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• W4293537731 abstract "Background: Osteoarthritis (OA) is a major factor causing pain and disability. Studies performed to date have suggested that synovitis is possibly a critical OA-related pathological change. Ferroptosis represents a novel type of lipid peroxidation-induced iron-dependent cell death. However, its effect on OA remains largely unclear. Objective: This work focused on identifying and validating the possible ferroptosis-related genes (FRGs) involved in synovitis of OA through bioinformatics analysis. Materials and Methods: The microarray dataset GSE55235 was downloaded in the database Gene Expression Omnibus (GEO). By the Venn diagram and GEO2R, differentially expressed genes (DEGs) and ferroptosis DEGs (FDEGs) were detected. DEGs were screened by GO and KEGG enrichment analysis, as well as protein-protein interaction (PPI) analysis. Besides, the software Cytoscape and database STRING were utilized to construct hub gene networks. Moreover, this study used the database NetworkAnalyst to predict the target miRNAs of the hub genes. Finally, the hub genes were confirmed by analysis of the receiver operating characteristic (ROC) curve on the GSE12021 and GSE1919 databases. Considering the relationship between ferroptosis and immunity, this study applied CIBERSORTx to analyze the immune infiltration in OA in addition. Results: This work discovered seven genes, including ATF3, IL6, CDKN1A, IL1B, EGR1, JUN, and CD44, as the hub FDEGs. The ROC analysis demonstrated that almost all hub genes had good diagnostic properties in GSE12021 and GSE 1919. Conclusion: This study discovered seven FDEGs to be the possible diagnostic biomarkers and therapeutic targets of synovitis during OA, which sheds more light on the pathogenesis of OA at the transcriptome level." @default.
• W4293537731 created "2022-08-30" @default.
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• W4293537731 date "2022-08-29" @default.
• W4293537731 modified "2023-10-14" @default.
• W4293537731 title "Identification and verification of ferroptosis-related genes in the synovial tissue of osteoarthritis using bioinformatics analysis" @default.
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• W4293537731 doi "https://doi.org/10.3389/fmolb.2022.992044" @default.
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