Identification of key pathways and biomarkers in rheumatoid arthritis synovial tissue through comprehensive transcriptomic integration.

Abstract

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovitis and joint destruction. Although our understanding of its pathogenesis has deepened, the molecular mechanisms underlying changes in synovial tissue remain incompletely elucidated. We integrated transcriptomic datasets from the GEO database, including gene expression microarray datasets and RNA-seq datasets (GSE1919, GSE12021, GSE55235, GSE55457, GSE77298, GSE89408), to identify differentially expressed genes (DEGs) in RA synovial tissue. Functional enrichment (GO, KEGG) and gene set enrichment analysis (GSEA) were performed to explore key pathways. CIBERSORT was used to assess immune cell infiltration. We applied a comprehensive machine learning approach using 113 algorithms to screen for core diagnostic genes, which were subsequently validated across multiple datasets. In the training set (GSE89408), we identified 9204 DEGs that were significantly enriched in immune-related processes (leukocyte migration, cytokine activity) and pathways (cytokine-cytokine receptor interaction, chemokine signaling). GSEA confirmed the activation of these pathways (NES >1, FDR <0.001). Immune infiltration analysis showed a significant increase in plasma cells in RA synovium (with no plasma cells in the control group). Machine learning identified 9 core genes (AIM2, AKR1B10, CXCL10, CXCL13, IGLC1, IL2RG, LRRC15, SDC1, and IGHG1), which demonstrated robust diagnostic performance. Cytokine-cytokine receptor interactions, chemokine signaling pathways, and plasma cell infiltration may play critical roles in synovial pathogenesis of rheumatoid arthritis. Among the 9 core genes identified, 8 have been experimentally validated for their functions in RA, while the unverified IGHG1 may serve as an important biomarker for the pathogenesis of RA synovium.