首页|期刊导航|江汉大学学报(自然科学版)|基于生物信息学分析膝骨关节炎差异表达基因并筛选潜在核心标志物

基于生物信息学分析膝骨关节炎差异表达基因并筛选潜在核心标志物OA

Bioinformatics-based Analysis of Differentially Expressed Genes in Knee Osteoarthritis and Screening of Potential Core Markers

中文摘要英文摘要

目 的 基于生物信息学方法探讨膝骨关节炎(knee osteoarthritis,KOA)中的枢纽基因及其可能影响预后的潜在分子靶点.方 法 从Gene Expression Omnibus(GEO)数据库中下载了 5 个有关的数据集,分别是GSE12021(其中包含 9 个正常对照组和 9 个KOA组),GSE55235(其中包含 10个正常对照组和 9个KOA组),GSE82107(其中包含 7个正常对照组和 10个KOA组),GSE29746(其中包含 11个正常对照组和 11个KOA组)以及GSE55457(其中包含 10个正常对照组和 10 个KOA组).使用R语言中的Limma包对差异表达基因(differentially expressed genes,DEGs)进行筛选,并借助R包绘制热图和火山图进行可视化分析.此外,进行了交叉分析,确定了与KOA相关的DEGs,并对这些基因进行了Gene Ontology(GO)和Kyoto Encyclope-dia of Genes and Genomes(KEGG)富集分析,以探索这些DEGs的生物学功能和潜在的分子机制.借助STRING数据库构建蛋白质-蛋白质相互作用(protein-protein interaction,PPI)网络,并运用了Cytoscape 3软件里的CytoHubba、MCODE和CytoNCA这三个工具来确定KOA的核心基因.为了进一步确认核心基因可靠性,在接收操作特征(receiver operating characteristic,ROC)曲线进行了测试,在 5 个不同的数据集(包含 GSE12021、GSE55235、GSE82107、GSE29746 和GSE55457)中去检验本研究的预测效果.结 果 在KOA数据集中,共鉴定出 238个与KOA相关的DEGs.经过ROC曲线分析,有 6个基因(ATF3、IL-6、JUN、PTGS2、MYC、SOCS3)在所有数据集(GSE12021、GSE55235、GSE82107、GSE29746、GSE55457)的曲线下面积(AUC)均>0.7,显示这些核心基因具有良好的诊断价值.这些基因可能在KOA的发病及预后过程中起重要作用,并具有作为潜在治疗靶点的可能性.结 论 ATF3、IL-6、JUN、PTGS2、MYC、SOCS3等基因可能参与KOA的发病及预后过程,这些基因有望成为新的治疗靶点.通过STRING数据库构建的PPI网络,进一步揭示了这些基因之间的复杂相互作用,为KOA的分子机制研究提供了新的见解.基于Cytoscape 3的多种算法筛选出的核心基因为后续的临床研究和靶向治疗提供了理论依据.最后,ROC曲线验证结果表明,这些基因在KOA的诊断中具有潜在的应用价值.

Objective To identify key genes and explore their potential molecular targets related to prognosis in knee osteoarthritis(KOA)by using bioinformatics approaches.Methods Gene expression datasets were obtained from the Gene Expression Omnibus(GEO)database,including GSE12021(9 normal controls and 9 KOA samples),GSE55235(10 normal controls and 9 KOA samples),GSE82107(7 normal controls and 10 KOA samples),GSE29746(11 normal controls and 11 KOA samples),and GSE55457(10 normal controls and 10 KOA samples).Differentially expressed genes(DEGs)were identified using the Limma package in R,and their expression patterns were visualized through heatmaps and volcano plots.Cross-analysis was conducted to determine KOA-associated DEGs.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses were performed to explore the biological roles and potential mechanisms of these genes.A protein-protein interaction(PPI)network was constructed using the STRING database,and core genes were identified using three algorithms(CytoHubba,MCODE,and CytoNCA)within Cytoscape 3 software.To further validate the reliability of the core genes,receiver operating characteristic(ROC)curve analysis was employed,and predictive performance was examined across the five datasets(GSE12021,GSE55235,GSE82107,GSE29746,GSE55457).Results A total of 238 KOA-related DEGs were identified.ROC curve analysis showed that six genes(ATF3,IL-6,JUN,PTGS2,MYC,and SOCS3)showed an area under the curve(AUC)greater than 0.7 across all datasets,indicating their strong diagnostic potential.These genes are likely to play critical roles in the pathogenesis and prognosis of KOA and may represent novel therapeutic targets.Conclusion ATF3,IL-6,JUN,PTGS2,MYC,and SOCS3 and other genes are involved in the pathogenesis and prognosis of KOA,and may serve as novel therapeutic targets.The PPI network constructed through the STRING database further revealed the complex interactions between these genes and provided new insights into the molecular mechanism of KOA.The core genes screened based on various algorithms of Cytoscape 3 provide a theoretical basis for subsequent clinical research and targeted therapy.Finally,ROC validation confirmed the potential diagnostic utility of these genes in KOA.

李再文

江汉大学 医学部,湖北 武汉 430056

医药卫生

膝骨关节炎生物信息学GEO核心基因

knee osteoarthritisbioinformaticsGEOcore gene

《江汉大学学报(自然科学版)》 2026 (2)

59-68,10

10.16389/j.cnki.cn42-1737/n.2026.02.006

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