{"title":"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","authors":"Dongdong Lin, Wei Li, Nu Zhang, Ming Cai","doi":"10.1049/syb2.12046","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 5","pages":"145-156"},"PeriodicalIF":1.9000,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469790/pdf/","citationCount":"1","resultStr":"{\"title\":\"Identification of TNFAIP6 as a hub gene associated with the progression of glioblastoma by weighted gene co-expression network analysis\",\"authors\":\"Dongdong Lin, Wei Li, Nu Zhang, Ming Cai\",\"doi\":\"10.1049/syb2.12046\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":50379,\"journal\":{\"name\":\"IET Systems Biology\",\"volume\":\"16 5\",\"pages\":\"145-156\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2022-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469790/pdf/\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Systems Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/syb2.12046\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Systems Biology","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/syb2.12046","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Identification of TNFAIP6 as a hub gene associated with the progression of glioblastoma by weighted gene co-expression network analysis
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.
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.