Identification of Ion Channel-Related Genes as Diagnostic Markers and Potential Therapeutic Targets for Osteoarthritis

Abstract

Abstract Osteoarthritis (OA) is a debilitating joint disorder characterized by the progressive degeneration of articular cartilage. Although the role of ion channels in OA pathogenesis is increasingly recognized, diagnostic markers and targeted therapies remain limited. In this study, we analyzed the GSE48556 dataset to identify differentially expressed ion channel-related genes (DEGs) in OA and normal controls. We identified a total of 47 DEGs, with the majority involved in transient receptor potential (TRP) pathways. To select potential diagnostic markers, we employed machine learning algorithms, LASSO and SVM-RFE, and identified seven genes (CHRNA4, GABRE, HTR3B, KCNG2, KCNJ2, LRRC8C, and TRPM5) as the best characteristic genes for distinguishing OA from healthy samples. The differential expression of these seven marker genes was validated, and gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to explore their involvement in biological pathways. We performed clustering analysis and identified two distinct subtypes of OA, C1 and C2, with differential gene expression and immune cell infiltration profiles. Using weighted gene co-expression network analysis (WGCNA), we identified three key genes (PPP1R3D, ZNF101, and LOC651309) associated with OA. We constructed a prediction model using these genes and validated it using the GSE46750 dataset, demonstrating reasonable accuracy and specificity. Our findings provide novel insights into the role of ion channel-related genes in OA pathogenesis and offer potential diagnostic markers and therapeutic targets for the treatment of OA.