Identification of Ferroptosis Genes Associated with Osteoarthritis Based on Bioinformatics and Machine Learning

Background: Osteoarthritis (OA) is the most common joint disease, and ferroptosis is a newly discovered form of cell death linked to the pathogenesis of OA, suggesting that ferroptosis-related genes may serve as potential biomarkers. Methods: This study set out to identify differentially expressed ferroptosis-related genes (Ferr-DEGs) associated with OA by looking at the GSE55235 microarray dataset from the GEO database. We employed machine learning algorithms, along with GO and KEGG enrichment analyses, to explore the biological functions and pathways of these genes. Results: We identified a total of 119 Ferr-DEGs, and three ferroptosis-related genes, IDH2, PLIN2, and KLF2, were ultimately determined as diagnostic biomarkers for OA. We confirmed high diagnostic accuracy using ROC curve analysis. Furthermore, an analysis of immune cell infiltration showed strong links between these genes and different types of immune cells, indicating their potential roles in the immune microenvironment of OA. Conclusion: Our findings indicate that IDH2, PLIN2, and KLF2 play a key role in OA development and could serve as promising diagnostic biomarkers. Future studies with larger sample sizes will be needed to validate the roles of these ferroptosis-related genes in OA and their potential as therapeutic targets.