Network signatures of survival in glioblastoma multiforme
To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguish...
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Published in | PLoS computational biology Vol. 9; no. 9; p. e1003237 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
01.09.2013
Public Library of Science (PLoS) |
Subjects | |
Online Access | Get full text |
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Summary: | To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included "protein kinase cascade," "IκB kinase/NFκB cascade," and "regulation of programmed cell death" - all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Undefined-2 MRC, SAC, and MK have competing interests in the software CRANE. Conceived and designed the experiments: VNP GG MK MRC. Performed the experiments: VNP GG SAC. Analyzed the data: VNP GG SAC YC. Contributed reagents/materials/analysis tools: AES JBS MRC. Wrote the paper: VNP MK MRC. |
ISSN: | 1553-7358 1553-734X 1553-7358 |
DOI: | 10.1371/journal.pcbi.1003237 |