Objective To screen for potential biomarkers of anal fistula(AF)by using weighted gene co-expres-sion network analysis(WGCNA),machine learning,immune infiltration analysis,and animal experiments.Meth-ods Download transcriptome data from the gene expression omnibus containing AF and peri-fistula tissue(PF)for differential analysis.Gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)pathway enrich-ment analyses on differentially expressed genes(DEGs)were performed.WGCNA results were integrated with DEGs to screen for genes related to AF.Machine learning methods such as the least absolute shrinkage and selec-tion operator(LASSO),support vector machine recursive feature elimination(SVM-RFE),and random forest(RF)were utilized to screen potential biomarkers for AF.Immune infiltration analysis was conducted and the AF rat model was replicated for validation.Results A total of 377 DEGs were obtained,mainly enriched in pathways such as B cell receptor signaling and chemokine signaling.Machine learning algorithms identified matrix metallo-proteinase 13(MMP13)as a potential biomarker for AF.In AF samples,memory B cells,plasma cells,M0 mac-rophages,and M1 macrophages were higher than in PF samples,while resting CD4 memory T cells and resting den-dritic cells were lower than in PF samples.MMP13 showed a positive correlation with M0 macrophages,activated mast cells,and immature B cells,and a negative correlation with resting mast cells.Experimental results showed that MMP13 expression levels were higher in rat AF samples compared to the control group.Conclusion The on-set of AF involves various immune cells and signaling pathways.MMP13 is significantly upregulated in AF tissue and correlates with multiple immune cells,making it a potential novel biomarker of AF.
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