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G-cimp status prediction of glioblastoma samples using mRNA expression data.

TitleG-cimp status prediction of glioblastoma samples using mRNA expression data.
Publication TypeJournal Article
Year of Publication2012
AuthorsBaysan M, Bozdag S, Cam MC, Kotliarova S, Ahn S, Walling J, Killian JK, Stevenson H, Meltzer P, Fine HA
JournalPLoS One
Volume7
Issue11
Paginatione47839
Date Published2012
ISSN1932-6203
KeywordsBrain Neoplasms, Cluster Analysis, CpG Islands, Databases, Genetic, DNA Methylation, Gene Expression Regulation, Neoplastic, Glioblastoma, Humans, Kaplan-Meier Estimate, Models, Genetic, Principal Component Analysis, Reproducibility of Results, RNA, Messenger
Abstract

Glioblastoma Multiforme (GBM) is a tumor with high mortality and no known cure. The dramatic molecular and clinical heterogeneity seen in this tumor has led to attempts to define genetically similar subgroups of GBM with the hope of developing tumor specific therapies targeted to the unique biology within each of these subgroups. Recently, a subset of relatively favorable prognosis GBMs has been identified. These glioma CpG island methylator phenotype, or G-CIMP tumors, have distinct genomic copy number aberrations, DNA methylation patterns, and (mRNA) expression profiles compared to other GBMs. While the standard method for identifying G-CIMP tumors is based on genome-wide DNA methylation data, such data is often not available compared to the more widely available gene expression data. In this study, we have developed and evaluated a method to predict the G-CIMP status of GBM samples based solely on gene expression data.

DOI10.1371/journal.pone.0047839
Alternate JournalPLoS ONE
PubMed ID23139755
PubMed Central IDPMC3490960
Grant List / / Intramural NIH HHS / United States