Identification of key hub genes in knee osteoarthritis through integrated bioinformatics analysis

Abstract Knee osteoarthritis (KOA) is a common chronic joint disease globally. Synovial inflammation plays a pivotal role in its pathogenesis, preceding cartilage damage. Identifying biomarkers in osteoarthritic synovial tissues holds promise for early diagnosis and targeted interventions. Gene expression profiles were obtained from the Gene Expression Omnibus database. Subsequent analyses included differential expression gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) on the combined datasets. We performed functional enrichment analysis on the overlapping genes between DEGs and module genes and constructed a protein–protein interaction network. Using Cytoscape software, we identified hub genes related to the disease and conducted gene set enrichment analysis on these hub genes. The CIBERSORT algorithm was employed to evaluate the correlation between hub genes and the abundance of immune cells within tissues. Finally, Mendelian randomization analysis was utilized to assess the potential of these hub genes as biomarkers. We identified 46 differentially expressed genes (DEGs), comprising 20 upregulated and 26 downregulated genes. Using WGCNA, we constructed a gene co-expression network and selected the most relevant modules, resulting in 24 intersecting genes with the DEGs. KEGG enrichment analysis of the intersecting genes identified the IL-17 signaling pathway, associated with inflammation, as the most significant pathway. Cytoscape software was utilized to rank the candidate genes, with JUN, ATF3, FOSB, NR4A2, and IL6 emerging as the top five based on the Degree algorithm. A nomogram model incorporating these five genes, supported by ROC curve analysis, validated their diagnostic efficacy. Immune infiltration and correlation analysis revealed that macrophages were significantly associated with JUN (p