title: Identification of prognostic gene signatures of glioblastoma: a study based on TCGA data analysis.
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.
authors.affiliation: Cancer Research Institute of Medical Science, The Catholic University of Korea, Seoul, Korea.
authors.forename: Yong-Wan
authors.initials: YW
authors.lastname: Kim
authors.forename: Dimpy
authors.initials: D
authors.lastname: Koul
authors.forename: Se Hoon
authors.initials: SH
authors.lastname: Kim
authors.forename: Agda Karina
authors.initials: AK
authors.lastname: Lucio-Eterovic
authors.forename: Pablo R
authors.initials: PR
authors.lastname: Freire
authors.forename: Jun
authors.initials: J
authors.lastname: Yao
authors.forename: Jing
authors.initials: J
authors.lastname: Wang
authors.forename: Jonas S
authors.initials: JS
authors.lastname: Almeida
authors.forename: Ken
authors.initials: K
authors.lastname: Aldape
authors.forename: W K Alfred
authors.initials: WK
authors.lastname: Yung
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D001932:Brain Neoplasms / Q000145:classification / Q000235:genetics* / Q000401:mortality; D016022:Case-Control Studies; D019295:Computational Biology; D056915:DNA Copy Number Variations / Q000235:genetics*; D030541:Databases, Genetic; D058750:Epithelial-Mesenchymal Transition; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D023281:Genomics; D005909:Glioblastoma / Q000145:classification / Q000235:genetics* / Q000401:mortality; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D012333:RNA, Messenger / Q000235:genetics*; D015996:Survival Rate
chemical_list: D014408:Biomarkers, Tumor; D012333:RNA, Messenger
keywords: EMT; TCGA; comparative genomic hybridization; gene expression; glioblastoma; prognostic marker