (B) were transfected with sh-CLEC3A-1 or sh-CLEC3A-2 and treated with protein synthesis inhibitor CHX; cells in (C) were transfected with sh-CLEC3A-1 or sh-CLEC3A-2 and treated with/without proteasome inhibitor MG132. (D–F) Protein expression of PD-L1 in BT-474 cells; cells in (D) were transfected with sh-CLEC3A-1, sh-CLEC3A-2, or oe-CLEC3A; cells in (E) were transfected with sh-CLEC3A-1 or sh-CLEC3A-2 and treated with protein synthesis inhibitor CHX; cells in (F) were transfected with sh-CLEC3A-1 or sh-CLEC3A-2 and treated with/without proteasome inhibitor MG132. *p<0.05, **p<0.01, ***p<0.001.

CLEC3A Affected the Luminal BC Immune Microenvironment by Regulating CD8⁺ T Cell Function

Next, we co-cultured activated human CD8⁺ T cells with MCF-7 or BT-474 cells to further assess the role of CLEC3A in the BC immune microenvironment. Functional assays revealed that inhibition of CLEC3A expression (sh-CLEC3A-1 and sh-CLEC3A-2) significantly enhanced the cytotoxic function of CD8⁺ T cells, evidenced by a significant increase in the proportions of CD8⁺-Perforin⁺ and CD8⁺-NF⁺ T cells (Figures 8A and B, 9A and B). Moreover, CLEC3A knockdown also significantly increased the tumor cell death rate in the co-culture system (Figures 8C and 9C), as well as the secretion levels of IL-2 and TNF-γ (Figures 8D and E, 9D and E). In contrast, CLEC3A overexpression (oe-CLEC3A) significantly suppressed CD8⁺ T cell functions, including a reduced proportion of CD8⁺-Perforin⁺ and CD8⁺-TNF⁺ T cells, as well as decreased tumor cell death rate and cytokine secretion levels (Figures 8A–E and 9A–E). These results suggest that high CLEC3A expression may impair the anti-tumor function of CD8⁺ T cells by inhibiting their cytotoxicity and cytokine secretion, while CLEC3A knockdown significantly enhances the anti-tumor effects of CD8⁺ T cells.

Figure 8 CLEC3A affected tumor immune microenvironment by regulating CD8⁺ T cell function in MCF-7 cells. (A and B) Detection of CD8⁺-Perforin⁺ and CD8⁺-NF⁺ T-cell levels by flow cytometry. (C) Alive cells of MCF-7. (D and E) Levels of IL-2 and TNF-γ. MCF-7 cells were transfected with sh-CLEC3A-1, sh-CLEC3A-2, or oe-CLEC3A. ***p<0.001.

Figure 9 CLEC3A affected tumor immune microenvironment by regulating CD8⁺ T cell function in BT-474 cells. (A and B) Detection of CD8⁺-Perforin⁺ and CD8⁺-NF⁺ T-cell levels by flow cytometry. (C) Alive cells of BT-474. (D and E) Levels of IL-2 and TNF-γ. BT-474 cells were transfected with sh-CLEC3A-1, sh-CLEC3A-2, or oe-CLEC3A. ***p<0.001.

CLEC3A Regulated CD8⁺ T Cell Function via PD-L1

To further elucidate the mechanism by which CLEC3A modulates CD8⁺ T cell function, we examined T cell activation, proliferation, cytotoxicity, and exhaustion in both MCF-7 and BT-474 cells. In MCF-7 cells, CLEC3A overexpression significantly reduced CD8⁺ T cell activation (CD69⁺, CD25⁺) (Figure 10A), proliferation (Figure 10B), and secretion of IFN-γ and Granzyme B (Figure 10C), while markedly increasing the proportion of exhausted PD-1⁺TIM3⁺ T cells (Figure 10D). Conversely, CLEC3A knockdown exerted the opposite effects, enhancing CD8⁺ T cell immune functions. Consistently, similar findings were observed in BT-474 cells: CLEC3A overexpression suppressed T cell activation, proliferation, and effector molecule secretion, whereas CLEC3A knockdown enhanced these functions (Figure 11A–D). Notably, blockade of PD-L1 or supplementation with recombinant PD-L1 could respectively reverse the functional changes induced by CLEC3A overexpression or knockdown, respectively (Figures 10A–D and 11A–D). These results suggest that CLEC3A may regulate the functional states of CD8 T cells through PD-L1, thereby promoting tumor immune evasion in luminal BC.

