G-cimp status prediction of glioblastoma samples using mRNA expression data. PLoS One 2012;7(11):e47839
Date
11/10/2012Pubmed ID
23139755Pubmed Central ID
PMC3490960DOI
10.1371/journal.pone.0047839Scopus ID
2-s2.0-84876435048 (requires institutional sign-in at Scopus site) 32 CitationsAbstract
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.
Author List
Baysan M, Bozdag S, Cam MC, Kotliarova S, Ahn S, Walling J, Killian JK, Stevenson H, Meltzer P, Fine HAAuthor
Serdar Bozdag BS,PhD Assistant Professor, Director of Bioinformatics Lab in the Dept. of Mathematics, Statistics and Computer Science department at Marquette UniversityMESH terms used to index this publication - Major topics in bold
Brain NeoplasmsCluster Analysis
CpG Islands
DNA Methylation
Databases, Genetic
Gene Expression Regulation, Neoplastic
Glioblastoma
Humans
Kaplan-Meier Estimate
Models, Genetic
Principal Component Analysis
RNA, Messenger
Reproducibility of Results
