ABSTRACT Introduction Rheumatoid arthritis (RA) is a chronic autoimmune disorder with unclear molecular mechanisms, complicating early diagnosis and treatment. This study aimed to identify hub genes and pathways driving RA pathogenesis and assess their therapeutic potential. Methods Gene expression datasets related to RA were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and analyzed by functional enrichment and protein–protein interaction network construction. Machine learning approaches, including LASSO regression, random forest, and SVM‐RFE, were used to screen hub genes. Pathway associations were explored using Gene Set Enrichment Analysis (GSEA). Experimental validation was performed in collagen‐induced arthritis (CIA) rat models and MH7A synovial fibroblast cells through Western blot and functional assays. Results A total of 106 DEGs were identified in RA synovial tissues, including 76 upregulated and 30 downregulated genes. Enrichment analyses revealed involvement in cytokine–cytokine receptor interaction, lymphocyte‐mediated immunity, and immunoglobulin complexes. SDC1 emerged as a key hub gene across all three machine learning methods. GSEA indicated its significant correlation with the JAK–STAT pathway. In CIA rats, SDC1 expression was markedly elevated alongside p‐JAK2 and p‐STAT3 levels. Silencing SDC1 in MH7A cells reduced cell proliferation, decreased p‐JAK2 and p‐STAT3 expression, and promoted apoptosis. Conclusions This study identifies SDC1 as a central hub gene in RA pathogenesis through activation of the JAK2–STAT3 signaling pathway. These findings highlight SDC1 as a potential biomarker for early diagnosis and a promising target for therapeutic intervention, providing new insights into RA management.
Cao et al. (2026) studied this question.