Article

Identification of prognostic gene signatures of glioblastoma: a study based on TCGA data analysis

Cancer Research Institute of Medical Science, The Catholic University of Korea, Seoul, Korea, (Y.-W.K.), Brain Tumor Center, Department of Neuro-Oncology (D.K., S.H.K., A.K.L.-E., J.Y., K.A., W.K.A.Y.), and Department of Bioinformatics and Computational Biology (P.R.F., J.W., J.S.A.), The University of Texas MD Anderson Cancer Center, Houston, Texas.
Neuro-Oncology (Impact Factor: 5.29). 03/2013; 15(7). DOI: 10.1093/neuonc/not024
Source: PubMed

ABSTRACT Background
The Cancer Genome Atlas (TCGA) project is a large-scale effort with the goal of identifying novel molecular aberrations in glioblastoma (GBM).Methods
Here, we describe an in-depth analysis of gene expression data and copy number aberration (CNA) data to classify GBMs into prognostic groups to determine correlates of subtypes that may be biologically significant.ResultsTo identify predictive survival models, we searched TCGA in 173 patients and identified 42 probe sets (P = .0005) that could be used to divide the tumor samples into 3 groups and showed a significantly (P = .0006) improved overall survival. Kaplan-Meier plots showed that the median survival of group 3 was markedly longer (127 weeks) than that of groups 1 and 2 (47 and 52 weeks, respectively). We then validated the 42 probe sets to stratify the patients according to survival in other public GBM gene expression datasets (eg, GSE4290 dataset). An overall analysis of the gene expression and copy number aberration using a multivariate Cox regression model showed that the 42 probe sets had a significant (P < .018) prognostic value independent of other variables.Conclusions
By integrating multidimensional genomic data from TCGA, we identified a specific survival model in a new prognostic group of GBM and suggest that molecular stratification of patients with GBM into homogeneous subgroups may provide opportunities for the development of new treatment modalities.

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