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中国医药导刊 ›› 2024, Vol. 26 ›› Issue (11): 1149-1154.

• 医药信息学 • 上一篇    下一篇

基于GEO数据库对系统性红斑狼疮差异基因的筛选和生物信息学分析

马小静, 郭志祥*   

  1. 浙江大学医学院附属第四医院,国际医学院,国际健康医学研究院,检验科,浙江 义乌 322000
  • 收稿日期:2024-08-23 修回日期:2024-11-25 出版日期:2024-11-28 发布日期:2024-11-28
  • 基金资助:
    金华市公益性技术应用研究项目(2024-4-269)

Screening and Bioinformatics Analysis of Differentially Expressed Expressed Genes in Systemic Lupus Erythematosus Based on GEO Database

  1. Department of Laboratory the Fourth Affiliated Hospital of School of Medicine and International School of Medicine
    International Institutes of Medicine Zhejiang Yiwu 322000, China
  • Received:2024-08-23 Revised:2024-11-25 Online:2024-11-28 Published:2024-11-28

摘要:

目的:通过分析基因表达综合数据库(GEO)中系统性红斑狼疮(SLE)患者外周血单个核细胞(PBMC)的基因数据集,探索新的能够辅助诊断SLE的生物标志物。方法:从GEO数据库中下载诊断为SLE的患者和健康志愿者的基因芯片数据,筛选出两者差异表达基因(DEGs)。利用DAVID数据库对DEGs进行基因本体(GO)富集分析和京都基因与基因组百科全书(KEGG)信号通路富集分析。根据STRING数据库构建差异基因蛋白质互作网络(PPI),通过Cytoscape软件中MCODE插件对其结果进行可视化分析,并使用CytoHubba插件筛选关键基因(hub gene)。结果:共采用2个平台的SLE患者和健康志愿者的基因芯片,筛选出共同DEGs230个,包括62个上调基因和168个下调基因。通过富集分析表明,DEGs在生物学过程中主要聚集在免疫反应;在细胞组成方面整合在细胞外间隙;在分子功能上主要发挥参与抗原和受体结合的作用。KEGG通路分析显示,DEGs涉及转录调节、细胞周期等信号通路。最终筛选出10个关键基因,分别为RRM2UBE2CKIFC1HJURPCDCA5CENPACDC45CDC25CCDC25AORC1。结论:通过生物信息学筛选的10个关键基因参与SLE的发生发展,且聚集在细胞周期信号通路,其或可成为辅助诊断SLE的潜在靶点,为后续研究和治疗提供新方向。


关键词: 基因表达综合数据库, 系统性红斑狼疮, 差异表达基因, 关键基因

Abstract:

Objective: To explore potential biomarkers for the diagnosis of systemic lupus erythematosus SLE by analyzing gene expression data from peripheral blood mononuclear cells PBMCs of SLE patients in the Gene Expression Omnibus GEO database.Methods: DNA microarray data of SLE patients and the healthy control group were downloaded from the GEO database and differentially expressed genes DEGs were identified. Gene Ontology GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes KEGG pathway enrichment analysis were performed using the DAVID database. The protein-protein interaction PPI network for the DEGs was constructed based on the STRING database and the results were visualized using the MCODE plugin in Cytoscape. Key genes hub genes were further identified using the CytoHubba plugin.Results: A total of 230 common DEGs including 62 upregulated genes and 168 downregulated genes were identified from the data sets of SLE patients and the healthy control group across two platforms. GO enrichment analysis revealed that the DEGs were primarily associated with immune response. In terms of cellular components' integration in the extracellular space. In terms of molecular functions the DEGs were mainly involved in antigen and receptor binding. KEGG pathway analysis showed that the DEGs were implicated in transcriptional regulation cell cycle and other signaling pathways. Ten key genes were selected including RRM2 UBE2C KIFC1 HJURP CDCA5 CENPA CDC45 CDC25C CDC25A and ORC1.Conclusion: The 10 key genes identified through bioinformatics analysis play a role in the development of SLE and are primarily involved in cell cycle-related signaling pathways. These genes may serve as potential targets for assisting in the diagnosis of SLE and provide new directions for future research and treatment strategies.


Key words: Gene expression omnibus , Systemic lupus erythematosus , Differentially expressed gene , Key gene

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