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Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data

Gene expression data were analysed using bioinformatic tools to demonstrate molecular mechanisms underlying the glioma CpG island methylator phenotype (CIMP). A gene expression data set (accession no. GSE30336) was downloaded from Gene Expression Omnibus, including 36 CIMP(+) and 16 CIMP(−) glioma s...

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Autores principales: Bo, Lijuan, Wei, Bo, Wang, Zhanfeng, Kong, Daliang, Gao, Zheng, Miao, Zhuang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5780009/
https://www.ncbi.nlm.nih.gov/pubmed/29152649
http://dx.doi.org/10.3892/mmr.2017.7834
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author Bo, Lijuan
Wei, Bo
Wang, Zhanfeng
Kong, Daliang
Gao, Zheng
Miao, Zhuang
author_facet Bo, Lijuan
Wei, Bo
Wang, Zhanfeng
Kong, Daliang
Gao, Zheng
Miao, Zhuang
author_sort Bo, Lijuan
collection PubMed
description Gene expression data were analysed using bioinformatic tools to demonstrate molecular mechanisms underlying the glioma CpG island methylator phenotype (CIMP). A gene expression data set (accession no. GSE30336) was downloaded from Gene Expression Omnibus, including 36 CIMP(+) and 16 CIMP(−) glioma samples. Differential analysis was performed for CIMP(+) vs. CIMP(−) samples using the limma package in R. Functional enrichment analysis was subsequently conducted for differentially expressed genes (DEGs) using Database for Annotation, Visualization and Integration Discovery. Protein-protein interaction (PPI) networks were constructed for upregulated and downregulated genes with information from STRING. MicroRNAs (miRNAs) targeting DEGs were also predicted using WebGestalt. A total of 439 DEGs were identified, including 214 upregulated and 198 downregulated genes. The upregulated genes were involved in extracellular matrix organisation, defence and immune response, collagen fibril organisation and regulation of cell motion and the downregulated genes in cell adhesion, sensory organ development, regulation of system process, neuron differentiation and membrane organisation. A PPI network containing 134 nodes and 314 edges was constructed from the upregulated genes, whereas a PPI network consisting of 85 nodes and 80 edges was obtained from the downregulated genes. miRNAs regulating upregulated and downregulated genes were predicted, including miRNA-124a and miRNA-34a. Numerous key genes associated with glioma CIMP were identified in the present study. These findings may advance the understanding of glioma and facilitate the development of appropriate therapies.
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spelling pubmed-57800092018-02-12 Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data Bo, Lijuan Wei, Bo Wang, Zhanfeng Kong, Daliang Gao, Zheng Miao, Zhuang Mol Med Rep Articles Gene expression data were analysed using bioinformatic tools to demonstrate molecular mechanisms underlying the glioma CpG island methylator phenotype (CIMP). A gene expression data set (accession no. GSE30336) was downloaded from Gene Expression Omnibus, including 36 CIMP(+) and 16 CIMP(−) glioma samples. Differential analysis was performed for CIMP(+) vs. CIMP(−) samples using the limma package in R. Functional enrichment analysis was subsequently conducted for differentially expressed genes (DEGs) using Database for Annotation, Visualization and Integration Discovery. Protein-protein interaction (PPI) networks were constructed for upregulated and downregulated genes with information from STRING. MicroRNAs (miRNAs) targeting DEGs were also predicted using WebGestalt. A total of 439 DEGs were identified, including 214 upregulated and 198 downregulated genes. The upregulated genes were involved in extracellular matrix organisation, defence and immune response, collagen fibril organisation and regulation of cell motion and the downregulated genes in cell adhesion, sensory organ development, regulation of system process, neuron differentiation and membrane organisation. A PPI network containing 134 nodes and 314 edges was constructed from the upregulated genes, whereas a PPI network consisting of 85 nodes and 80 edges was obtained from the downregulated genes. miRNAs regulating upregulated and downregulated genes were predicted, including miRNA-124a and miRNA-34a. Numerous key genes associated with glioma CIMP were identified in the present study. These findings may advance the understanding of glioma and facilitate the development of appropriate therapies. D.A. Spandidos 2017-12 2017-10-19 /pmc/articles/PMC5780009/ /pubmed/29152649 http://dx.doi.org/10.3892/mmr.2017.7834 Text en Copyright: © Bo et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Bo, Lijuan
Wei, Bo
Wang, Zhanfeng
Kong, Daliang
Gao, Zheng
Miao, Zhuang
Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data
title Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data
title_full Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data
title_fullStr Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data
title_full_unstemmed Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data
title_short Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data
title_sort identification of key genes in glioma cpg island methylator phenotype via network analysis of gene expression data
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5780009/
https://www.ncbi.nlm.nih.gov/pubmed/29152649
http://dx.doi.org/10.3892/mmr.2017.7834
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