Literature DB >> 21159630

Coexpression network analysis identifies transcriptional modules related to proastrocytic differentiation and sprouty signaling in glioma.

Alexander E Ivliev1, Peter A C 't Hoen, Marina G Sergeeva.   

Abstract

Gliomas are primary brain tumors with high mortality and heterogeneous biology that is insufficiently understood. In this study, we performed a systematic analysis of the intrinsic organization of complex glioma transcriptome to gain deeper knowledge of the tumor biology. Gene coexpression relationships were explored in 790 glioma samples from 5 published patient cohorts treated at different institutions. We identified 20 coexpression modules that were common to all the data sets and associated with proliferation, angiogenesis, hypoxia, immune response, genomic alterations, cell differentiation phenotypes, and other features inherent to glial tumors. A collection of high-quality signatures for the respective processes was obtained using cross-data set summarization of the modules' gene composition. Individual modules were found to be organized into higher order coexpression groups, the two largest of them associated with glioblastoma and oligodendroglioma, respectively. We identified a novel prognostic gene expression signature (185 genes) linked to a proastrocytic pattern of tumor cell differentiation. This "proastrocytic" signature was associated with long survival and defined a subgroup of the previously established "proneural" class of gliomas. A strong negative correlation between proastrocytic and proneural markers across differentiated tumors underscored the distinction between these subtypes of glioma. Interestingly, one further novel signature in glioma was identified that was associated with EGFR (epidermal growth factor receptor) gene amplification and suggested that EGF signaling in glioma may be a subject to regulation by Sprouty family proteins. In summary, this integrated analysis of the glioma transcriptome provided several novel insights into molecular heterogeneity and pathogenesis of glial tumors. ©2010 AACR.

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Year:  2010        PMID: 21159630     DOI: 10.1158/0008-5472.CAN-10-2465

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  44 in total

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2.  Transcriptional modules related to hepatocellular carcinoma survival: coexpression network analysis.

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Journal:  Cancer Biol Ther       Date:  2015       Impact factor: 4.742

4.  Analysis of discordant Affymetrix probesets casts serious doubt on idea of microarray data reutilization.

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5.  Transcriptional networks implicated in human nonalcoholic fatty liver disease.

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Journal:  Mol Genet Genomics       Date:  2015-04-08       Impact factor: 3.291

Review 6.  EGFR-dependent mechanisms in glioblastoma: towards a better therapeutic strategy.

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Journal:  Cell Mol Life Sci       Date:  2014-03-27       Impact factor: 9.261

Review 7.  Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders.

Authors:  C Gaiteri; Y Ding; B French; G C Tseng; E Sibille
Journal:  Genes Brain Behav       Date:  2013-12-10       Impact factor: 3.449

8.  Genome-wide screening and co-expression network analysis identify recurrence-specific biomarkers of esophageal squamous cell carcinoma.

Authors:  Zong-wu Lin; Jie Gu; Rong-hua Liu; Xiao-ming Liu; Feng-kai Xu; Guang-yin Zhao; Chun-lai Lu; Di Ge
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9.  Diversity and divergence of the glioma-infiltrating T-cell receptor repertoire.

Authors:  Jennifer S Sims; Boris Grinshpun; Yaping Feng; Timothy H Ung; Justin A Neira; Jorge L Samanamud; Peter Canoll; Yufeng Shen; Peter A Sims; Jeffrey N Bruce
Journal:  Proc Natl Acad Sci U S A       Date:  2016-06-03       Impact factor: 11.205

10.  Inhibition of DYRK1A destabilizes EGFR and reduces EGFR-dependent glioblastoma growth.

Authors:  Natividad Pozo; Cristina Zahonero; Paloma Fernández; Jose M Liñares; Angel Ayuso; Masatoshi Hagiwara; Angel Pérez; Jose R Ricoy; Aurelio Hernández-Laín; Juan M Sepúlveda; Pilar Sánchez-Gómez
Journal:  J Clin Invest       Date:  2013-05-01       Impact factor: 14.808

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