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January 19, 2026

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January 19, 2026

CXCL16 Promotes the Development of Chronic Atrophic Gastritis by Regul

Introduction

Chronic Atrophic Gastritis (CAG) is a chronic inflammatory disease characterised by gastric mucosal epithelium degradation, resulting in the reduction or absence of gastric mucosal glands, with or without intestinal epithelial or pyloric glandular metaplasia.¹ Recurrent or chronic inflammation has been linked with the onset and progression of various human cancers. In this regard, it is noteworthy that CAG is often the first step in gastric mucosal changes that could progress to irreversible gastric carcinogenesis.² Although immune-mediated inflammation has been significantly implicated in CAG’s complex pathogenesis, the precise mechanisms remain unclear. Complex interactions between immune cells and gastric mucosal epithelial cells could promote chronic inflammation in the gastric microenvironment. Notably, a complex network of signalling molecules, involving cytokines and chemokines, which facilitate immune cell recruitment and activation, thus influencing inflammation at local tissue sites, was reported to mediate the aforementioned dynamic interaction. Macrophages, as part of the immune system, help remove pathogens and damaged cells under normal circumstances, but can also, depending on the surrounding environment, become overactive and attack healthy tissues, leading to inflammation and damage.³ Owing to their remarkable plasticity, macrophages could polarise into classically activated (M1) or bypass-activated (M2) phenotypes in response to different factors and microenvironments. Notably, M1 macrophages are highly expressive of pro-inflammatory factors such as CD86, Major Histocompatibility Complex-II (MHC-II), and Inducible Nitric Oxide Synthase (iNOS) in response to Lipopolysaccharide (LPS), Interferon-gamma (IFN-γ), and TNF-α stimulation—a process mediated through the activation of pathways such as TLR- and NF-κB. These pro-inflammatory factors could exert tumour-suppressive and pro-inflammatory effects.⁴ Conversely, M2 macrophages, after treatment with TGF⁃β, IL-10, and Th2 cytokines (IL-4, IL-13), could exert anti-inflammatory effects and tissue repair functions—a process mediated through pathways such as JAK-STAT6 and PI3K-AKT.⁵ Following inflammation onset, numerous macrophages often infiltrate the gastric mucosal tissues in CAG;⁶ hence, CAG progression could be attributed to macrophage infiltration and polarisation. Moreover, macrophage conversion from the M1 to M2 phenotype could result in the upregulation of Transforming Growth Factor β (TGF-β) and Interleukin-10 (IL-10), thus alleviating inflammation.⁷ Therefore, proper macrophage phenotype modulation could precisely regulate the tissue and intracellular microenvironment, presenting a promising therapeutic strategy for CAG. Exploring this hypothesis could enhance our understanding of the pharmacological effects and potential mechanisms of CAG from a macrophage polarisation perspective.

The human CXC Chemokine Ligand 16 (CXCL16), a member of the chemokine family, is primarily expressed in monocytes, macrophages, dendritic cells, and endothelial cells, among other immune cells. Following inflammation occurrence, CXCL16, a vital inflammation transmitter, promotes immune cell chemotaxis to the inflammation site, facilitating inflammatory factor phagocytosis and release—a phenomenon that further aggravates the inflammatory response.^8,9 According to research, CXCL16 overexpression in the gastric mucosa could induce local CD8⁺ T lymphocyte infiltration, weakening the body’s defence function and ultimately promoting CAG development.¹⁰ Furthermore, CXCL16 could promote CXCL16/CXCR6 axis activation, exacerbating the gastric mucosa’s inflammatory response, ultimately disrupting local immune homeostasis and increasing the risk of gastric carcinogenesis.¹¹ Despite CXCL16 playing a key role in gastric mucosal injury, its precise mechanisms, especially in modulating the dysregulation of CAG immune response, remain to be elucidated. In addition to inflammatory responses, CXCL16 might also regulate macrophage migration, accelerating inflammatory mediator release and amplifying local and systemic inflammatory responses.¹² Based on these insights, we sought to identify the potential targets for CAG treatment from a macrophage polarisation perspective.

Presently, CAG treatment is primarily based on acid-suppressing drugs. Nonetheless, due to the heterogeneity and resistance to acid-suppressing drugs in CAG patients, these standardised therapeutic approaches may not be universally applicable. Therefore, developing alternative therapeutic strategies and targets would be imperative for improved clinical outcomes across different patient groups. In this study, in order to preliminarily investigate the mechanism of the interaction between CAG progression and macrophage polarization and to find out whether there is some kind of cytokine in CAG that acts on macrophages to promote their polarization or can attract the aggregation of M1-associated macrophages.we leverage a comprehensive analytical approach to identify potential biomarkers and elucidate the immune-mediated mechanisms in CAG. To establish whether CXCL16 regulates macrophage polarisation, we initially employed a comprehensive analytical approach to elucidate the immune-related biological functions of CAG and to identify potential biomarkers. Subsequently, data analysis utilizing the database revealed significant differences in the expression levels of CD86, CD163, and CXCL16 between the CAG and CNAG groups. Verification through multiplex immunohistochemistry (mIHC) technology demonstrated that M1 macrophages accumulate abundantly within the inflammatory microenvironment. Notably, CD86 and CXCL16 expression levels were significantly elevated in CAG patients, while CD163 protein was upregulated in CNAG patients; however, CXCL16 exhibited reduced expression in both CNAG and CAG-E patients. Further co-localization and correlation analyses indicated that CXCL16 is predominantly co-expressed with the M1 macrophage marker CD86 in CAG patients, suggesting its role in regulating M1 macrophage polarization. Additionally, in vitro experiments showed that stimulation with varying concentrations of CXCL16 resulted in a significant increase in CD86 mRNA expression alongside a marked decrease in CD163 mRNA expression. This further corroborates that CXCL16 can promote macrophage polarization towards an M1 phenotype. These findings provide novel insights and evidence for understanding the pathology of CAG as well as potential targeted therapeutic strategies.

Materials

Access to Public Databases

Based on the CAG disease type and human species, the gene expression datasets GSE153224 and GSE27411, containing CAG and Chronic Non-Atrophic Gastritis (CNAG) whole blood samples were collected from the US Centre for Biotechnology Information’s Gene Expression Omnibus (GEO) database (https://www.ncbi. nlm.nih.gov/geo/). Immune Genes (IGs) were also downloaded from the Import database (https://www.immport.org/).

