G-Cimp Status Prediction Of Glioblastoma Samples Using mRNA Expression Data

PLoS One

Serdar Bozdag¹  Holly Stevenson²  Jonathan K. Killian²  Paul Meltzer²  Margaret C. Cam³  Susie Ahn³  Mehmet Baysan³  Howard A. Fine³  Svetlana Kotliarova³  Jennifer Walling³ 

[1] Department of Mathematics, Statistics, and Computer Science, Marquette University, Milwaukee, Wisconsin, United States of America;Genetics Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America;Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America

关键词: Methylation;    DNA methylation;    Forecasting;    Gene expression;    Glioblastoma multiforme;    Gene prediction;    Data processing;    Glioma;   

DOI  :  10.1371/journal.pone.0047839

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

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.

【 授权许可】

CC BY