Abstract
BACKGROUND: Osteoarthritis (OA) is a degenerative disease associated with aging. Although an increasing body of research suggests a close relationship between aging and OA, the underlying mechanisms remain unclear. This study explores the relationship between aging related genes (ARGs) and OA, providing potential new targets for understanding the pathogenesis and treatment of OA. METHODS: The OA synovial tissue dataset was obtained from the GEO database, and differentially expressed genes (DEGs) were screened. The DEGs were intersected with ARGs to identify differentially expressed aging related genes (DEARGs), which were then subjected to functional enrichment analysis, PPI network analysis, and machine learning algorithms (LASSO and RF) to identify key genes. In addition, a nomogram was constructed based on the key genes to predict OA risk, and its diagnostic value was evaluated using ROC curves. Subsequently, the expression levels of the key genes were validated through qRT-PCR experiments. Finally, the CIBERSORT algorithm was applied to assess the proportion of immune cells and investigate the correlation between the key genes and immune cells. RESULTS: A total of 34 DEARGs were identified. PPI network analysis revealed 12 key DEARGs. Subsequently, LASSO and RF algorithms identified ATF3, KLF4, NFKBIA, and SOD2 as key genes. Based on nomogram and ROC curve analysis, these four key genes demonstrated good diagnostic value. qRT-PCR showed that ATF3, KLF4, NFKBIA, and SOD2 were significantly downregulated in OA. Immune infiltration analysis revealed differences in Plasma cells, T cells follicular helper, Mast cells resting, T cells CD4 memory resting, NK cells activated, Monocytes, and Mast cells activated between the OA group and normal controls. CONCLUSION: ATF3, KLF4, NFKBIA and SOD2 are identified as novel biomarkers associated with aging in OA and may serve as potential therapeutic targets for OA treatment.