Clinical Sample Collection and Patient Screening

This study included 20 cases each of gastric tissue wax blocks mainly from the gastric sinus area, extracted from patients with CNAG, CAG and Chronic atrophic gastritis with erosion (CAG-E) diagnosed via endoscopy and histopathological examination at Gansu Provincial Hospital’s Department of Gastroenterology from October 2023 to September 2024. The enrolled patients were predominantly aged 18–65 years. Diagnosis adhered to the (1) endoscopic diagnostic criteria outlined in the Chinese Guidelines for the Diagnosis and Treatment of Chronic Gastritis (2022) and (2) the pathohistological diagnostic criteria outlined in the Consensus on Pathological Diagnosis of Gastric Mucosal Biopsy for Chronic Gastritis and Epithelial Tumours (2017). Compliance procedures were informed by the ethical standards set by the Committee in Charge of Human Trials.

The inclusion criteria were: (1) Patients aged 18–65 years who met the diagnostic criteria and whose diagnosis was confirmed via gastroscopy and pathological examination; and (2) Patients with complete clinical history data who signed the informed consent form. On the other hand, the exclusion criteria were: (1) Patients who did not meet the inclusion criteria; (2) Patients who were treated with proton pump inhibitors, Non-Steroidal Anti-Inflammatory Drugs (NSAIDs), Traditional Chinese Medicine (TCM) herbs, antiplatelet drugs, and anticoagulants in the last one month; (3) Patients with a combination of severe cardiac, cerebral, renal, and pulmonary comorbidities, psychiatric disorders, or those who were pregnant/lactating women; (3) Patients who presented with reflux oesophagitis, peptic ulceration, polyp, and hypertrophic gastritis in the last month of endoscopic examination, Gastric Cancer (GC), and other malignancies intervened with surgery, radiotherapy, or chemotherapy within the last 5 years; and (4) Patients with autoimmune atrophic gastritis diagnosed using the anti-mural cell antibody test.

Reagents and materials

The key laboratory instruments and equipment included: An orthostatic fluorescence photomicrograph microscope (Nikon-eclipse ti2, Japan); an inverted white light/fluorescence photomicrograph microscope (Olympus, Japan); a water purifier, digital pendulum shaker, vortex mixer, and magnetic stirrer (ServiceBio, China); an electrothermal incubator (Shanghai Yiheng, China); a palm centrifuge (Scilogex, USA); a benchtop centrifuge (Shanghai Anting Scientific Instrument Factory, China); an ice maker (Changshu Xueke Electrical Appliance Co., China); refrigerators (4°C and −20°C; XINGX, China); pipettes (100–1000 μL, 20–200 μL, 10–100 μL, 0.5–10 μL, 0.1–2.5 μL; Eppendorf, German); a decolourisation shaker (Beijing Liuyi Instrument Factory, China); a PCR instrument (Hangzhou Bori Technology, China), and a fluorescence quantitative PCR instrument (Life technologies, USA).

The other laboratory reagents and consumables included: Triton X-100 and Bovine Serum Albumin (BSA; Beijing Solebo Technology Co., Ltd., China); an anti-fluorescence quenching sealer (SouthernBiotech, USA); pipette tips (1000 μL, 200 μL, and 10 μL), NaCl, Na2HPO4-12H2O, NaH2PO4-2H2O, disodium EDTA, NaOH, citrate buffer, xylene, anhydrous ethanol, Paraformaldehyde (PFA), Hydrochloric acid (HCl), glycerol, trichloromethane, and isopropanol (Sinopharm Chemical Reagent Co., Ltd., China); Phosphate Buffered Saline (PBS) solution, TSA-480, 570, 520, and 670, group paintbrush, coverslips, slides, and citric acid restoration solution 20* (Wuhan Snowpigeon Biotech Co., Ltd., China); DAPI staining solution and Tris base (Sigma, USA); Horseradish Peroxidase (HRP)-goat anti-rabbit IgG and HRP-goat anti-mouse IgG antibodies (KPL, USA); triple pure total RNA extraction reagent, EntiLink™ first strand cDNA synthesis kit, and EnTurbo™ SYBR Green PCR SuperMix kit (KPL, China).

Methods

Bioinformatics Analysis methods

Differentially Expressed Genes (DEGs) Analysis Using the GEO Database

DEGs from the 2 datasets with CAG samples were analysed using the online database GEO2R (https://wwwncb.i.nlm.nih.gov/geo/geo2r/) and volcano plots generated for visualisation. The conditions for screening were adj.P.Val<0.05 and an absolute value of the multiplicity of differences > 1 (|log2FC|>1).¹³ The Pheatmap package was used to generate differential gene heatmap displays for the top 15 genes. The online intersection analysis software (http://bioinformatics.psb.ugent.be/webtools/Venn/) venn graph was used to find the DEGs that are common to the above GEO data chips.

Acquisition of CAG-Related Immune Genes and Their Enrichment Analysis

CAG-associated immune genes were obtained by screening co-expressed genes between co-DEGs and Immune Genes (IGs) using Venn diagrams. These genes were then subjected to Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the DAVID (https://david.ncifcrf.gov/home.jsp) online database to identify related functions and pathways. Specifically, KEGG pathway enrichment analysis was performed to identify the biological functions in which these IGs were involved, while GO analysis examined the key Biological Processes (BP), Cell Components (CCs), and Molecular Functions (MFs). Finally, the top-ranked results were visualised and analysed using a P-value Cutoff of 0.05 and a Q-value Cutoff of 0.05.

Target Core Proteins Screening via Protein-Protein Interaction (PPI) Network Analysis

The obtained proteins encoding CAG-associated immunity genes were introduced into STRING11.5 (https:// cn.string-db.org/) for visualisation, with the species set to “Homo sapiens”. A PPI network was then constructed and visualised. The results were further imported into Cytoscape 3.10.3 software in the xlsx format to construct a PPI network diagram. Topology analysis was performed using the cytoHubba plug-in of Cytoscape 3.10.3 based on the betweenness calculation method to filter out key proteins that could influence disease pathogenesis. To evaluate the target core proteins that most closely correlated with CAG, the key proteins were imported into Cytoscape again for interaction analysis.

Analyzing Differential Expression of Target Target Factors and Their Correlation Using Data From Databases

First, GSE153224-normalised microarray datasets were downloaded and screened for the expression of the target macrophage markers CD86 and CD163, as well as the impact factor CXCL16, across different gastritis tissues, respectively. Comparisons and Pearson’s correlation analyses were carried out by using GraphPad Prism 10.4 software, and the results were visualized.

