Open Access

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

  • Authors:
    • Lijuan Bo
    • Bo Wei
    • Zhanfeng Wang
    • Daliang Kong
    • Zheng Gao
    • Zhuang Miao
  • View Affiliations

  • Published online on: October 19, 2017     https://doi.org/10.3892/mmr.2017.7834
  • Pages: 9503-9511
  • Copyright: © Bo et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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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.
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December-2017
Volume 16 Issue 6

Print ISSN: 1791-2997
Online ISSN:1791-3004

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Copy and paste a formatted citation
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Spandidos Publications style
Bo L, Wei B, Wang Z, Kong D, Gao Z and Miao Z: Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data. Mol Med Rep 16: 9503-9511, 2017
APA
Bo, L., Wei, B., Wang, Z., Kong, D., Gao, Z., & Miao, Z. (2017). Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data. Molecular Medicine Reports, 16, 9503-9511. https://doi.org/10.3892/mmr.2017.7834
MLA
Bo, L., Wei, B., Wang, Z., Kong, D., Gao, Z., Miao, Z."Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data". Molecular Medicine Reports 16.6 (2017): 9503-9511.
Chicago
Bo, L., Wei, B., Wang, Z., Kong, D., Gao, Z., Miao, Z."Identification of key genes in glioma CpG island methylator phenotype via network analysis of gene expression data". Molecular Medicine Reports 16, no. 6 (2017): 9503-9511. https://doi.org/10.3892/mmr.2017.7834