基于免疫相关基因和加权共表达网络分析的缺血性脑卒中分子亚型鉴定

IF 1.9 4区 生物学 Q4 CELL BIOLOGY
Duncan Wei, Xiaopu Chen, Jing Xu, Wenzhen He
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引用次数: 0

摘要

据报道,免疫系统在缺血性卒中(IS)的发展中起着关键作用。然而,其确切的免疫相关机制尚未完全揭示。从Gene expression Omnibus数据库下载IS和健康对照的基因表达数据,得到差异表达基因(differential expression genes, DEGs)。免疫相关基因(IRGs)数据从import数据库下载。基于IRGs和加权共表达网络分析(WGCNA)鉴定IS的分子亚型。在IS中共获得827个deg和1142个irg。基于1142个IRGs, 128个IS样本被聚为两个分子亚型:clusterA和clusterB。基于WGCNA,作者发现蓝色模块与IS的相关性最高。在蓝色模块中,筛选了90个基因作为候选基因。在蓝色模块中所有基因的蛋白-蛋白相互作用网络中,根据基因程度选取前55个基因作为中心节点。通过重叠,获得了9个能够区分IS clusterA亚型和clusterB亚型的真实枢纽基因。真正的中枢基因(IL7R、ITK、SOD1、CD3D、LEF1、FBL、MAF、DNMT1和SLAMF1)可能与IS的分子亚型和免疫调节有关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Identification of molecular subtypes of ischaemic stroke based on immune-related genes and weighted co-expression network analysis

Identification of molecular subtypes of ischaemic stroke based on immune-related genes and weighted co-expression network analysis

Immune system has been reported to play a key role in the development of ischaemic stroke (IS). Nevertheless, its exact immune-related mechanism has not yet been fully revealed. Gene expression data of IS and healthy control samples was downloaded from Gene Expression Omnibus database and differentially expressed genes (DEGs) was obtained. Immune-related genes (IRGs) data was downloaded from the ImmPort database. The molecular subtypes of IS were identified based on IRGs and weighted co-expression network analysis (WGCNA). 827 DEGs and 1142 IRGs were obtained in IS. Based on 1142 IRGs, 128 IS samples were clustered into two molecular subtypes: clusterA and clusterB. Based on the WGCNA, the authors found that the blue module had the highest correlation with IS. In the blue module, 90 genes were screened as candidate genes. The top 55 genes were selected as the central nodes according to gene degree in protein–protein interactions network of all genes in blue module. Through taking overlap, nine real hub genes were obtained that might distinguish between clusterA subtype and clusterB subtype of IS. The real hub genes (IL7R, ITK, SOD1, CD3D, LEF1, FBL, MAF, DNMT1, and SLAMF1) may be associated with molecular subtypes and immune regulation of IS.

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来源期刊
IET Systems Biology
IET Systems Biology 生物-数学与计算生物学
CiteScore
4.20
自引率
4.30%
发文量
17
审稿时长
>12 weeks
期刊介绍: IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.
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