Integrating multiple microarray datasets to explore the significance of ferroptosis regulators in the diagnosis and subtype classification of osteoarthritis.

Abstract

Osteoarthritis (OA) is a chronic joint disease that reduces quality of life for patients. Ferroptosis plays a significant role in OA. However, its underlying mechanism remains unclear. In this study, we integrated 7 OA synovial datasets from the GEO database to screen for significant ferroptosis-related genes. The top 5 ferroptosis regulators were used to construct nomogram models to predict OA prevalence. Consensus clustering was applied to classify OA patients into different ferroptosis patterns based on significant ferroptosis-related genes. Subsequently, an immune cell infiltration study was performed to investigate the relationship between the significant ferroptosis regulators and immune cells. As a result, we screened 11 ferroptosis-related genes in OA patients. Five candidate ferroptosis regulators (SLC7A11, ALOX5, SLC1A5, GOT1, and GSS) were used to predict OA risk. The nomogram model based on these 5 genes is important for assessing the occurrence of OA. Consensus clustering analysis showed that OA patients could be classified into 2 ferroptosis patterns (Clusters A and B). Immune cell infiltration levels were higher in Cluster B than in Cluster A. Two subtypes, gene Clusters A and B, were classified according to the expression of ferroptosis-related DEGs among the ferroptosis patterns. Cluster A and gene Cluster A had higher ferroptosis scores than Cluster B or gene Cluster B, whereas the expression levels of the proinflammatory cytokines interleukin (IL)-1β, tumor necrosis factor, IL-6, IL-18, and IL-10 were higher in Cluster B or gene Cluster B than those in Cluster A or gene Cluster A. Different subtypes of ferroptosis play critical roles in OA. Furthermore, immunotherapy strategies for OA treatment may be guided by our study on ferroptosis patterns.