基于加权基因共表达网络分析挖掘ECMO治疗后心源性休克结局的核心枢纽基因

王建茹; 李彬; 彭广操; 于瑞; 王新陆; 赵齐飞; 王永霞; 朱明军

doi:10.13201/j.issn.1001-1439.2021.05.009

基于加权基因共表达网络分析挖掘ECMO治疗后心源性休克结局的核心枢纽基因

- 河南中医药大学第一附属医院心内科(郑州,450000)
基金项目:
河南省科技攻关项目(No:202102310492)

河南省中医药科学研究专项课题(No:2019JDZX2013、20-21ZY2187)

国家重点研发计划“中医药现代化研究”重点专项(No:2019YFC1710000、2019YFC1710001、2019YFC1710002、2019YFC1710003)

中医药循证能力建设项目(No:2019XZZX-XXG003)

中原院士基金-中原名医

详细信息

通讯作者: 朱明军,E-mail:zhumingjun317@163.com

中图分类号: R541.64

收稿日期: 2020-10-13

修回日期: 2020-12-18

Identification of core hub genes of cardiogenic shock outcome after ECMO treatment based on weighted gene co-expression network analysis

- Department of Cardiology,The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, 450000, China

More Information

Corresponding author: ZHU Mingjun, E-mail: zhumingjun317@163.com

Received Date: 13 October 2020

Revised Date: 18 December 2020

摘要

摘要: 目的:利用加权基因共表达网络分析(WGCNA)法挖掘影响体外膜肺氧合(ECMO)治疗后心源性休克结局(outcome)的潜在核心枢纽基因。方法:首先,从GEO数据库下载GSE93101数据,并对数据进行预处理。其次,运用R中的WGCNA软件包来构建共表达网络,识别基因共表达模块并与临床性状进行关联,计算基因显著性(GS)和模块身份(MM),确定与outcome这一临床性状相关性最强的关键模块。然后,对关键模块内所有基因进行基因集富集分析(GSEA),同时依据|MM|、|GS|和Cytoscape软件分析出的网络拓扑分析结果筛选关键模块内的枢纽基因。最后,利用limma包对GSE93101数据集中outcome成功和失败两组进行差异基因表达分析,并将筛选出的差异表达基因(DEGs)与枢纽基因取交集,获取核心枢纽基因,并在GSE93101数据集中分别对核心枢纽基因开展单基因GSEA信号通路的富集分析。结果:本研究在GSE93101数据集中发现有26个共表达模块,其中关键模块darkseagreen 4与outcome显著相关。关键模块内所有基因的GSEA显示,这些基因主要与细胞运动、有机酸分解代谢过程等5个生物学功能相关,参与了花生四烯酸代谢等9条信号通路。本研究从关键模块中筛选出4个枢纽基因,从GSE93101数据集中筛选出与失败outcome相关的DEGs128个,其中97个上调,31个下调,进一步将DEGs与枢纽基因取交集后共确定3个核心枢纽基因即表达上调的脂肪酸结合蛋白1(FABP1)、丙氨酸-乙醛酸盐和丝氨酸-丙酮酸转氨酶(AGXT)、间α-胰蛋白酶抑制剂重链H2(ITIH2)。单基因GSEA显示,3个核心枢纽基因高表达组可共同富集到花生四烯酸代谢、PPAR信号通路等29条信号通路。结论:本研究发现核心枢纽基因FABP1、AGXT、ITIH2的高表达可能是预测ECMO治疗CS失败的潜在生物标记物,为临床决策提供一定的参考。
- 心源性休克 /
- 体外膜肺氧合 /
- 加权基因共表达网络分析 /
- 生物信息学
Abstract: Objective: To identify the core hub genes of cardiogenic shock outcome after ECMO treatment using weighted gene co-expression network analysis(WGCNA).Methods: Firstly, GSE93101 data set was downloaded from GEO database and preprocessed. Secondly, the WGCNA software package in R was used to construct coexpression network, identify the gene coexpression modules and correlate with clinical phenotype, calculate the gene significance(GS) and module identity(MM), and then determine the key modules with the strongest correlation with outcome. Thirdly, gene set enrichment analysis(GSEA) of all genes in key modules were carried out, and the hub genes in key modules were screened according to the network topology analysis results analyzed by Cytoscape software and |MM|, |GS|. Finally, the limma package was used to analyze the differential genes expression between the successful and failed groups in GSE93101 data set, and the selected differential expression genes(DEGs) were intersected with hub genes to obtain the core hub genes. Meanwhile, the enrichment analysis of single gene GSEA signaling pathway of core hub genes were carried out in GSE93101 data set.Results: In this study, 26 coexpression modules were found in GSE93101 data set, and darkseagreen4, the key module, was significantly correlated with outcome. The GSEA results of all genes in key modules showed that the genes were mainly related to 5 biological functions including cell movement and organic acid catabolism, and participated in 9 signaling pathways including arachidonic acid metabolism. Four hub genes were selected from key module, and 128 DEGs related to failed outcome were selected from GSE93101 data set, of which 97 DEGs were up-regulated and 31 DEGs were down-regulated. After further intersecting DEGs with hub genes, the up-regulated core hub genes FABP1, AGXT and ITIH2 were identified in failed group. Single gene GSEA showed that 29 signaling pathways including arachidonic acid metabolism, PPAR signaling pathway were enriched jointly in the high expression group of three core hub genes.Conclusion: In this study, we found that the high expression of core hub genes FABP1, AGXT and ITIH2 may be a potential biomarker for predicting the failure of ECMO in treating CS, which can provide some reference for clinical decision-making.
- cardiogenic shock /
- extracorporeal membrane oxygenation /
- weighted gene co-expression network analysis /
- bioinformatics

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