Integrated bioinformatics analysis of cartilage for osteoarthritis

Abstract

Background Osteoarthritis (OA) is a joint disease which threatens the health of older people around the world. However, its pathological mechanism remains unclear. In the current study, we aimed to identify the key genes and underlying pathological mechanisms associated with OA. Methods Firstly, GSE117999 was obtained from the Gene Expression Omnibus (GEO) database. We then obtained the DEGs, hub genes and key genes of the dataset using bioinformatics analysis, after which we analyzed these items using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). Furthermore, the target miRNAs of key genes were predicted by miRDB. The Drug Gene Interaction database (DGIdb) was used to predict target drugs for key genes. Receiver operating characteristic (ROC) analysis was employed to evaluate the effectiveness of key genes. Results: A total of 974 DEGs were identified. Furthermore, we found 19 hub genes and 10 key genes. We then predicted that hsa-mir-3613-3p, hsa-mir-548e-5p and hsa-mir-5692a would be closely related to key genes through web tools, and that two drugs, etoposide and everolimus, were highly specific to some key genes. Finally, we analyzed the receiver operating characteristic (ROC) of key genes in the samples and speculated that ESR1 may be a biomarker of OA. Conclusions This study deepens our understanding of OA and may be beneficial for further explorations of the pathological mechanism of the disease.