Integration of gene expression and methylation to unravel biological networks in glioblastoma patients.
Genet Epidemiol
; 41(2): 136-144, 2017 Feb.
Article
em En
| MEDLINE
| ID: mdl-28019039
ABSTRACT
The vast amount of heterogeneous omics data, encompassing a broad range of biomolecular information, requires novel methods of analysis, including those that integrate the available levels of information. In this work, we describe Regression2Net, a computational approach that is able to integrate gene expression and genomic or methylation data in two steps. First, penalized regressions are used to build Expression-Expression (EEnet) and Expression-Genomic or Expression-Methylation (EMnet) networks. Second, network theory is used to highlight important communities of genes. When applying our approach, Regression2Net to gene expression and methylation profiles for individuals with glioblastoma multiforme, we identified, respectively, 284 and 447 potentially interesting genes in relation to glioblastoma pathology. These genes showed at least one connection in the integrated networks ANDnet and XORnet derived from aforementioned EEnet and EMnet networks. Although the edges in ANDnet occur in both EEnet and EMnet, the edges in XORnet occur in EMnet but not in EEnet. In-depth biological analysis of connected genes in ANDnet and XORnet revealed genes that are related to energy metabolism, cell cycle control (AATF), immune system response, and several cancer types. Importantly, we observed significant overrepresentation of cancer-related pathways including glioma, especially in the XORnet network, suggesting a nonignorable role of methylation in glioblastoma multiforma. In the ANDnet, we furthermore identified potential glioma suppressor genes ACCN3 and ACCN4 linked to the NBPF1 neuroblastoma breakpoint family, as well as numerous ABC transporter genes (ABCA1, ABCB1) suggesting drug resistance of glioblastoma tumors.
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MEDLINE
Assunto principal:
Regulação Neoplásica da Expressão Gênica
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Glioblastoma
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Metilação de DNA
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Genômica
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Redes Reguladoras de Genes
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Proteínas de Neoplasias
Limite:
Humans
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article