| 引用本文: | 窦思佳,丁志祥,仲兴亚,朱博,沈鹏飞.骨关节炎糖酵解相关基因的筛选与验证[J].生物信息学,2025,23(3):184-191. |
| DOU Sijia,DING Zhixiang,ZHONG Xingya,ZHU Bo,SHEN Pengfei.Screening and validation of glycolysis-related genes in osteoarthritis[J].Chinese Journal of Bioinformatics,2025,23(3):184-191. |
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| 摘要: |
| 骨关节炎(Osteoarthritis,OA)导致的疼痛和残疾已经极大地影响了患者的生活质量。近期许多研究表明,骨关节炎与糖酵解之间有密切关联。然而,糖酵解对OA的影响在很大程度上仍不清楚。为此,通过生物信息学分析鉴定和验证可能参与OA滑膜炎的糖酵解相关基因。从Gene Expression Omnibus(GEO)数据库中选择微阵列数据集GSE55235。通过维恩图和NetworkAnalyst筛选糖酵解表型差异基因。通过GO和KEGG富集分析以及蛋白质-蛋白质相互作用(PPI)分析筛选出表型差异基因。此外,利用Cytoscape软件和数据库STRING构建枢纽基因网络。然后通过分析GSE206848数据库上的受试者工作特征(ROC)曲线,确认了枢纽基因。考虑到糖酵解与免疫的关系,应用CIBERSORTx分析了OA中的免疫浸润。研究发现了4个基因,包括表皮生长因子受体(Epidermal growth factor receptor,EGFR)、血管内皮生长因子A(Vascular endothelial growth factor A,VEGFA)、Egl-9家族缺氧诱导因子3(Egl nine homolog 3,EGLN3)和DNA损伤诱导转录子4(DNA Damage Inducible Transcript 4,DDIT4),为枢纽基因。ROC分析表明,几乎所有枢纽基因在GSE206848中都具有良好的诊断特性。本研究发现4个表型差异基因是OA期间滑膜炎潜在的诊断生物标志物和治疗靶点,这也许能完善OA的发病机制。 |
| 关键词: 差异表达基因 糖酵解 骨关节炎 |
| DOI：10.12113/202404013 |
| 分类号:Q343.1 |
| 文献标识码:A |
| 基金项目: |
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| Screening and validation of glycolysis-related genes in osteoarthritis |
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DOU Sijia, DING Zhixiang, ZHONG Xingya, ZHU Bo, SHEN Pengfei
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(Changzhou Hospital Affiliated to Nanjing University of Chinese Medicine,Changzhou 213004,Jiangsu, China)
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| Abstract: |
| Osteoarthritis significantly compromises patients quality of life due to the associated pain and disability. A growing body of evidence underscores a robust association between osteoarthritis (OA) and glycolysis, yet its influence on OA largely remains to be elucidated. This research aimed to identify and authenticate glycolysis-associated genes potentially implicated in OA synovitis through a comprehensive bioinformatic approach. The microarray dataset GSE55235 was extracted from the Gene Expression Omnibus(GEO) database. Glycolytic phenotype-specific differentially expressed genes were identified via a Venn diagram and NetworkAnalyst. Phenotypic variants were further screened using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, in addition to protein-protein interaction (PPI) analysis. A hub gene network was established utilizing Cytoscape software and the STRING database. Hub genes were pinpointed via Receiver Operating Characteristic (ROC) curve analysis of the GSE206848 database. Given the established interplay between glycolysis and immunity, this investigation employed CIBERSORTx to assess immune infiltration in OA. This study discerned four genes, namely EGFR, VEGFA, EGLN3, and DDIT4, as critical to the investigation. ROC analysis confirmed that the majority of these key genes exhibited reliable diagnostic attributes in the GSE206848 dataset. This research unveiled four phenotypically unique genes as potential diagnostic markers and therapeutic targets for synovitis in OA, thereby offering new insights into the diseases pathogenesis. |
| Key words: Differentially expressed genes Glycolysis Osteoarthritis |
