G-Cimp Status Prediction Of Glioblastoma Samples Using mRNA Expression Data
Author(s) -
Mehmet Baysan,
Serdar Bozdag,
Margaret C. Cam,
Svetlana Kotliarova,
Susie Ahn,
Jennifer Walling,
Jonathan Keith Killian,
Holly Stevenson,
Paul S. Meltzer,
Howard A. Fine
Publication year - 2012
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0047839
Subject(s) - dna methylation , biology , glioma , glioblastoma , methylation , cpg site , phenotype , gene expression profiling , gene , gene expression , computational biology , cancer research , genetics
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.
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