Abstract
PURPOSE: Osteoarthritis (OA) is the most common chronic joint disorder in elderly individuals. This study aimed to identify immune-related diagnostic gene signatures for OA. METHODS: First, we performed single-sample gene set enrichment analysis (ssGSEA) to evaluate the infiltration of immune cells in OA expression data from the Gene Expression Omnibus (GEO) database. Then, weighted gene coexpression network analysis (WGCNA) was performed to identify hub modules and genes related to immune cell types with significant infiltration. Finally, we screened diagnostic markers from the differentially expressed genes (DEGs) in both the OA group and the hub module using least absolute shrinkage and selection operator (LASSO) logistic regression. RESULTS: Immune filtration analysis showed that immature B cells, mast cells, natural killer T cells, myeloid-derived suppressor cells (MDSCs), and type 2 T helper cells were dysregulated in OA samples. In WGCNA, a total of 120 genes were selected as hub genes associated with mast cell infiltration.The enrichment analysis showed that spliceosome, positive regulation of cell migration, and response to mechanical stimulus were mainly involved. The LASSO regression model for the GSE117999 dataset revealed 15 DEGs for predicting OA. Finally, two genes were obtained by intersection for further investigation. CONCLUSION: Cold-inducible RNA-binding protein (CIRBP) and transient receptor potential vanilloid 4 (TRPV4) were identified as diagnostic biomarkers for OA, and both were positively correlated with mast cell infiltration.