Experimental Validation

Multiple Fluorescent Immunohistochemical Staining (the mIHC-Tetrachromatic Method)

The differential expressions of CD86, CD163, and CXCL16 proteins in CNAG, CAG, and CAG-E patients were verified using mIHC. Briefly, paraffin-embedded tissues were sectioned and baked in a 60°C oven for 1 h. They were then deparaffinized in xylene and hydrated in gradient alcohol (xylene I for 15 min, xylene II for 15 min, 100% anhydrous ethanol I for 5 min, 100% anhydrous ethanol II for 5 min, 95% ethanol I for 5 min, 85% ethanol for 5 min, and 75% ethanol for 5 min). After rinsing in tap water for 10 min, the sections were soaked in distilled water for 5 min. They were then subjected to high-temperature and high-pressure antigen repair in an autoclave. An appropriate amount of 0.01 M sodium citrate buffer (pH 6.0) repair solution was first added before turning on the autoclave and timing the heating to the point of jetting for 2 min, with the sections ultimately allowed to cool naturally after the thermal repair was completed. After rinsing in running tap water for 5 min, the sections were immersed in distilled water for 5 min and then placed in 3% H2O2 solution to seal the endogenous peroxidase. The sections were then incubated at Room Temperature (RT) for 20 min. After washing with distilled water, the non-specific binding site was sealed with 5% BSA before incubating at RT for an additional 30 min. Following that, the sealing solution was discarded, and the unwashed sections were incubated with antibody dilutions based on predetermined optimal concentrations. Specifically, the sections were subjected to primary antibody incubation at 37°C for 2 h, followed by a PBST-prepared, HRP enzyme-labelled secondary antibody incubation at RT for 1 h after washing three times in PBS (for 5 min each time). After washing away the secondary antibody with PBST three times (for 5 min each time), the sections were incubated with a Tyramine Signal Amplification (TSA) dye—added dropwise—at 37°C for 30 min. The tissues were then washed three times with PBS (for 5 min) and subjected to secondary antigenic repair (repeat the above steps without adding 3% hydrogen peroxide dropwise). After sealing, a second antibody was added, and the steps were repeated until the third antibody staining was completed. Finally, DAPI staining of nuclei was performed. Specifically, the DAPI working solution was added dropwise and incubated at RT for 5 min. The tissues were then washed with PBS three times (for 5 min each time). Subsequently, the liquid was shaken dry before adding an anti-fluorescence sealer dropwise and sealing with cover slips. The complete fluorescent sheets were stored at 4°C in the dark. Table 1 lists the antibodies and Table 2 shows the fluorescein information.

Table 1 Antibodies Used

Table 2 Fluorescein Information

Image Acquisition and Outcome Evaluation of mIHC

Stained images were first captured using a Leica DMi8 microscope equipped with high-efficiency fluorescent dye-specific filters for DAPI, FITC, Cy3, and Cy5. Scans of the Immunofluorescence (IF) multilabel were then examined for the number of positive cells, positive density, and co-localisation using the Indica Labs (U.S.A) digital image analysis software (HALO V2.0). Briefly, two pathologists blinded to the patient’s condition annotated the inflammatory borders. The software then circled the area to be assessed along the tissue to be tested, selecting either the number or the area of positives for the analysis module. Subsequently, fluorescent signals for each channel destination were selected manually and identified using the software across several iterations to ensure all positive signals were selected, while saving the initial colour selection criterion. The same colour selection criteria were applied to similar indexes within the same batch of sections. Following that, the software identified and located all nuclei with DAPI blue fluorescence and extended the cytoplasmic range, calculating different parameters such as the number of positive cells, the positive area, the positive intensity (grey value of fluorescence signals), and so on. Other parameters, such as the ratio of positive cells, the density of positive cells, the intensity of positive cells, and so on, were also calculated. Relevant parameters were then analysed through co-localisation to evaluate the strength of positivity. The area to be measured was calculated step by step under high magnification. The results of the analysis were then exported per predetermined requirements, and a report was generated. In cases where the positive cell ratio equalled the number of positive cells/total number of cells,^8,14 the percentage was calculated with the number of all nucleated cells (DAPI+) as the denominator. For statistical analysis, the following distinctions were made based on the ratio of positive cells in the sections: Non-expression group (no positive cell staining); low expression group (1–40% positive cell staining); and high expression group (>40% positive cell staining). Positive cell density, which evaluates the distribution and number of certain types of positive cells in the tissue, was calculated as follows: Positive cell density = number of positive cells/areas of tissue to be tested.¹⁵ This parameter was appropriately combined with mIHC staining intensity to assess the expression of each index in the tissue. On the other hand, positive intensity (staining intensity), which reflects the average depth of the positive signal, was evaluated based on the depth of positivity, with larger values indicating a greater brightness of the fluorescent signal. Notably, this parameter is particularly suitable when the positivity is patchy and widely expressed.^16,17 Herein, the positive intensity scoring criteria were as follows: Non-expression group (number of positive cells <10%), low expression group (10~40% positivity), and high expression group (>40% positivity).

Cell Culture

The human myeloid leukemia mononuclear cell line THP-1 utilized in this study was obtained from Saibaikang Biotechnology Co. Ltd. The cell line has the research resource identifier (RRID) CVCL_0006, as recorded in the Cellosaurus database. The use of this commercially available cell line was approved by the Ethics Committee of Gansu Provincial Hospital (Approval number: 2025–410). The cells were cultured in a Lymphocyte Medium (LM) supplemented with 10% Fetal Bovine Serum (FBS), 1% Penicillin-Streptomycin (P-S), and RPMI 164 basal medium at 37°C and 5% CO2.

Cellular IF Staining

First, THP-1 cells were cultured in LM containing 160 nmol/L phorbol ester (phorbol 12-myristate 13-acetate, PMA) for 24 h to induce their differentiation into macrophages. After discarding the old medium, the cells were washed three times (for 5 min each) with 200 μL PBS. The cells were then fixed in 4% PFA for 20 min and washed 3 times (for 5 min each) with PBS. To prevent the incubation solution from draining away in the later stages, circles were drawn with a histochemical pen. The cells were then sealed with 5% BSA to reduce non-specific staining. Following that, the cells were subjected to primary antibody incubation at 4°C overnight with mouse anti-CXCL16 (1:200) and rabbit anti-CXCR6 (1:200) polyclonal antibodies, followed by secondary antibody incubation with CoraLite488 and FITC-labelled secondary antibodies (Table 1) added dropwise at 37°C for 40 min in a water bath in the dark. The cells were then washed with PBS three times (for 5 min each time). Subsequently, the nuclei of the cells were restrained with DAPI in the dark at RT for 20 min, washed with anti-BSA, and sealed with an anti-quenching sealer for 20 min. Finally, the cells were observed under a fluorescence microscope, with the fluorescence images of CXCL16 and CXCR6 captured.

