Construction of ferroptosis-related gene signatures for identifying potential biomarkers and immune cell infiltration in osteoarthritis

Abstract

Osteoarthritis (OA) is a comprehensive joint disorder. The specific genes that trigger OA and the strategies for its effective management are not fully understood. This study focuses on identifying key genes linked to iron metabolism that could influence both the diagnosis and therapeutic approaches for OA. Analysis of GEO microarray data and iron metabolism genes identified 15 ferroptosis-related DEGs, enriched in hypoxia and HIF-1 pathways. Ten key hub genes (ATM, GCLC, PSEN1, CYBB, ATG7, MAP1LC3B, PLIN2, GRN, APOC1, SIAH2) were identified. Through stepwise regression, we screened 4 out of the above 10 genes, namely, GCLC, GRN, APOC1, and SIAH2, to obtain the optimal model. AUROCs for diagnosis of OA for the four hub genes were 0.81 and 0.80 of training and validation sets, separately. According to immune infiltration results, OA was related to significantly increased memory B cells, M0 macrophages, regulatory T cells, and resting mast cells but decreased activated dendritic cells. The four hub genes showed a close relation to them. It is anticipated that this model will aid in diagnosing osteoarthritis by assessing the expression of specific genes in blood samples. Moreover, studying these hub genes may further elucidate the pathogenesis of osteoarthritis.

Keywords

• Osteoarthritis
• ferroptosis
• immune infiltration
• weighted correlation network analysis
• gene set enrichment analysis