Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Bo L

PROVIDER: S-EPMC5780009 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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

Bo Lijuan L Wei Bo B Wang Zhanfeng Z Kong Daliang D Gao Zheng Z Miao Zhuang Z

Molecular medicine reports 20171019 6

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) us ...[more]

PMID: 29152649

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Project description:BackgroundGrade IV glioblastomas exist in two forms, primary (de novo) glioblastomas (pGBM) that arise without precursor lesions, and the less common secondary glioblastomas (sGBM) which develop from earlier lower grade lesions. Genetic heterogeneity between pGBM and sGBM has been documented as have differences in the methylation of individual genes. A hypermethylator phenotype in grade IV GBMs is now well documented however there has been little comparison between global methylation profiles of pGBM and sGBM samples or of methylation profiles between paired early and late sGBM samples.MethodsWe performed genome-wide methylation profiling of 20 matched pairs of early and late gliomas using the Infinium HumanMethylation450 BeadChips to assess methylation at >485,000 cytosine positions within the human genome.ResultsClustering of our data demonstrated a frequent hypermethylator phenotype that associated with IDH1 mutation in sGBM tumors. In 80% of cases, the hypermethylator status was retained in both the early and late tumor of the same patient, indicating limited alterations to genome-wide methylation during progression and that the CIMP phenotype is an early event. Analysis of hypermethylated loci identified 218 genes frequently methylated across grade II, III and IV tumors indicating a possible role in sGBM tumorigenesis. Comparison of our sGBM data with TCGA pGBM data indicate that IDH1 mutated GBM samples have very similar hypermethylator phenotypes, however the methylation profiles of the majority of samples with WT IDH1 that do not demonstrate a hypermethylator phenotype cluster separately from sGBM samples, indicating underlying differences in methylation profiles. We also identified 180 genes that were methylated only in sGBM. Further analysis of these genes may lead to a better understanding of the pathology of sGBM vs pGBM.ConclusionThis is the first study to have documented genome-wide methylation changes within paired early/late astrocytic gliomas on such a large CpG probe set, revealing a number of genes that maybe relevant to secondary gliomagenesis.

Dataset Information

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

Publications

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

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