RT-qPCR Detection of the CD86 and CD163 mRNA Expression Levels in Macrophages

After inoculation into 6-well plates at a density of 1×10⁵ cells/well, the induced macrophages were cultivated in a cell culture incubator for 24 h. After wall attachment, the original medium was aspirated before washing the adherent cells twice with 200 µL PBS solution. Subsequently, different concentrations of the CXCL16 factor (0, 50, and 100 μg/mL, respectively) were added to stimulate the macrophages and incubated for an additional 48 h. Cells were collected from each group, and total RNA was extracted using TRIzol (Invitrogen, Carlsbad, USA) per the manufacturer’s instructions. Following that, 1µg of mRNA from each sample was reverse-transcribed into complementary DNA (cDNA) using the EntiLink™ 1st Strand cDNA Synthesis Kit. The reverse transcription products were then collected in the StepOne™ 1st Strand cDNA Synthesis Kit and subjected to Polymerase Chain Reaction (PCR) on a StepOne™ Real-Time PCR instrument under the following conditions: Pre-denaturation at 95°C for 3 min, 40 cycles (95°C 10s→58°C 30s→72°C 30s). The mRNA content was calculated using the 2^−ΔΔCT method, and GAPDH was used as the internal reference gene. Table 3 shows the primer sequences used.

Table 3 Primer Sequences

Statistical methods

The CAG transcriptomics results from the GEO database were mapped and analysed using the R4.0 software package. Meanwhile, the datas were analysed and graphed using SPSS 26.0 and GraphPad Prism 10.4 software. Statistical values of the samples from each group were assessed for normality using the Shapira-Wilkinson test, with P>0.05 indicating normal distribution. Metrological data between two groups were compared using the t-test, while those between multiple groups with the same variance were compared using one-way Analysis of Variance (ANOVA). Pairwise comparisons were performed using Bonferroni’s method. Pearson’s correlation analysis was used to analyse data on the relationship between macrophage-associated markers (CD86, CD163) and the chemokine CXCL16 obtained from mIHC. All tests were two-sided, and results with P<0.05 were considered statistically significant.

Results

Bioinformatics Analysis results

DEGs Identification

Combining the data from the 2 GEO database microarray sequences, we first analyzed the differential expression of genes in CAG using the GEO2R analysis system.It was found that 1096 genes were significantly up-regulated and 714 genes were significantly down-regulated in the GSE153224 dataset compared with the control CNAG. 220 genes were significantly up-regulated and 386 genes were down-regulated in the GSE27411 dataset (Figure 1A). Heatmaps of the top 15 DGEs of these two datasets were also shown separately (Figure 1B). And the intersection of the 2 data sets was taken to find the shared differential genes, and finally 239 shared DGEs were obtained (Figure 1C).

Figure 1 Differentially Expressed Genes Screening (A) Volcano plot of DEGs; (B) Heatmap of DEGs; (C) Venn diagram of 239 co-DEGs.

Screening and Biological Functions of CAG-Related Immune Genes

The intersection of differential genes with 459 IGs using Venn plots yielded 24 common IRGs in CAG (Figure 2). These genes were then subjected to GO annotation and KEGG enrichment analyses using the DAVID database to gain a deeper understanding of their biological roles. According to the GO-BP analysis results, these genes were involved in the positive regulation of BPs such as cell migration, signal transduction, cell chemotaxis, and immune responses (Figure 3A). On the other hand, GO-CC analysis revealed significant enrichment mainly in extracellular regions and the extracellular space (Figure 3B). Finally, the MF mainly included chemokine, growth factor, cytokine, and receptor ligand activities (Figure 3C). Additionally, cytokine-cytokine receptor interactions and chemokine signalling pathways were detected in KEGG enrichment analysis (Figure 3D). These findings collectively suggest that intrinsic immunity, adaptive immunomodulation, and chemokine activity are crucially involved in CAG pathogenesis—a phenomenon that aligns with the basic pathological features of macrophages and chemokines.

Figure 2 CAG immunity-related genes.

Figure 3 Functional and Pathway Enrichment Analysis Results for CAG-Related Immune Genes (A) Biological processes analysed by GO; (B) Cellular components analysed by GO; (C) Molecular functions analysed by GO; (D) KEGG analysis.

PPI Network Analysis and Screening of Key Markers

The 24 common IGs were subjected to protein interaction analysis and visualisation using the STRING database (Figure 4A), with CXCL16, CX3CL1, IL1RN, CD86, CD163, CCL28, and CCL15 emerging as the top seven key proteins. Since CD86 and CD163 are markers of different macrophage phenotypes, they were re-analysed in terms of interactions, revealing a close association between them and other proteins in CAG (Figure 4B). However, these proteins exhibited significant tissue specificity, among which CX3CL1 is distributed in dorsal root ganglia and spinal cord neurons, which are mainly involved in neurological disorders.¹⁸ IL1RN is highly expressed in cancer-related diseases, such as breast and gastric cancers,¹⁹ and very few literature reports on the correlation between IL1RN and the risk of CAG.CCL28 is mainly distributed in the mammary glands, small bowel, and colon, among others. CCL15 is expressed in the intestine and liver, and they have also been found to be associated with a variety of cancers.²⁰ While CXCL16 being highly expressed in antigen-presenting cells [macrophages and Dendritic Cells (DCs)] as a chemotactic agent for monocytes and macrophages, and correlating with inflammatory regulation. Nobly, this phenomenon aligns with our study goal. Consequently, we considered CD86 and CD163 as key markers for different macrophage phenotypes, and CXCL16 as a candidate for regulating macrophage polarisation in CAG.

Figure 4 PPI analysis network diagram (A) CAG-Related Immune Gene PPI Network Diagrams; (B) Interactions between the seven key proteins.

Analyzing Differential Expression and Correlation of Candidate Markers Using Database Information

Based on the datas screened in the GEO database, we first examined the expression of macrophage-related markers CD86 and CD163, as well as that of their potential influencing factor CXCL16 in the CNAG and CAG groups (Figure 5A). According to the results, the CAG group exhibited a higher expression of the M1 macrophage marker CD86 and chemokine CXCL16 than the CNAG group (P = 0.017 and 0.001, respectively). Conversely, the CNAG group showed a higher expression of the M2 macrophage marker CD163 than the CAG group (P=0.011). To further explore the effect of CXCL16 on macrophage polarisation in CAG, we analysed the correlations among CD86, CD163, and CXCL16 expressions in CAG (Figure 5B). According to the results, the M1 macrophage marker CD86 correlated strongly with the chemokine CXCL16 in CAG (0.8≤|r|<1, P<0.05). Conversely, the correlation of CD163 expression with CXCL16 was not statistically significant (P>0.05). These findings collectively suggest that CXCL16 and CD86 could play a mutually synergistic role in CAG, with CXCL16 potentially influencing M1 macrophage polarisation.

Figure 5 Bioinformatics analysis of the expression and correlation of macrophage markers with CXCL16 (A) The differential expression of CD86, CD163 and CXCL16 in CAG and CNAG tissues; (B) Correlation analysis for the association of CD86 with CXCL16 in CAG and CD163. * indicates P<0.05, ** indicates P<0.01, P<0.05 is statistically significant.