Figure 10 CLEC3A regulates CD8⁺ T cell function via PD-L1 in MCF-7 cells. (A) Flow cytometry analysis and quantification of CD8⁺ T cell activation (CD69⁺/CD25⁺) in different groups. (B) Analysis and quantification of CD8⁺ T cell proliferation (CFSE dilution). (C) Flow cytometry analysis and quantification of IFN-γ and Granzyme B production by CD8⁺ T cells. (D) Flow cytometry analysis and quantification of exhausted PD-1⁺TIM3⁺ T cell proportions. oe-CLEC3A-transfected MCF-7 cells were treated with 10 μg/mL anti-PD-L1 antibodies, and sh-CLEC3A-transfected MCF-7 cells were treated with 5 μg/mL recombinant PD-L1 protein. **p<0.01, ***p<0.001.

Figure 11 CLEC3A regulates CD8⁺ T cell function via PD-L1 in BT-474 cells. (A) Flow cytometry analysis and quantification of CD8⁺ T cell activation (CD69⁺/CD25⁺) in different groups. (B) Analysis and quantification of CD8⁺ T cell proliferation (CFSE dilution). (C) Flow cytometry analysis and quantification of IFN-γ and Granzyme B production by CD8⁺ T cells. (D) Flow cytometry analysis and quantification of exhausted PD-1⁺TIM3⁺ T cell proportions. oe-CLEC3A-transfected BT-474 cells were treated with 10 μg/mL anti-PD-L1 antibodies, and sh-CLEC3A-transfected BT-474 cells were treated with 5 μg/mL recombinant PD-L1 protein. **p<0.01, ***p<0.001.

Discussion

In this study, we focused on the role of CLEC3A in luminal BC and its potential effects on prognosis, immune regulation, and tumor progression. Our results suggest that CLEC3A may be an important regulatory factor in BC, influencing cell proliferation, immune responses, and the stability of PD-L1, thereby shaping the tumor immune microenvironment.

By using bioinformatics analysis on the GEO and TCGA datasets, we clustered luminal BC patients into two subtypes, C1 and C2, with CLEC3A being a key gene in this classification. CLEC3A was first reported to be expressed in cartilage and later found to be a biomarker in various tumors, such as endometrial cancer,¹⁸ pancreatic neuroendocrine tumors,¹⁹ and lung cancer.²⁰ Several studies have reported the role of CLEC3A in the prognosis of BC. Chen et al identified CLEC3A as a risk gene in cuproptosis-related prognostic genes.¹⁴ Another study indicated that low expression of CLEC3A was associated with improved OS in BC.¹⁷ In our study, CLEC3A was upregulated in the C2 subtype, which had a worse prognosis. Survival analysis also showed that high CLEC3A expression was significantly associated with worsened OS in luminal BC patients, supporting its potential as a prognostic biomarker. To our knowledge, only Ni et al¹⁶ have reported that CLEC3A promotes the proliferation, migration, and invasion of BC cells. In our study, CLEC3A expression was significantly upregulated in MCF-7 (luminal A) and BT-474 (luminal B) cells compared to normal cells. CLEC3A knockout significantly reduced cell viability, induced apoptosis, and decreased colony formation, migration, and invasion. Overexpression of CLEC3A enhanced the malignant behavior of MCF-7 and BT-474 cells. These results further confirm the findings of Ni et al,¹⁶ supporting CLEC3A as a predictive biomarker for luminal BC progression. Zhang et al found that CLEC3A overexpression reduced G1 phase arrest in osteosarcoma cells.²¹ Cell cycle dysregulation, characterized by the inactivation of tumor suppressors and abnormal activation of cyclins and cyclin-dependent kinases, is a hallmark of BC.²² Our findings show that CLEC3A knockout leads to significant downregulation of cell proliferation markers such as CCND1, MYC, E2F1, and PCNA, and promotes the expression of CDKN1A, while CLEC3A overexpression produces the opposite effect. These results suggest that CLEC3A may regulate cell cycle checkpoints while promoting cell proliferation and potentially plays a carcinogenic role in luminal BC.

