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

Yong Wan Kim, Dimpy Koul, Se Hoon Kim, Agda Karina Lucio-Eterovic, Pablo R. Freire, Jun Yao, Jing Wang, Jonas S. Almeida, Ken Aldape, W. K.Alfred Yung

Research output: Contribution to journalArticle

49 Citations (Scopus)

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. Results: To 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.

Original languageEnglish
Pages (from-to)829-839
Number of pages11
JournalNeuro-Oncology
Volume15
Issue number7
DOIs
Publication statusPublished - 2013 Jul 1

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Atlases
Glioblastoma
Genome
Survival
Genes
Neoplasms
Gene Expression
Gene Dosage
Proportional Hazards Models
Datasets

All Science Journal Classification (ASJC) codes

  • Oncology
  • Clinical Neurology
  • Cancer Research

Cite this

Kim, Y. W., Koul, D., Kim, S. H., Lucio-Eterovic, A. K., Freire, P. R., Yao, J., ... Yung, W. K. A. (2013). Identification of prognostic gene signatures of glioblastoma: A study based on TCGA data analysis. Neuro-Oncology, 15(7), 829-839. https://doi.org/10.1093/neuonc/not024
Kim, Yong Wan ; Koul, Dimpy ; Kim, Se Hoon ; Lucio-Eterovic, Agda Karina ; Freire, Pablo R. ; Yao, Jun ; Wang, Jing ; Almeida, Jonas S. ; Aldape, Ken ; Yung, W. K.Alfred. / Identification of prognostic gene signatures of glioblastoma : A study based on TCGA data analysis. In: Neuro-Oncology. 2013 ; Vol. 15, No. 7. pp. 829-839.
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Kim, YW, Koul, D, Kim, SH, Lucio-Eterovic, AK, Freire, PR, Yao, J, Wang, J, Almeida, JS, Aldape, K & Yung, WKA 2013, 'Identification of prognostic gene signatures of glioblastoma: A study based on TCGA data analysis', Neuro-Oncology, vol. 15, no. 7, pp. 829-839. https://doi.org/10.1093/neuonc/not024

Identification of prognostic gene signatures of glioblastoma : A study based on TCGA data analysis. / Kim, Yong Wan; Koul, Dimpy; Kim, Se Hoon; Lucio-Eterovic, Agda Karina; Freire, Pablo R.; Yao, Jun; Wang, Jing; Almeida, Jonas S.; Aldape, Ken; Yung, W. K.Alfred.

In: Neuro-Oncology, Vol. 15, No. 7, 01.07.2013, p. 829-839.

Research output: Contribution to journalArticle

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T1 - Identification of prognostic gene signatures of glioblastoma

T2 - A study based on TCGA data analysis

AU - Kim, Yong Wan

AU - Koul, Dimpy

AU - Kim, Se Hoon

AU - Lucio-Eterovic, Agda Karina

AU - Freire, Pablo R.

AU - Yao, Jun

AU - Wang, Jing

AU - Almeida, Jonas S.

AU - Aldape, Ken

AU - Yung, W. K.Alfred

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N2 - 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. Results: To 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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