摘要： 目的 基于基因表达综合（GEO）数据库，采用生物信息学方法挖掘肥厚型心肌病相关的差异表达基因（DEG），为肥厚型心肌病的临床治疗提供新思路。方法 从GEO数据库中筛选肥厚型心肌病患者和正常对照者的基因数据集芯片GSE68316和GSE148602，应用RStudio软件和GEO数据库基因表达分析工具（GEO2R），以|log₂FC|≥ 1且P < 0.05作为筛选标准，筛选数据集中的DEG。选取2个数据集中差异表达最显著的上调和下调基因各10个，分别绘制热图。通过R语言完成关键DEG的基因本体论（GO）以及京都基因和基因组数据库（KEGG）分析。结果 在GSE68316数据集中共筛选出247个DEG，包括125个表达上调的DEG和122个表达下调的DEG；在GSE148602数据集中共筛选出157个DEG，包括44个表达上调的DEG和113个表达下调的DEG。KEGG和GO分析中存在多条通路的富集。结论 通过生物信息学方法，可以有效挖掘肥厚型心肌病DEG，为后续治疗提供新策略。

基于GEO数据库的肥厚型心肌病差异表达基因分析

王潇, 杨智勇

中国医科大学附属盛京医院心血管内科, 沈阳 110004

收稿日期:2022-11-30出版日期:2023-04-30发布日期:2023-04-15
通讯作者:王潇E-mail:wx787119851@126.com
作者简介:王潇(1993-),男,医师,博士研究生.
基金资助:辽宁省应用基础研究计划联合计划项目（2022JH2/101500075）


关键词: 基因表达综合数据库, 肥厚型心肌病, 差异表达基因
Abstract: Objective To explore the differentially expressed genes (DEGs) of hypertrophic cardiomyopathy using bioinformatics based on the Gene Expression Omnibus (GEO) database and provide new ideas for improving treatment for hypertrophic cardiomyopathy. Methods Patients with hypertrophic cardiomyopathy and normal controls were screened from the GSE68316 and GSE148602 datasets. The DEGs in the datasets were screened using the criteria |log₂FC| ≥ 1 and P < 0.05 through the RStudio software and GEO database gene expression analysis tool (GEO2R). Both were selected to draw heat maps. Key DEGs were analyzed through complete gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) using R language. Results A total of 247 DEGs were screened from the GSE68316 dataset, including 125 upregulated and 122 downregulated DEGs, while 157 DEGs were screened from GSE148602 dataset, including 44 upregulated and 113 downregulated DEGs. Multiple pathways were enriched in the KEGG and GO analysis. Conclusion The bioinformatics method can effectively explore the DEGs of hypertrophic cardiomyopathy, which may provide a new strategy for the treatment of hypertrophic cardiomyopathy.
Key words: Gene Expression Omnibus database, hypertrophic cardiomyopathy, differentially expressed gene
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