基于GEO数据库筛选妊娠糖尿病关键基因及其诊断价值分析

doi:10.3969/j.issn.1673-8640.2023.03.014

摘要/Abstract

摘要： 目的应用生物信息学方法筛选妊娠糖尿病（GDM）关键基因，并探讨其诊断价值。方法从基因表达数据库（GEO）下载与GDM相关的微阵列数据（GSE103552数据集）。采用R软件中的“limma”程序包筛选对照组和GDM组差异表达基因，使用R软件中的“Clusterprofiler”程序包对差异表达基因进行基因本体（GO）和京都基因与基因组数据库（KEGG）富集分析，使用WGCNA输出模块基因，取模块基因与差异表达基因的交集确定特征差异基因。通过STRING在线网站和Cytoscape软件构建特征差异基因蛋白互作网络，并筛选关键基因。采用R软件分析关键基因表达水平，采用受试者工作特征（ROC）曲线评价关键基因诊断GDM的效能。结果 GSE103552数据集包含17例对照者样本和20例GDM患者样本，共筛选出118个差异表达基因，其中表达上调65个、表达下调53个；差异表达基因多富集于细胞外基质组织、细胞外结构组织和DNA构象变化反应，主要调控RNA转运和味觉转导等通路。共确定33个特征差异基因。通过蛋白互作网络确定的5个关键基因（ANKRD36C、CLK1、LUC7L3、NKTR和RSRP1）均在GDM患者中呈高表达，诊断GDM的曲线下面积均>0.8。结论基于GEO数据库共获得5个与GDM相关的高表达关键基因，为GDM诊断、预后和治疗提供了新方向。

关键词: 妊娠糖尿病, 基因表达数据库, 关键候选基因, 诊断标志物

Abstract: Objective To screen hub genes for gestational diabetes mellitus（GDM） by bioinformatics. Methods Microarray data related to GDM were downloaded from gene expression omnibus（GEO）database（GSE103552）. Limma package was used to screen the differentially expressed genes of control group and GDM group. Clusterprofiler was used for Gene Ontology（GO） and Kyoto Encyclopedia of Genes and Genomes（KEGG） enrichment analysis. WGCNA outputed module genes were used，and the intersection with modular genes and differentially expressed genes was used to determine the characteristic difference genes. The online website STRING and Cytoscape software were used to construct the protein interaction network for the characteristic difference genes，and the hub genes were screened. R software was used to analyze the expression and diagnostic value of hub genes. Receiver operating characteristic（ROC） curve was used to evaluate the diagnostic performance of hub genes. Results The GDM-related microarray matrix GSE103552 included 17 controls and 20 GDM samples. There were 118 differentially expressed genes，including 65 up-regulated cases and 53 down-regulated cases. Differentially expressed genes were enriched in extracellular matrix tissues，extracellular structural tissues and DNA conformation change response，and they regulated RNA transport and taste transduction. Totally，33 characteristic difference genes. Protein interaction network construction identified 5 hub genes（ANKRD36C，CLK1，LUC7L3，NKTR and RSRP1），the 5 hub genes were highly expressed in differentially expressed genes，and the area under curve for diagnosing GDM was >0.8. Conclusions Based on GEO database，the 5 hub genes are highly expressed in differentially expressed genes，providing potential diagnostic，prognostic markers and therapeutic targets for GDM.

Key words: Gestational diabetes mellitus, Gene expression omnibus database, Hub genes, Diagnostic marker

中图分类号:

R446.1

陈慧, 陈光辉, 梁映亮, 王万党, 尹志军. 基于GEO数据库筛选妊娠糖尿病关键基因及其诊断价值分析[J]. 检验医学, 2023, 38(3): 276-281.

CHEN Hui, CHEN Guanghui, LIANG Yingliang, WANG Wandang, YIN Zhijun. Screening hub genes and diagnostic analysis of gestational diabetes mellitus based on GEO database[J]. Laboratory Medicine, 2023, 38(3): 276-281.

图/表 5

图1 GDM组和对照组差异表达基因的筛选注：（a）GDM组和对照组差异表达基因热图；对照组； GDM组；（b）GDM组和对照组差异表达基因火山图。

图2 GDM差异表达基因涉及的生物学功能注：（a）表达上调的差异基因GO富集分析；（b）表达下调的差异基因GO富集分析；（c）表达上调的差异基因KEGG通路富集分析；（d）表达下调的差异基因KEGG通路富集分析。

图3 GDM组和对照组WGCNA分析注：（a）样本聚类分析；（b）样本聚类特征热图；（c）网络拓扑分析；（d）共表达网络构建；（e）样本特征与模块的相关性分析[r值（P值）]；（f）与GDM临床特征最相关的模块基因。

图4 关键基因筛选注：（a）模块基因与差异表达基因的交集；（b）关键基因。

图5 对照组和GDM组关键基因相对表达量比较及其诊断GDM的ROC曲线

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