Screening of TNF signaling pathway-related genes in knee osteoarthritis (KOA) using WGCNA and machine learning

Knee osteoarthritis (KOA), a prevalent degenerative joint disease, involves complex inflammatory responses and signal pathway regulation in its pathogenesis. This study investigates the tumor necrosis factor signaling pathway's mechanistic role in KOA using diverse bioinformatics approaches. Initially, differential expression analysis identified 222 significant differentially expressed genes. Subsequent Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that these differentially expressed genes are mainly involved in inflammatory responses and cytokine signaling pathways. Weighted gene co-expression network analysis was then employed to identify the MEbrown module, which is closely associated with knee arthritis, and 20 key tumor necrosis factor signaling pathway-related genes were selected. Further analysis using LASSO regression and the Boruta algorithm screened out important genes, including MCL1, JUN, and GADD45B, whose superior performance in classification tasks was validated. Additionally, immune infiltration analysis revealed significant differences in immune cell distribution across knee arthritis samples, underscoring the critical role of inflammation regulation in disease progression. Overall, this study enhances the understanding of KOA pathogenesis and offers valuable insights for personalized treatment and, precision medicine.