Identification of BMX as a Potential Biomarker for Rheumatoid Arthritis Based on WGCNA, Machine Learning, and Experimental Validation.

BACKGROUND

Rheumatoid arthritis (RA) is a chronic autoimmune disease needing reliable biomarkers. This study aimed to identify potential RA biomarkers using WGCNA and machine learning, and analyze their correlation with disease activity and STAT3.

METHODS

GEO datasets were used. WGCNA and three machine learning methods (LASSO, SVM, Boruta) screened common core genes. Correlation with STAT3 was analyzed. RT-qPCR validated core gene expression in RA patients. Correlation with clinical parameters (DAS28, CCP, RF, ESR) and ROC curves were assessed.

RESULTS

WGCNA identified 11 modules; the black module correlated most with RA. LASSO, SVM, and Boruta identified 16, 35, and 46 key genes respectively, with one overlapping core gene: BMX. BMX expression positively correlated with STAT3. RT-qPCR confirmed BMX upregulation in RA, which positively correlated with DAS28 and RF. ROC analysis gave an AUC of 0.789.

CONCLUSION

BMX expression is upregulated in RA patients, correlates with disease activity, and represents a potential diagnostic biomarker. The BMX‑STAT3 correlation suggests its possible involvement in RA pathogenesis, but the regulatory relationship requires further functional validation.