Experimental Validation Results

Multiple Fluorescence Immunohistochemistry Analysis

Establishment of Multiple Fluorescence Immunostaining methods

The CD86, CD163, and CXCL16 monoclonal antibodies were detected in gastric tissue sections, with clear blue, red, green, and yellow fluorescence observed in local magnification images. Furthermore, although CXCL16 exhibited a lower expression compared to CD86 and CD163, all of them were predominantly expressed in the cell membranes (Figure 6).

Figure 6 Multiplex fluorescent immunohistochemical staining was used to detect the expression and localization of CD86, CD163, and CXCL16 molecules in gastric tissue.Fluorescent labelling was examined by CaseViewer 2.4 scanning immunofluorescence microarray at 40x field of view, DAPI blue, CD86 red, CD163 green, CXCL16 yellow, scale bar 20μm.

Expression and Distribution of Macrophage-Related Markers and CXCL16 in Different Pathological Stages of Gastritis

To validate the differential expression of the two macrophage-related markers and CXCL16, we stained 60 gastritis tissue samples with mIHC and performed cell notation under high magnification. The number of CD86+, CD163+ and CXCL16+ macrophages across different cell types was analysed semi-quantitatively, with the ratio of positive cells calculated and statistically analysed. The results of mIHC staining and semi-quantitative analysis revealed that M1(CD86+) macrophages exhibited a higher expression in gastric tissues of CAG and CAG-E patients compared to CNAG patients. Furthermore, CD86 was sparsely expressed in gastritis tissues, exhibiting a step-wise upregulation with inflammation progression (both P<0.05). Conversely, the expression of CD163+ cells was lower in CAG and CAG-E patients than in CNAG patients (P<0.05). Finally, although CXCL16+ cells were up-regulated in the CAG stage, they were largely absent in CNAG and CAG-E patients (P<0.001) (Figures 7A and B).

Figure 7 mIHC technology validates the expression of macrophage markers and CXCL16 in gastric tissue (A) mIHC images of CD86 (red), CD163 (green) and CXCL16 (yellow) expressed in CNAG, CAG and CAG-E, respectively. Scale bar: 50 μm; (B) Differential Analysis of CD86+, CD163+, and CXCL16+ Cell Percentages in Different Types of Gastritis Tissue; (C) Box line plot of CD163+, CD86+ and CXCL16+ cell density in the study cohort. * indicates P<0.05, ** indicates P<0.01, *** indicates P<0.001, and **** indicates P<0.0001. Total sample size n=60, with n=20 per group.

To evaluate the distribution of positive cells in tissues and their number per unit area, we further assessed CD86⁺, CD163⁺ and CXCL16⁺ cell densities across three types of gastritis. According to the results, the positive cell density and percentage of positive cells analysed were consistent, with a more concentrated distribution of CD163⁺ and CD86⁺ cells observed in the CNAG group, which also exhibited the highest density of CD163⁺ cells (median number of positive cells/mm², 2451.7 vs 2418.7, P = 0.030; and 2451.7 vs 56.7, P= 0.009, respectively), followed by CD86⁺ cells higher than CXCL16 (median number of positive cells/mm², 2418.7 vs 56.7, P=0.001). Conversely, CD86⁺ cell density was significantly higher than that of the other two in the CAG and CAG-E groups, with a statistically significant difference in both cases (P<0.05). Furthermore, the distribution of CXCL16⁺ cell was highly centralised, with a low positive cell density in the CAG group and an even lower (almost negligible) density in the CNAG and CAG-E groups (Figure 7C).

Positive Cell Intensity Further Validates the Expression of CD86, CD163 and CXCL16 at Different Stages of Gastritis Progression After

Determining the staining intensity of CD86, CD163 and CXCL16 in the gastric tissues of patients with CNAG, CAG and CAG-E through multiple immunofluorescence assay, we further tested the expression levels of these indexes in different progression periods of gastritis, and observed that CD86, CD163 and CXCL16 were expressed in the early stage of gastritis, suggesting that they participate in the entire process of gastritis evolution. CD163 likewise stained with the strongest intensity in CNAG, and CAG was similar to that in CAG-E, with insignificant changes, and the difference between the 2 groups was not statistically significant (P>0.05). In contrast, CD86-positive macrophages exhibited increasing fluorescence intensity during the development of gastritis, and were significantly upregulated expression in gastric tissues, with the expression level being twice higher compared with that of the other 2 molecules,with pairwise comparisons showing statistically significant differences (P<0.05). The CXCL16-positive cells showed strong staining intensity in CAG (Figure 8).

Figure 8 Staining intensity of CD86, CD163, and CXCL16 positive cells in CNAG, CAG, and CAG-E CD86: P < 0.05, compared among the three gastritis groups; P < 0.01, the CAG group compared with the CNAG group; P < 0.001, the CAG-E group compared with the CNAG group; P < 0.01, the CAG-E group compared with the CAG group. CD613: P < 0.05, compared among the three gastritis groups; P < 0.01, comparison between CAG and CNAG groups; P < 0.05, comparison between CAG-E and CNAG groups. CXCL16: P < 0.05, compared among the three gastritis groups; P < 0.05, the CAG group compared with the CNAG groups.

The differential expression analyses indicated that in the CNAG group, CD163 expression was notably elevated, suggesting that the early stages of gastritis are predominantly characterized by M2 macrophage polarization. In contrast, the CAG and CAG-E groups exhibited higher CD86 expression, indicating a shift toward M1 macrophage activation, which likely plays a critical role in the progression and pathogenesis of gastritis. This indicated a potential correlation between macrophage polarisation status and the degree of inflammation and pathological changes in gastritis, with M1-type macrophages being associated with severe inflammation and tissue damage, and the M2-type macrophages exerted a restorative effect in the early stages of inflammation, but its anti-inflammatory response was relatively insufficient in the later stages of the disease, allowing persistent inflammation making it difficult to reverse the disease. CXCL16, a chemokine, was also upregulated and may act a synergistic effect with M1 macrophages to promote CAG development.

