Identification and verification of ferroptosis-related genes in the synovial tissue of osteoarthritis using bioinformatics analysis

利用生物信息学分析鉴定和验证骨关节炎滑膜组织中铁死亡相关基因

阅读:1

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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。