Author ORCID Identifier
Document Type
Article
Publication Date
9-19-2013
Abstract
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.
Keywords
gene expression, proteomics, proteomic databases, protein expression, glioblastoma multiforme, cancer treatment, malignant tumors, STAT proteins
Publication Title
PLoS Computational Biology
Grant
P30-CA-043703
Rights
© 2013 Patel et al. This is an open-access article distributed under the terms of the CreativeCommons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Patel VN, Gokulrangan G, Chowdhury SA, Chen Y, Sloan AE, et al. (2013) Network Signatures of Survival in Glioblastoma Multiforme. PLOS Computational Biology 9(9): e1003237. https://doi.org/10.1371/journal.pcbi.1003237
Manuscript Version
Final Publisher Version