识别关键miRNA并构建心肌缺血再灌注的miRNA‑TF‑mRNA调控网络  

Identification of key microRNA and construction of microRNA‑TF‑mRNA regulatory network for myocardial ischemia reperfusion

作　　者：亚力·亚森 胡振飞[1] 朱阔 程虎[1] Yali·Yasen;Hu Zhenfei;Zhu Kuo;Cheng Hu(Department of Anesthesiology,the First Affiliated Hospital of Xinjiang Medical University,Urumchi 830054,China)

机构地区：[1]新疆医科大学第一附属医院麻醉科,乌鲁木齐830054

出　　处：《国际麻醉学与复苏杂志》2025年第2期130-138,共9页International Journal of Anesthesiology and Resuscitation

基　　金：新疆维吾尔自治区自然科学基金(2019D01C305)。

摘　　要：目的基于基因表达数据库(GEO)运用生物信息学分析筛选与小鼠心肌缺血再灌注相关的微RNA(miRNA)‑转录因子(TF)基因网络。方法从GEO中获取数据集GSE148864,其包含8个缺血再灌注组小鼠样本和6个假手术组小鼠样本。用差异分析软件包(limma)识别两组之间差异表达的微RNA(DEM),再利用DIANA‑miRNA通路分析工具(DIANA‑miRPath)对DEM进行京都基因与基因组百科全书(KEGG)富集分析;绘制受试者操作特征曲线(ROC)评估DEM的诊断价值,并将曲线下面积(AUC)>0.85的DEM鉴定为心肌缺血再灌注损伤(MIRI)中的关键miRNA。使用综合性miRNA靶基因数据库(miRWalk)预测关键miRNA可能调控的信使核糖核酸(mRNA);使用clusterProfiler R软件包分析预测mRNA的生物学功能,并通过enrichplot包(版本1.12.2)中的cnetplot函数构建前5个生物过程‑基因网络和前10个KEGG通路‑基因网络。此外,将具有编码序列靶点的预测mRNA与转录调控网络数据库(TRRUST)中下载提取的TF进行重叠,获得miRNA‑TF调控关系对;同时,利用TRRUST数据库预测受上述TF调控的mRNA,获得TF‑mRNA调控关系对;然后,利用蛋白质‑蛋白质相互作用的数据库(STRING)预测mRNA的相互作用,并根据miRNA之间存在2个及以上的共同靶点mRNA,筛选miRNA‑miRNA调控关系对。最后,提取miRNA‑mRNA、mRNA‑mRNA、miRNA‑miRNA、miRNA‑TF和TF‑mRNA关系对,构建miRNA‑miRNA‑TF‑mRNA‑mRNA调控网络,利用Cytoscape软件将其可视化,并分析其拓扑特征。此外,基于miRNA‑miRNA‑TF‑mRNA‑mRNA调控网络,利用功能模体发现分析(FANMOD)软件检测了三节点和四节点网络模式,保留Zscore最高的子网络类型,提取所有符合的网络模式,绘制miRNA‑TF‑mRNA调控网络。结果在GSE148864数据集中,两组之间共发现了12个DEM,DEM富集到32条KEGG通路,其中AUC>0.85的mmu‑miR‑1843a‑5p、mmu‑miR‑5621‑3p、mmu‑miR‑5113、mmu‑miR‑8116、mmu‑miR‑144‑5p、Objective To screen for microRNA(miRNA)‑transcription factor(TF)gene networks related to myocardial ischemia reperfusion in mice based on the Gene Expression Omnibus(GEO)using bioinformatics analysis.Methods The dataset GSE148864 was obtained from GEO,which includes mouse samples from 8 ischemia reperfusion groups and 6 sham operation groups.Differential expression microRNA(DEM)between the two groups were identified by the limma package.The resultant DEM were then subjected to Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis using the DIANA‑miRNA pathway analysis tool(DIANA‑miRPath).Receiver operating characteristic(ROC)curves were plotted to evaluate the diagnostic value of DEM,and DEM with an area under the curve(AUC)>0.85 were identified as key miRNA in myocardial ischemia reperfusion injury(MIRI).The comprehensive miRNA target gene database(miRWalk)was used to predict messenger ribonucleic acids(mRNA)potentially regulated by the key miRNA.The clusterProfiler R package was used to analyze the biological functions of the predicted mRNA,and the cnetplot function in the enrichplot package(version 1.12.2)was used to construct the top 5 biological process‑gene networks and the top 10 KEGG pathway‑gene networks.Additionally,predicted mRNA with coding sequence targets were overlapped with TF extracted from the TRRUST database to obtain miRNA‑TF regulatory pairs.Meanwhile,mRNA regulated by these TF were predicted using the TRRUST database to obtain TF‑mRNA regulatory pairs.The STRING database was used to predict mRNA interactions,and miRNA‑miRNA regulatory pairs were screened based on the presence of two or more common target mRNA between the miRNA.Finally,miRNA‑mRNA,mRNA‑mRNA,miRNA‑miRNA,miRNA‑TF,and TF‑mRNA relationship pairs were extracted to construct a miRNA‑miRNA‑TF‑mRNA‑mRNA regulatory network,which was visualized by Cytoscape software and its topological features were analyzed.Furthermore,based on the miRNA‑miRNA‑TF‑mRNA‑mRNA regulatory network,the FANM

关 键 词：心肌缺血再灌注 微RNA 转录因子 微RNA‑转录因子‑信使核糖核酸调控网络 

分 类 号：R54[医药卫生—心血管疾病]