Integrated bioinformatics and clinical data identify three novel biomarkers for osteoarthritis diagnosis and synovial immune

Abstract

Abstract Osteoarthritis (OA) is a degenerative joint disease that can be aggravated by synovitis and synovial immune disorders (SID). However, the role of synovial SID-related genes in OA synovium remains poorly understood. OA synovial and peripheral blood datasets were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/). Immune-related genes (https://reactome.org/) showing differential expression in peripheral blood were identified as immune disorder genes. Subsequently, differentially expressed immune disorder genes in OA synovium were further identified as SID genes. The Venn diagram, random forest, SVM-RFE algorithm, and multivariate analysis were employed to determine SID-related hub genes in OA synovium. Using the identified hub genes, we constructed and validated a diagnostic model for predicting OA occurrence. The correlation between hub gene expression and immune-related modules was explored using CIBERSORT and MCP-counter analyses. We identified three SID-related hub genes (ACAT1, SPHK1, and ACACB) in OA synovium. The diagnostic model incorporating these hub genes demonstrated reliable predictive accuracy (AUC = 0.939). Through qPCR analysis, we quantitated the expression levels of the hub genes and confirmed that three hub genes could serve as novel biomarkers for OA patients (AUC = 0.960). Furthermore, we observed a significant correlation between the expression of these hub genes and immune cell infiltration, as well as inflammatory cytokine levels in OA synovium. Our findings suggest that three SID-related hub genes have the potential to serve as diagnostic biomarkers for OA patients. These genes are associated with immune disorder and contribute to immune alterations within the OA synovium.