Identification of SDC1 as a Key Regulator and Therapeutic Target in Rheumatoid Arthritis via JAK2-STAT3 Pathway.

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.