Identification of Immune-Related Risk Genes in Osteoarthritis Based on Bioinformatics Analysis and Machine Learning

In this research, we aimed to perform a comprehensive bioinformatic analysis of immune cell infiltration in osteoarthritic cartilage and synovium and identify potential risk genes. Datasets were downloaded from the Gene Expression Omnibus database. We integrated the datasets, removed the batch effects and analyzed immune cell infiltration along with differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was used to identify the positively correlated gene modules. LASSO (least absolute shrinkage and selection operator)-cox regression analysis was performed to screen the characteristic genes. The intersection of the DEGs, characteristic genes and module genes was identified as the risk genes. The WGCNA analysis demonstrates that the blue module was highly correlated and statistically significant as well as enriched in immune-related signaling pathways and biological functions in the KEGG and GO enrichment. LASSO-cox regression analysis screened 11 characteristic genes from the hub genes of the blue module. After the DEG, characteristic gene and immune-related gene datasets were intersected, three genes, PTGS1, HLA-DMB and GPR137B, were identified as the risk genes in this research. In this research, we identified three risk genes related to the immune system in osteoarthritis and provide a feasible approach to drug development in the future.