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

Rebecka Jörnsten (Institutionen för matematiska vetenskaper, matematisk statistik) ; Tobias Abenius (Institutionen för matematiska vetenskaper, matematisk statistik) ; Teresia Kling ; Linnéa Schmidt ; Erik Johansson ; Torbjörn E M Nordling ; Bodil Nordlander ; Chris Sander ; Peter Gennemark (Institutionen för matematiska vetenskaper) ; Keiko Funa ; Björn Nilsson ; Linda Lindahl ; Sven Nelander
Molecular Systems Biology (1744-4292). Vol. 7 (2011), p. 486.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Cell Line, Tumor, Chromosome Aberrations, Databases, Factual, Gene Dosage, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Genome, Human, Genome-Wide Association Study, Glioblastoma, genetics, metabolism, mortality, pathology, Humans, Models, Genetic, Nerve Tissue Proteins, genetics, metabolism, Nervous System Neoplasms, genetics, metabolism, mortality, pathology, Nuclear Proteins, genetics, metabolism, Prognosis, Software, Transcriptional Activation, genetics, Tumor Suppressor Protein p53, genetics, metabolism



Denna post skapades 2011-06-13. Senast ändrad 2016-11-07.
CPL Pubid: 141635

 

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Institutioner (Chalmers)

Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)
Institutionen för medicin (GU)
Institutionen för biomedicin (GU)
Institutionen för cell- och molekylärbiologi, mikrobiologi (1994-2011)
Institutionen för matematiska vetenskaperInstitutionen för matematiska vetenskaper (GU)

Ämnesområden

Matematisk statistik
Bioinformatik och systembiologi
Funktionsgenomik
Tumörbiologi
Molekylär medicin

Chalmers infrastruktur