Exploring the Mystery of Osteoarthritis using Bioinformatics Analysis of Cartilage Tissue.

Abstract

Osteoarthritis (OA) is a kind of chronic disease relating to joints, which seriously affectsthe daily life activities of the elderly and can also lead to disability. However, the pathogenesis of OA is still unclear, which leads to 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. 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 found to serve as an indicator to identify drugs with potential therapeutic effects on key genes and Receiver Operating Characteristic (ROC) analysis was used for identifying underlying biomarkers of OA. 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 hsamir- 5692a to be 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, which were potentially specific to the most key genes. Finally, we speculated that ESR1 might be a potential biomarker of OA. 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.