Identification and validation of amino acid metabolism-related genes and immunological characteristics in osteoarthritis by bioinformatics analysis

Abstract

Abstract Background: Osteoarthritis (OA) is a common joint disorder characterized by progressive destruction of articular cartilage and chronic inflammation. Growing evidence has implicated the roles of amino acid metabolism (AAM) and immunological factors in OA occurrence and development. However, the detailed mechanisms remain largely unknown. Therefore, identifying crucial genes and pathways related to AAM and immunology in OA using bioinformatics approaches is an important aspect to elucidate the pathogenesis of OA. Methods: Publicly available gene expression profiling datasets of OA were obtained from the gene expression omnibus (GEO) database. Differential expression analysis was performed to identify differentially expressed genes (DEGs) between OA and normal control samples. DEGs were intersected with amino acid metabolism related genes (AAMRGs) to obtain OA associated DEGs. Enrichment analysis including gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathways were performed on these DEGs. protein-protein interaction (PPI) network was constructed and key genes were identified by topology analysis. Immune cell infiltration was estimated by gene set enrichment analysis (GSEA) algorithm and CIBERSORT tool. receiver operating characteristic curve (ROC) curve analysis was applied to assess diagnostic performance of hub genes. Results: A total of 64 DEGs related to AAM were identified in OA. Enrichment analysis indicated these DEGs were mainly involved in glycine, serine and threonine metabolism. There were 8 hub genes identified from the PPI network. Immune cells analysis revealed increased infiltration of macrophages and neutrophils in OA compared to normal controls. Several hub genes such as SLC2A1 and VEGFA demonstrated high diagnostic accuracy for OA. Significant correlations were observed between AAM genes and multiple immune cells. Conclusion Through multi-omics analysis of osteoarthritis data, we identified AAM-related hub genes PPARG and VEGFA. Their expression associated with OA pathogenesis and immune infiltration, providing evidence for AAM involvement in the pathogenesis of OA. Further validation may facilitate their utility as OA biomarkers and therapeutic targets.