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Mol Syst Biol. 2011 Apr 26;7:486. doi: 10.1038/msb.2011.17.

Network modeling of the transcriptional effects of copy number aberrations in glioblastoma.

Author information

1
Mathematical Sciences, University of Gothenburg and Chalmers University of Technology, Gothenburg, Sweden.

Abstract

DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.

PMID:
21525872
PMCID:
PMC3101951
DOI:
10.1038/msb.2011.17
[Indexed for MEDLINE]
Free PMC Article
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