Identification and verification of novel ferroptosis genes in osteoarthritis synovial tissue through bioinformatics analysis

Abstract

Abstract Background: Osteoarthritis (OA) is a leading cause of pain and disability. Previous studies have indicated that synovitis may play a crucial role in OA-related pathological changes. Ferroptosis, a form of iron-dependent cell death induced by lipid peroxidation, is a novel mechanism that has not been extensively studied in the context of OA. Objective: This study aimed to identify and validate novel potential ferroptosis-related genes (FRGs) associated with synovitis in OA using bioinformatics analysis. Materials and Methods: The microarray dataset GSE55457 from the Gene Expression Omnibus (GEO) database was utilized to identify differentially expressed genes (DEGs). Further analysis involved screening DEGs using GO and KEGG enrichment analysis, as well as Immune cell infiltration analysis. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed to identify meaningful modules and hub genes within these modules. Furthermore, an intersection analysis was conducted to compare these hub genes with ferroptosis genes( FDEGs) to acquire novel hub FDEGs. The diagnostic potential of the hub FDEGs were verficated through receiver operating characteristic (ROC) curve and expression levels analysis using the GSE55235 databases. Results: Elevene genes, namely SLC2A14,SLC7A5,NR4A1,ZFP36,MEG3,CDKN1A,VEGFA, TNFAIP3,LRRFIP1 and CAPG, were identified as hub FDEGs. SLC2A14,SLC7A5 and MEG3 has not been reported before.The ROC analysis indicated that SLC2A14 and SLC7A5 exhibited strong diagnostic properties in GSE55235 with significant differences in expression levels Conclusion: This study identified two novel FDEGs as potential diagnostic biomarkers and therapeutic targets for synovitis in OA, providing valuable insights into the transcriptome-level pathogenesis of OA.