BACKGROUND Rheumatoid arthritis (RA) has a high prevalence in the elderly population. The genes and pathways in the inflamed synovium in patients with RA are poorly understood. This study aimed to identify differentially expressed genes (DEGs) linked to the progression of synovial inflammation in RA using bioinformatics analysis. MATERIAL AND METHODS Gene expression profiles of datasets GSE55235 and GSE55457 were acquired from the Gene Expression Omnibus (GEO) database. DEGs were identified using Morpheus software, and co-expressed DEGs were identified with Venn diagrams. Protein-protein interaction (PPI) networks were assembled with Cytoscape software and separated into subnetworks using the Molecular Complex Detection (MCODE) algorithm. The functions of the top module were assessed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. RESULTS DEGs that were upregulated were significantly enhanced in protein binding, the cell cytosol, organization of the extracellular matrix (ECM), regulation of RNA transcription, and cell adhesion. DEGs that were downregulated were associated with control of the immune response, B-cell and T-cell receptor signaling pathway regulation. KEGG pathway analysis showed that upregulated DEGs enhanced pathways associated with the cell adherens junction, osteoclast differentiation, and hereditary cardiomyopathies. Downregulated DEGs were enriched in primary immunodeficiency, cell adhesion molecules (CAMs), cytokine-cytokine receptor interaction, and hematopoietic cell lineages. CONCLUSIONS The findings from this bioinformatics network analysis study identified molecular mechanisms and the key hub genes that may contribute to synovial inflammation in patients with RA.