Co-Localisation of Expression and Correlation Analysis of CD86, CD163 and CXCL16 Proteins

To investigate the impact of CXCL16 on macrophage polarisation, we conducted the positive cell co-localisation and correlation analyses of CD86 and CD163 with CXCL16 expression in gastritis tissues. Such analyses enabled us to demonstrate the relationship between macrophage polarisation and CXCL16 in gastritis. Results showed that in CNAG and CAG-E groups, the expression of CXCL16 was higher in CD86⁺ cells than in CD163⁺ cells, despite low expression of CXCL16 and sparse co-localised expression of CD86⁺CXCL16⁺ and CD163+CXCL16⁺ (P<0.05). In contrast, in the CAG group, CXCL16 expression was significantly higher in M1 (CD86⁺) macrophages than in M2 macrophages (CD163⁺) (Figure 9A and B, P<0.05). Pairwise comparisons of CXCL16 co-localization with CD86 and CD163 among the three gastritis groups revealed that the CAG group exhibited significantly higher CXCL16 expression in both CD86+ and CD163+ cells compared to the other groups (P<0.05). Next, co-localisation positive cell density assessment of CXCL16 expression on CD86⁺ and CD163⁺ cells in different degrees of inflammation was examined (Figure 9C), which indicated a similar association, with CD86⁺ CXCL16⁺ cell density [median density of 160.0/mm² (range 93.7–339.4)] being significantly higher in the CAG state than CD163⁺ CXCL16⁺ cells [median density of 90.4/mm² (range 0.0–128.8)]. In contrast, the CD86⁺ CXCL16⁺ cells were dispersed in CNAG and CAG-E with median densities and ranges of [2.4/mm² (0.0–4.0) and 2.4/mm² (0.0–2.4)], respectively. This suggested that the CXCL16 expression primarily affected M1 macrophage polarisation, but it did not affect the degree of inflammation.

Figure 9 Co-expression and Correlation of CXCL16 and macrophage markers in gastric tissue (A) mIHC images of CD86, CD163, and CXCL16 co expressed in CNAG, CAG, and CAG-E, respectively; (B) The percentage of CXCL16 expression observed on CD86+and CD163+cells at different levels of inflammation; (C) The density of positive cells showing co-localization of CD86, CD163, and CXCL16 expression; (D) Correlation analysis of CD86, CD163 and CXCL16 expression. * indicates P<0.05, **** indicates P<0.0001.

Next, we investigated the relationship between macrophage polarisation status and CXCL16 based on the CD86,CD163 and CXCL16 protein correlation analysis. The data indicatedthat there was no correlation between the expression of CD163 and CXCL16 in CAG (P>0.05). In contrast, CD86 was positively correlated with CXCL16 expression (Figure 9D, P<0.05). This analysis suggested that CXCL16 was primarily involved in the regulation of M1 macrophage polarisation and participates in M1 macrophage-mediated inflammatory environment of CAG.

CXCL16 Promotes Macrophage Polarization to M1 and Inhibits Its Polarization to M2

THP-1 is a human monocyte cell line that is often applied in research on inflammation and immune response. To determine the regulatory effect of CXCL16 on macrophage polarisation, the THP-1 cells were cultured in vitro and induced to form macrophages, after which the expression of CXCL16 and CXCR6 in cells was examined via immunofluorescence staining experiments. In these test, we observed strong fluorescent signals of CXCL16 and its sole receptor CXCR6 in macrophages (Figure 10A). The qRT-PCR results showed that the mRNA expression of the M1 macrophage marker CD86 was significantly up-regulated after macrophage treated with different concentrations of CXCL16 (0, 50, and 100ug/mL) compared with the A1 control group in a concentration-dependent manner. Notably, as the concentration of CXCL16 increased, the mRNA expression level of CD86 was also significantly up-regulated (P<0.05, Figure 10B). However, the mRNA expression level of the M2 macrophage marker CD163, gradually decreased, with the lowest expression levels observed in the CI group, indicating that the expression of CD163 was significantly down-regulated as the concentration of CXCL16 increased (P<0.05, Figure 10C). These results suggested that CXCL16 promoted macrophage polarisation to M1 type, and inhibited its polarisation to M2 type, and this effect is more pronounced as the concentration of CXCL16 increased.

Figure 10 The effects of CXCL16 on macrophage polarization (A) Immunofluorescence staining of CXCL16 and CXCR6 in macrophages; (B) mRNA expression of CD86 in macrophages incubated with CXCL16 at different concentrations; (C) mRNA expression level of CD163 in macrophages. * indicates P<0.05, ** indicates P<0.01, *** indicates P<0.001.

Taken together, these results provide important clues that will expand the current understanding the role of macrophage polarisation in different types of gastritis and the role of CXCL16 in regulating macrophage function.The expression pattern of CD86, CD163 and CXCL16 molecules at different stages of gastritis development suggests that these molecules may serve as potential early biomarkers for the onset of gastritis. Moreover, their sustained expression during the progression phase suggests a role in influencing the disease’s advancement.

Discussion

To the best of our knowledge, this is the first study to assess the expression of CXCL16 and macrophage markers in CAG through bioinformatics analysis and experimental validation. Specifically, we examined the impact of CXCL16 expression on macrophage polarisation and the potential role of immune responses in CAG formation. Our findings revealed that macrophage polarisation correlated closely with CXCL16 expression. We also found a correlation between chemokines and immune cells, of which both were linked to changes in the CAG microenvironment. These results highlight the potential regulatory mechanism of CXCL16 and its potential clinical utility as a novel target for CAG immunotherapy.

Besides affecting food digestion functions, CAG, a characteristic precancerous lesion, significantly increases the risk of Gastric Cancer (GC). Gastric mucosa atrophy could impair folate and vitamin B12 adsorption, increasing the risk of severe hematological illnesses such as pernicious anemia, along with various neurological, psychiatric, cognitive, and ischemic heart diseases.^21,22 Moreover, the global incidence rate of CAG has been increasing annually in recent years, particularly in younger patients, severely impacting their health and quality of life. Therefore, early screening and development of effective pharmacological interventions would be imperative for improved clinical outcomes. Macrophages—bone marrow-derived mononuclear phagocytic cells—can polarise into different functional subtypes under specific stimulation conditions, finely regulating and responding to various stimuli and ultimately impacting inflammation or disease pathogenesis.²³ They could also release numerous inflammatory cytokines, thus mediating innate immune responses. For instance, significant M1 M φ s activation could cause gastric mucosal damage and inhibit M2 M φ s polarisation, potentially resulting in a more severe gastric inflammation.²⁴ Furthermore, Zhou and Naqvi et al reported a significant upregulation of the M1/M2 ratio in the gingival tissue of patients with chronic periodontitis, a phenomenon that correlated with disease severity, whereas the expression of M1 macrophage markers were downregulated following periodontitis treatment.^25,26 Additionally, M1 macrophages were implicated in early lung inflammation and injury. Meanwhile, M2 macrophages promoted pulmonary fibrosis, with macrophage depletion exerting a contrary effect.²⁷ These studies link the dynamic changes in macrophage polarisation closely to chronic inflammation, although their specific regulatory mechanisms in CAG remain unclear. Given the pro- and anti-inflammatory effects of M1 and M2 macrophages, respectively, we hypothesised that the regulation of the M1/M2 ratio could be a promising therapeutic strategy for CAG.

