Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma.
Mol Syst Biol, 2012;8:605.
Setty M[1], Helmy K, Khan AA, Silber J, Arvey A, Neezen F, Agius P, Huse JT, Holland EC, Leslie CS
Affiliations
PMID: 22929615DOI: 10.1038/msb.2012.37
Impact factor: 13.068
Abstract
Large-scale cancer genomics projects are profiling hundreds of tumors at multiple molecular layers, including copy number, mRNA and miRNA expression, but the mechanistic relationships between these layers are often excluded from computational models. We developed a supervised learning framework for integrating molecular profiles with regulatory sequence information to reveal regulatory programs in cancer, including miRNA-mediated regulation. We applied our approach to 320 glioblastoma profiles and identified key miRNAs and transcription factors as common or subtype-specific drivers of expression changes. We confirmed that predicted gene expression signatures for proneural subtype regulators were consistent with in vivo expression changes in a PDGF-driven mouse model. We tested two predicted proneural drivers, miR-124 and miR-132, both underexpressed in proneural tumors, by overexpression in neurospheres and observed a partial reversal of corresponding tumor expression changes. Computationally dissecting the role of miRNAs in cancer may ultimately lead to small RNA therapeutics tailored to subtype or individual.
MeSH terms
Animals; Cell Line, Tumor; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Genome, Human; Genomics; Glioblastoma; Humans; Mice; Mice, Transgenic; MicroRNAs; Models, Biological; Neural Stem Cells; RNA, Messenger; Regression Analysis; Transcription Factors
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