Identification of secretory protein related Biomarkers for Primary Biliary Cholangitis based on Machine Learning and experimental validation

Primary biliary cholangitis (PBC) is challenging to diagnose and treat due to its insidious onset. This study aimed to identify effective diagnostic biomarkers for PBC by focusing on secreted proteins through bioinformatics approaches. Two PBC-related bulk datasets, GSE119600 and GSE61260, were retrieved from the GEO database for analysis and validation, respectively. Gene sets related to secreted proteins were sourced from the THPA database. The analysis of GSE119600 included differential expression analysis, WGCNA, immune infiltration analysis, and enrichment analyses. By intersecting differentially expressed genes (DEGs), WGCNA hub module genes, and genes related to secreted proteins, 18 candidate genes were identified. Machine learning techniques—LASSO, random forest, GMM, and SVM-RFE—narrowed these to four hub genes: CSF1R, PLCH2, SLC38A1, and CST7. The diagnostic performance of these genes was assessed using LDA, QDA, Bayesian test, and Nomogram methods, with internal and external validation AUC values of 0.867 and 0.722, respectively. Experimental validation in PBC model mice confirmed that the expression of these genes was significantly altered. These findings suggest that CSF1R, PLCH2, SLC38A1, and CST7 could serve as novel diagnostic biomarkers for early PBC detection and provide insights into its underlying mechanisms.