The prognosis of BC is not only related to biological characteristics but also to the TME. In luminal BC, a high proportion of infiltrating immune cells predicts a poorer prognosis, suggesting that effective immune escape may be an important factor influencing luminal BC recurrence.²³ CLEC3A is an immune-related gene in lung squamous cell carcinoma²⁰ and is also a core gene associated with immune cells in BC.¹⁷ Our investigation into the immune-related effects of CLEC3A shows that it can influence key immune molecules in the BC tumor microenvironment. Specifically, CLEC3A knockout leads to an increase in the expression of immune-related factors, including CXCL10, CCL2, IL2, IFNG, and STAT1, while CLEC3A overexpression inhibits the expression of these factors. These molecules are known to play crucial roles in promoting T-cell recruitment, activation, and anti-tumor immunity. CXCL10-positive BC shows higher CD8⁺ immune cell infiltration.²⁴ In luminal BC, CCL2 is associated with the infiltration of tumor-associated macrophages, and increased CCL2 release enhances angiogenesis within macrophages.²⁵ IL-2 stimulates anti-cancer immunity and is one of the earliest cytokines used in cancer therapy.²⁶ IFNG is a crucial helper for CD8⁺ T cell cytotoxicity and can edit the BC microenvironment to promote stemness, disease progression, and resistance to immunotherapy.²⁷ STAT1 drives immune surveillance in BC.²⁸ We hypothesize that CLEC3A may participate in regulating the immune landscape of BC by inhibiting the activation of anti-tumor immunity.

The efficacy and safety of immunotherapy in luminal BC have been tested in several ongoing clinical trials.^10,29 As maintenance therapy, anti-PD-L1 antibodies have been shown to significantly improve OS in luminal BC compared to chemotherapy.³⁰ This study found no significant correlation between CLEC3A and the PD-L1 encoding gene CD274, and changes in CLEC3A expression did not significantly affect PD-L1 mRNA levels. However, CLEC3A knockout significantly reduced PD-L1 protein expression, and proteasome inhibition restored PD-L1 levels. This suggests that CLEC3A may regulate the stability of PD-L1 protein in BC cells via the ubiquitination pathway, thereby affecting immune escape mechanisms. Previous studies have shown that ubiquitination enhances the immunosuppressive activity of PD-L1, thereby weakening the immune evasion ability of tumor cells.³¹ In our co-culture experiments, CLEC3A knockout significantly enhanced the cytotoxic activity of CD8⁺ T cells, as evidenced by increased proportions of CD8⁺-Perforin⁺ and CD8⁺-TNF⁺ T cells, along with elevated secretion of cytokines such as IL-2 and TNF-γ. In contrast, CLEC3A overexpression significantly suppressed CD8⁺ T cell function, indicating that high levels of CLEC3A may inhibit anti-tumor immune responses. Importantly, our results demonstrated that CLEC3A-driven PD-L1 stabilization directly affected CD8⁺ T cell functional states. CLEC3A overexpression reduced CD8⁺ T cell activation, proliferation, and cytotoxic cytokine secretion, while these effects could be reversed by PD-L1 blockade. This finding aligns with a recent cross-sectional study, which reported that approximately 33% of BC patients exhibited high PD-L1 expression and low CD8⁺ T cell counts.³² Taken together, these results highlight a novel mechanism whereby CLEC3A promotes luminal BC progression by regulating PD-L1 to suppress CD8⁺ T cell–mediated anti-tumor immunity.

This study has certain limitations. First, the functional validation of CLEC3A was mainly performed in vitro using cell-based assays, without further verification in animal models. Although clinical luminal BC tissues supported the high expression of CLEC3A, the absence of in vivo experiments partially limits the translational relevance of our findings. Future studies incorporating animal models will be essential to confirm the role of CLEC3A in luminal BC progression and to provide more robust evidence for its potential as a prognostic biomarker and therapeutic target.

Conclusion

In conclusion, our study demonstrates that CLEC3A is associated with poor prognosis in luminal BC. CLEC3A promotes the malignant characteristics of BC cells by regulating cell proliferation and the immune microenvironment. Furthermore, CLEC3A affects PD-L1 stability through ubiquitination, contributing to immune escape. These findings suggest that CLEC3A may be a potential therapeutic target for improving anti-tumor immune responses in luminal BC.

Data Sharing Statement

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

Ethics Approval

The Ethics Committee of The First Affiliated Hospital of Bengbu Medical University deemed that this research is based on open-source data, so the need for ethics approval was waived.

Funding

This work was supported by the following grants: Key Projects of Natural Science Research in Higher Education Institutions in Anhui Province (No. KJ2021A0815); Open Subjects of Scientific Research Platform of Anhui Biochemical Engineering Centre [No. 2023SYKFZ06]; Natural Science Key Projects of Bengbu Medical College (No. 2021byzd121).

Disclosure

The authors report no conflict of interest.

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