Identification of TNFAIP6 as a hub gene associated with the progression of glioblastoma by weighted gene co-expression network analysis

IF 1.9 4区 生物学 Q4 CELL BIOLOGY IET Systems Biology Pub Date : 2022-06-29 DOI:10.1049/syb2.12046
Dongdong Lin, Wei Li, Nu Zhang, Ming Cai
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引用次数: 1

Abstract

This study aims to discover the genetic modules that distinguish glioblastoma multiforme (GBM) from low-grade glioma (LGG) and identify hub genes. A co-expression network is constructed using the expression profiles of 28 GBM and LGG patients from the Gene Expression Omnibus database. The authors performed gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) analysis on these genes. The maximal clique centrality method was used to identify hub genes. Online tools were employed to confirm the link between hub gene expression and overall patient survival rate. The top 5000 genes with major variance were classified into 18 co-expression gene modules. GO analysis indicated that abnormal changes in ‘cell migration’ and ‘collagen metabolic process’ were involved in the development of GBM. KEGG analysis suggested that ‘focal adhesion’ and ‘p53 signalling pathway’ regulate the tumour progression. TNFAIP6 was identified as a hub gene, and the expression of TNFAIP6 was increased with the elevation of pathological grade. Survival analysis indicated that the higher the expression of TNFAIP6, the shorter the survival time of patients. The authors identified TNFAIP6 as the hub gene in the progression of GBM, and its high expression indicates the poor prognosis of the patients.

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通过加权基因共表达网络分析确定TNFAIP6是胶质母细胞瘤进展相关的枢纽基因
本研究旨在发现区分多形性胶质母细胞瘤(GBM)和低级别胶质瘤(LGG)的遗传模块,并鉴定中枢基因。利用基因表达综合数据库中28例GBM和LGG患者的表达谱构建共表达网络。作者对这些基因进行了基因本体(GO)和京都基因与基因组百科全书(KEGG)分析。采用最大团中心性方法对轮毂基因进行识别。使用在线工具来确认枢纽基因表达与患者总体生存率之间的联系。将变异最大的前5000个基因分为18个共表达基因模块。氧化石墨烯分析表明,“细胞迁移”和“胶原代谢过程”的异常变化参与了GBM的发展。KEGG分析表明,“局灶黏附”和“p53信号通路”调节肿瘤进展。TNFAIP6被鉴定为枢纽基因,并且随着病理分级的升高,TNFAIP6的表达增加。生存分析表明,TNFAIP6表达越高,患者生存时间越短。作者发现TNFAIP6是GBM进展的枢纽基因,其高表达提示患者预后不良。
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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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