Owing to recent advancements in pertinent technologies, bioinformatics has recently emerged as a vital clinical tool, particularly in exploring the diagnostic markers and biological processes of diseases. Herein, to explore the biological functions and potential target markers of CAG immunity, we first screened CAG-related datasets and immune genes using the GEO database, yielding 24 common CAG IGs. Enrichment analyses further suggested that these genes were mainly involved in immune cell differentiation, recruitment, and homing, intrinsic and adaptive immunity regulation, cytokine-cytokine receptor interactions, and chemokine signalling pathways. Therefore, the identified genes might regulate functional modules that are highly comparable to the physiological functions of chemokines and macrophages and correlate closely with CAG onset. The identified IGs were further subjected to protein interaction analyses, revealing that the target markers C86 and CD163, and the chemokine CXCL16, crucially influenced CAG onset.

Chemokines, key molecules that regulate immune cell migration and localisation, play an important role in immune and inflammatory responses. Notably, CXCL16, a member of the CXC chemokine family, exists in two forms: Transmembrane CXCL16 (mCXCL16) and soluble CXCL16 (sCXCL16). Whereas sCXCL16 is responsible for the chemotaxis of cells carrying the CXCR6 receptor,⁸ mCXCL16 mainly functions as an intercellular adhesion molecule and is often cleaved by ADAM10 to form sCXCL16, ultimately inducing CXCR6+ cell recruitment towards the lesion site^17,28—a phenomenon that has been established to regulate the pathological processes of several inflammatory illnesses. Aberrant CXCL16 expression was previously reported in various inflammatory tissues, serving as a marker and promoter of inflammation-related diseases. For instance, Rheumatoid Arthritis (RA) patients exhibited serum CXCL16 upregulation.²⁹ Additionally, CXCL16 overexpression was reported in patients with Acute Kidney Injury (AKI), with CXCL16 inhibition reducing pro-inflammatory factor production post-AKI.³⁰ Furthermore, M1 macrophage and CXCL16 upregulation was detected in blood samples from acute stone cholecystitis patients, promoting neutrophil migration and Neutrophil Extracellular Trap (NET) formation, with a reduction of CXCL16 levels and macrophage polarisation alleviating the disease.³¹ Moreover, CXCL16 was implicated in liver disease onset and progression, with CXCL16, CXCR6 and ADAM10 upregulation and significant CXCL16 upregulation observed in the liver during inflammatory responses and infectious shock, respectively.^32,33 These findings collectively suggest that CXCL16 is crucially involved in pro-inflammatory microenvironment Formation. Nonetheless, the biological functions and molecular mechanisms of CXCL16 in CAG remain unclear.

Herein, we examined different phenotypic macrophage markers, CD86 and CD163, as well as the chemokine CXCL16. Based on GEO data and mIHC staining, we hypothesised that macrophage polarisation in gastric mucosal tissues might correlate with CXCL16 expression (positive expression; and co-expression of CXCL16 and macrophage markers). We observed significant M1 macrophage upregulation in CAG with gawith a higher expression in CAG and CAG-E than in CNAG at the protein level. These findistritis progression. Furthermore, the expression of the M1 macrophage marker CD86 exhibited a stepwise increase, ngs align with a previous study, which reported that M1 macrophage activation aggravated gastric inflammation,²⁴ further confirming that M1 macrophages play a major role in CAG. Conversely, M2 macrophages were correspondingly upregulated in the early stage of inflammation, inhibiting inflammatory overreactions and protecting gastric mucosal tissues from further damage. Additionally, CXCL16 was upregulated in CAG, particularly in CD86+ macrophages. Correlation analysis further revealed that M1 macrophage polarisation correlated positively with CXCL16 expression, suggesting that CXCL16 might influence M1 macrophage polarisation and CAG onset. To test this hypothesis, we performed in vitro experiments, and the results confirmed that CXCL16 exerts a potent chemotactic effect on M1 macrophages, promoting M1 macrophage polarisation and inhibiting M2 polarisation in the CAG microenvironment. These findings suggest that inhibiting CXCL16 expression could suppress M1 macrophage polarisation, thus alleviating gastric mucosal injury—a phenomenon that crucially elucidates CAG pathogenesis. However, the mechanisms behind macrophage transformation in CAG and those through which CXCL16 regulates the M1 phenotype remain unknown. Macrophage-expressed CXCR6 could bind to CXCL16 through its specific motifs, thereby affecting leukocyte recruitment and adhesion processes.³⁴ therefore, CXCL16/CXCR6 axis activation might crucially regulate macrophage-mediated inflammatory responses. Herein, macrophages exhibited an elevated expression of CXCL16 and its specific receptor CXCR6, implying that the regulatory effect of CXCL16 on macrophage polarisation (M1/M2) observed in this study could have been achieved via CXCL16/CXCR6 signalling axis activation. This mechanism aligns with the function of CXCL16/CXCR6 in immunomodulation as reported in the previous literature.¹¹ Moreover, CXCL16 overexpression in gastric mucosal tissues correlated with CXCR6 and ADAM10 upregulation.¹¹ Additionally, pro-inflammatory cytokines were reported to increase sCXCL16 shedding through ADAM10.^17,28,35 Based on these studies, we inferred that CXCL16 upregulation in CAG might promote ADAM10 expression, which, in turn, could cleave CXCL16 to produce sCXCL16, thus initiating CXCR6 activation. After several cell signaling transductions, we found that CXCL16 induced M1 macrophage proliferation and migration to the lesion site, triggering inflammatory cytokine production and exacerbating gastric mucosal tissue inflammation. This process highlights the specific mechanism through which CXCL16 could regulate macrophage polarisation in CAG; the positive feedback loop established through the CXCL16/CXCR6 axis.

