Bioinformatics approach for potential genes associated with osteoarthritis

Abstract

Background: Osteoarthritis is the main cause of disability and pain. Due to limited understanding of the disease mechanism, it is generally difficult to diagnose early and eliminate sequelae. The aim of this study was to identify novel biomarkers for osteoarthritis (OA) using a bioinformatics approach. Method: The gene expression dataset GSE82107 were obtained from the Gene Expression Omnibus (GEO) database. Matrix quality assessment was performed using corrplot packages and principal component analysis. The differentially expressed genes (DEGs) picked out using GEO2R tool. Enrichment analyses were performed using The Database for Annotation, Visualization and Integrated Discovery and Gene Set Enrichment Analysis (GSEA). Weighted correlation network analysis (WGCNA) was used to find gene modules highly associated with OA. Cytoscape with Molecular Complex Detection (MCODE) plug-in was utilized to analyze protein-protein interaction of these DEGs. Receiver operator characteristic curve analysis was used to evaluate the diagnostic effectiveness of genes. Results: Samples with different conditions (HC and OA) are obviously distinguished, and the distance between biological replicates is relatively close. In total, 1676 DEGs were identified. Enrichment analysis showed that there were some gene sets related to OA pathology, such as chondrocyte development. The results of WGCNA analysis showed that 298 genes were most positive associated with OA. 10 common genes obtained were selected as candidate core genes. ROC results showed that 5 of these genes had the greatest diagnostic value. Conclusion: This is the first study to identify biomarkers related to OA by combining multiple algorithms such as GSEA, WGCNA, MCODE and ROC. We suggest that GLG1, PAPSS2, CTSK, TIMP1 and SDC1 could serve as valuable biomarkers. Further studies are needed to examine the precise role and mechanism of these genes in OA.