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Network modeling of the transcriptional effects of copy number aberrations in glioblastoma
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2011 (Engelska)Ingår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 7, s. 486-Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
2011. Vol. 7, s. 486-
Nyckelord [en]
cancer biology, cancer genomics, glioblastoma
Nationell ämneskategori
Medicin och hälsovetenskap
Identifikatorer
URN: urn:nbn:se:uu:diva-153949DOI: 10.1038/msb.2011.17ISI: 000290411600004PubMedID: 21525872OAI: oai:DiVA.org:uu-153949DiVA, id: diva2:419195
Tillgänglig från: 2011-05-26 Skapad: 2011-05-23 Senast uppdaterad: 2017-12-11Bibliografiskt granskad

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