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

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...

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Bibliographic Details
Published in:PloS one 2012-11, Vol.7 (11), p.e47839-e47839
Main Authors: Baysan, Mehmet, Bozdag, Serdar, Cam, Margaret C, Kotliarova, Svetlana, Ahn, Susie, Walling, Jennifer, Killian, Jonathan K, Stevenson, Holly, Meltzer, Paul, Fine, Howard A
Format: Article
Language:English
Subjects:
Aberration
Biology
Biomarkers
Brain cancer
Brain Neoplasms - genetics
Brain research
Brain tumors
Cluster Analysis
Comparative analysis
Copy number
CpG islands
CpG Islands - genetics
Databases, Genetic
Deoxyribonucleic acid
Development and progression
DNA
DNA methylation
DNA Methylation - genetics
Epigenetics
Gene expression
Gene Expression Regulation, Neoplastic
Genes
Genetics
Genomes
Genomics
Genotype & phenotype
Glioblastoma
Glioblastoma - genetics
Glioblastoma multiforme
Glioblastomas
Glioma
Health aspects
Humans
Identification
Kaplan-Meier Estimate
Laboratories
Medical prognosis
Medical research
Medicine
Messenger RNA
Methylation
Models, Genetic
Mortality
Mutation
Oncology
Principal Component Analysis
Prognosis
Reproducibility of Results
RNA, Messenger - genetics
Studies
Subgroups
Tumors
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Items that cite this one
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Description
Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0047839