The significant correlation between the overexpression of inflammatory factors (such as TNF-α, IL-6, and IL-1 β) and CAG onset and progression is well-documented.^36,37 While TNF-α exerts the strongest destructive effect on gastric mucosa, IL-1 β might activate specific immune responses and regulate immune surveillance function.³⁸ In the present study, we found that macrophages were stimulated by CXCL16 with a significant increase in M1 macrophages, while TNF-α, IL-6 and IL-1β were mainly secreted by M1 macrophages. This suggests that CXCL16 induced MI macrophage polarisation in CAG, promoting the secretion of numerous pro-inflammatory factors and causing gastric mucosal damage.Pro-inflammatory factors such as TNF-α, IFN-γ, IL-1β and IL-6 can increase the expression of CXCL16, and our study found that the expression of CXCL16 and CXCR6 was up-regulated in macrophages, and CXCL16 has the property of an adhesion protein that can bind to its specific receptor CXCR6^39,40—a phenomenon that could increase M1 macrophage accumulation at the inflammation site. Therefore, the roles of M1 macrophages and CXCL16 in CAG onset and progression could be reciprocal. According to research, the nuclear factor-κB (NF-κB) pathway, a pro-inflammatory signalling pathway, could initiate and regulate the transcriptional expression of pro-inflammatory genes.⁴¹ Herein, we mined the GEO dataset and found that CAG-related IGs, including the chemokine CXCL16, were mainly enriched in the cytokine-cytokine receptor interaction and chemokine signalling pathways. The cytokine-cytokine receptor interaction pathway, in which NF-κB expression was up-regulated in CAG, intestinal metaplasia, and GC, regulates the apoptosis and proliferation of gastric epithelial cells, and also plays an important role in “inflammation-cancer transition”.⁴¹ The chemokine signalling pathway, on the other hand, delivers signals to cells through a series of molecular events, regulating cell migration, immune response, and inflammatory response initiation and maintenance.⁴² Additionally, CXCL16 activates the NF-κB pathway via trimeric G proteins, PI 3-kinase, Akt, and IκB kinases (PI3K, Akt, IKK, and IB phosphorylation) to induce TNF-α expression.⁴³ Based on these studies, we deduced that CXCL16 treatment might significantly activate the pro-inflammatory pathway (NF-κB pathway), induce MI macrophage polarisation, and release numerous inflammatory factors (eg, TNF-α, IL-6, and so on) that could cause gastric mucosa injury, thus promoting CAG onset and progression. In other words, CXCL16 interacts with immune cells through multiple mechanisms, regulates immune responses, and modulates CAG immunopathology. Overall, pro-inflammatory chemokines play an additional role in CAG as inflammatory promoters, and besides inflammatory factor upregulation, CAG occurrence and the effect of CXCL16 in macrophage polarisation regulation might also be dependent on NF-κB activation and the positive feedback regulation of the CXCL16/CXCR6 axis.

This study highlights the critical role of CXCL16 in CAG via macrophage polarisation regulation, presenting it as a promising target for preventing gastric mucosal inflammation aggravation. Therefore, CXCL16-neutralising antibodies or CXCR6 receptor antagonists could be leveraged to suppress inflammatory progression. However, antibodies that could specifically target CXCL16 remain clinically unavailable. Furthermore, although the recombinant Tumour Necrosis Factor Receptor:Fc (rhTNFR:Fc) fusion protein can attenuate inflammation in patients with ankylosing spondylitis via CXCL16/CXCR6 pathway inhibition,³¹ studies on its potential efficacy in downregulating CXCL16 expression during CAG progression are scarce, underscoring the need for additional research in the future.

Despite its valuable insights, this study has several notable limitations. First, due to the restricted disease types and research focus, the datasets included were limited in both scope and sample size. To further elucidate the diagnosis and immune responses associated with CAG, future research should prioritize selecting additional datasets from diverse databases and incorporating larger sample sizes. Second, macrophages in vivo typically exhibit distinct tissue-specific phenotypes. Our experimental validation was conducted using in vitro cultured macrophages without subsequent in vivo validation through mouse models. The growth processes and environmental conditions of ex vivo macrophages may differ significantly from those observed in vivo. Third, mIHC staining of CXCL16 and macrophage-related markers may demonstrate heterogeneity; additionally, depending on storage duration, loss of antigenicity in stored paraffin sections could impact results. Fourth, while our current findings provide preliminary insights into the relationship between CXCL16 and macrophage polarization, the observed dose-response pattern indicates a degree of specificity. However, establishing a direct causal relationship will require future studies employing functional knockout experiments (eg, CXCL16 neutralization or CXCR6 siRNA/CRISPR knockout) for more compelling validation. Furthermore, there is a lack of fundamental experimental verification regarding the transcriptional regulatory mechanisms underlying the expression patterns of CXCL16 and macrophage markers. Additionally, it remains unclear which molecular mechanisms or signaling pathways are involved by which CXCL16 modulates macrophage subpopulations in CAG. Consequently, further research will be necessary to deepen our understanding of potential biomechanisms. Many other chemokines could also lead to CAG onset and should be explored in future research. Moreover, the expression and potential functions of CXCL16 in other immune cells remain unclear.

Conclusion

In summary, this study employed bioinformatics techniques to elucidate the immunobiological functions and associated pathways related to CAG, identifying macrophage-related markers and their potential influencing factors. The findings revealed that the M1 macrophage marker CD86 and the chemokine CXCL16 are significantly upregulated in CAG, demonstrating a positive correlation between these two entities. Furthermore, experimental validation not only confirmed but also illuminated for the first time the potential role of CXCL16 in inducing M1 macrophage polarization while simultaneously suppressing M2 macrophage polarization. As a regulator of macrophage inflammatory responses, CXCL16 influences both the onset and progression of CAG. This discovery provides fundamental insights into how CXCL16 modulates immune responses in disease contexts, suggesting its potential as a novel target for regulating M1 macrophage polarization in CAG. It enhances our understanding of CAG pathogenesis and offers new perspectives for identifying biomarkers and therapeutic interventions. Moving forward, we can explore innovative diagnostic approaches and therapeutic strategies for CAG by targeting the regulation of macrophage polarization. Concurrently, implementing loss-of-function studies and animal models will further clarify the precise roles and molecular mechanisms of chemokines and macrophages in CAG, thereby advancing our comprehensive understanding of the disease’s pathogenesis.

Data Sharing Statement

The datasets analyzed in this study are available in the GEO under accession number [GSE153224](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE153224) and [GSE27411] (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27411), as well as in the ImmPort database (https://www.immport.org/shared/genelists). All data are publicly accessible.

Human Ethics and Consent to Participate Declarations

The study protocol was reviewed and approved for consent by the Ethics Committee of Gansu Provincial Hospital (Approval number: 2025-410) and was conducted in accordance with the principles of the Declaration of Helsinki.

Acknowledgments

The authors would like to thank all the reviewers who participated in the review and MJEditor (www.mjeditor.com) for its linguistic assistance during the preparation of this manuscript.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data and analysis, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was supported by the Gansu Provincial Health Industry Research Project (GSWSQN2025-04), the Research Project of the Eighth Affiliated Hospital of Southern Medical University (SRSP2025013) and the National Natural Science Foundation of China (81560093).

Disclosure

All authors declare that there are no competing conflicts of interest in this study.

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January 19, 2026

Know where to watch live streaming in India

After a first-round exit last week, PV Sindhu will be looking to bounce back when she leads India’s challenge at the Indonesia Masters 2026 badminton tournament, which starts on Tuesday.

Badminton matches from the Indonesia Masters 2026, a BWF…

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January 19, 2026