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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Vishal N Patel, Giridharan Gokulrangan, Salim A Chowdhury, Yanwen Chen, Andrew E Sloan, Mehmet Koyutürk, Jill Barnholtz-Sloan, Mark R Chance
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c3b2052b1d324f75a6f87cbd16219579
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c3b2052b1d324f75a6f87cbd16219579
record_format	dspace
spelling	oai:doaj.org-article:c3b2052b1d324f75a6f87cbd162195792021-11-18T05:53:34ZNetwork signatures of survival in glioblastoma multiforme.1553-734X1553-735810.1371/journal.pcbi.1003237https://doaj.org/article/c3b2052b1d324f75a6f87cbd162195792013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24068912/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358To 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.Vishal N PatelGiridharan GokulranganSalim A ChowdhuryYanwen ChenAndrew E SloanMehmet KoyutürkJill Barnholtz-SloanMark R ChancePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 9, p e1003237 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Vishal N Patel Giridharan Gokulrangan Salim A Chowdhury Yanwen Chen Andrew E Sloan Mehmet Koyutürk Jill Barnholtz-Sloan Mark R Chance Network signatures of survival in glioblastoma multiforme.
description	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.
format	article
author	Vishal N Patel Giridharan Gokulrangan Salim A Chowdhury Yanwen Chen Andrew E Sloan Mehmet Koyutürk Jill Barnholtz-Sloan Mark R Chance
author_facet	Vishal N Patel Giridharan Gokulrangan Salim A Chowdhury Yanwen Chen Andrew E Sloan Mehmet Koyutürk Jill Barnholtz-Sloan Mark R Chance
author_sort	Vishal N Patel
title	Network signatures of survival in glioblastoma multiforme.
title_short	Network signatures of survival in glioblastoma multiforme.
title_full	Network signatures of survival in glioblastoma multiforme.
title_fullStr	Network signatures of survival in glioblastoma multiforme.
title_full_unstemmed	Network signatures of survival in glioblastoma multiforme.
title_sort	network signatures of survival in glioblastoma multiforme.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/c3b2052b1d324f75a6f87cbd16219579
work_keys_str_mv	AT vishalnpatel networksignaturesofsurvivalinglioblastomamultiforme AT giridharangokulrangan networksignaturesofsurvivalinglioblastomamultiforme AT salimachowdhury networksignaturesofsurvivalinglioblastomamultiforme AT yanwenchen networksignaturesofsurvivalinglioblastomamultiforme AT andrewesloan networksignaturesofsurvivalinglioblastomamultiforme AT mehmetkoyuturk networksignaturesofsurvivalinglioblastomamultiforme AT jillbarnholtzsloan networksignaturesofsurvivalinglioblastomamultiforme AT markrchance networksignaturesofsurvivalinglioblastomamultiforme
_version_	1718424691795820544

Network signatures of survival in glioblastoma multiforme.

Ejemplares similares