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Identification of BMX as a Potential Biomarker for Rheumatoid Arthritis Based on WGCNA, Machine Learning, and
Xinmin Huang1, Xu Cai1, Zhenbo Yan1
1Rheumatology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, China.
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.
