title: A 4-gene signature associated with clinical outcome in high-grade gliomas.
abstract: OBJECTIVE
  Gene expression studies provide molecular insights improving the classification of patients with high-grade gliomas. We have developed a risk estimation strategy based on a combined analysis of gene expression data to search for robust biomarkers associated with outcome in these tumors.
  
  
  METHODS
  We performed a meta-analysis using 3 publicly available malignant gliomas microarray data sets (267 patients) to define the genes related to both glioma malignancy and patient outcome. These biomarkers were used to construct a risk-score equation based on a Cox proportional hazards model on a subset of 144 patients. External validations were performed on microarray data (59 patients) and on RT-qPCR data (194 patients). The risk-score model performances (discrimination and calibration) were evaluated and compared with that of clinical risk factors, MGMT promoter methylation status, and IDH1 mutational status.
  
  
  RESULTS
  This interstudy cross-validation approach allowed the identification of a 4-gene signature highly correlated to survival (CHAF1B, PDLIM4, EDNRB, and HJURP), from which an optimal survival model was built (P < 0.001 in training and validation sets). Multivariate analysis showed that the 4-gene risk score was strongly and independently associated with survival (hazard ratio = 0.46; 95% CI, 0.26-0.81; P = 0.007). Performance estimations indicated that this score added beyond standard clinical parameters and beyond both the MGMT methylation status and the IDH1 mutational status in terms of discrimination (C statistics, 0.827 versus 0.835; P < 0.001).
  
  
  CONCLUSIONS
  The 4-gene signature provides an independent risk score strongly associated with outcome of patients with high-grade gliomas.
authors.affiliation: CNRS UMR 6061 Genetic and Development, University of Rennes 1, Rennes, France.
authors.forename: Marie
authors.initials: M
authors.lastname: de Tayrac
authors.forename: Marc
authors.initials: M
authors.lastname: Aubry
authors.forename: Stephan
authors.initials: S
authors.lastname: Saïkali
authors.forename: Amandine
authors.initials: A
authors.lastname: Etcheverry
authors.forename: Cyrille
authors.initials: C
authors.lastname: Surbled
authors.forename: Frédérique
authors.initials: F
authors.lastname: Guénot
authors.forename: Marie-Dominique
authors.initials: MD
authors.lastname: Galibert
authors.forename: Abderrahmane
authors.initials: A
authors.lastname: Hamlat
authors.forename: Thierry
authors.initials: T
authors.lastname: Lesimple
authors.forename: Véronique
authors.initials: V
authors.lastname: Quillien
authors.forename: Philippe
authors.initials: P
authors.lastname: Menei
authors.forename: Jean
authors.initials: J
authors.lastname: Mosser
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis; D001932:Brain Neoplasms / Q000235:genetics* / Q000401:mortality; D005260:Female; D020869:Gene Expression Profiling; D005910:Glioma / Q000235:genetics* / Q000401:mortality; D006801:Humans; D008297:Male; D015201:Meta-Analysis as Topic; D008875:Middle Aged; D018570:Risk Assessment; D016019:Survival Analysis; D016896:Treatment Outcome
chemical_list: D014408:Biomarkers, Tumor