Exploring the mystery of osteoarthritis using bioinformatics analysis from cartilage tissue.

BACKGROUND Osteoarthritis (OA) is a kind of chronic joint destructive disease, which seriously endangers the activity ability of the elderly all over the world and can lead to disability. However, the pathogenesis of OA is still unclear, which leads to the limited treatment and the therapeutic effect far from people's expectations. This study aims to filter out key genes in the pathogenesis of OA and explore their potential role in the occurrence and development of OA. METHOD The dataset of GSE117999 was obtained and analyzed in order to identify the differentially expressed genes (DEGs), hub genes and key genes. We also identified potential miRNAs which may play a major role in the pathogenesis of OA, and verified their difference in OA by real-time quantitative PCR (RT-qPCR). DGldb was served to identify drugs with potential therapeutic effects on key genes and Receiver Operating Characteristic (ROC) analysis was used for identifying underlying biomarkers of OA. RESULTS We identified ten key genes including MDM2, RB1, EGFR, ESR1, UBE2E3, WWP1, BCL2, OAS2, TYMS and MSH2. Then, we identified hsa-mir-3613-3p, hsa-mir-548e-5p and hsa-mir-5692a were potentially related to key genes. In addition, RT-qPCR confirmed the differential expression of identified genes in mouse cartilage with or without OA. We then identified Etoposide and Everolimus, were potentially specific to the most key genes. Finally, we speculated that ESR1 might be a potential biomarker of OA. CONCLUSIONS In this study, potential key genes related to OA and their biological functions were identified, and their potential application value in the diagnosis and treatment of OA has been demonstrated , which will help us to improve the therapeutic effect of OA.