BACKGROUND: The Cancer Genome Atlas (TCGA) has divided patients with glioblastoma multiforme (GBM) into four subtypes based on mRNA expression microarray. The mesenchymal subtype, with a larger proportion, is considered a more lethal one. Clinical outcome prediction is required to better guide more personalized treatment for these patients. AIMS: The objective of this study was to identify a mRNA expression signature to improve outcome prediction for patients with mesenchymal GBM. RESULTS: For signature identification and validation, we downloaded mRNA expression microarray data from TCGA as training set and data from Rembrandt and GSE16011 as validation set. Cox regression and risk-score analysis were used to develop the 4 signatures, which were function and prognosis associated as revealed by Gene Ontology (GO) analysis and Gene Set Variation Analysis (GSVA). Patients who had high-risk scores according to the signatures had poor overall survival compared with patients who had low-risk scores. CONCLUSIONS: The signatures were identified as risk predictors that patients who had a high-risk score tended to have unfavorable outcome, demonstrating their potential for personalizing cancer management.
BACKGROUND: The Cancer Genome Atlas (TCGA) has divided patients with glioblastoma multiforme (GBM) into four subtypes based on mRNA expression microarray. The mesenchymal subtype, with a larger proportion, is considered a more lethal one. Clinical outcome prediction is required to better guide more personalized treatment for these patients. AIMS: The objective of this study was to identify a mRNA expression signature to improve outcome prediction for patients with mesenchymal GBM. RESULTS: For signature identification and validation, we downloaded mRNA expression microarray data from TCGA as training set and data from Rembrandt and GSE16011 as validation set. Cox regression and risk-score analysis were used to develop the 4 signatures, which were function and prognosis associated as revealed by Gene Ontology (GO) analysis and Gene Set Variation Analysis (GSVA). Patients who had high-risk scores according to the signatures had poor overall survival compared with patients who had low-risk scores. CONCLUSIONS: The signatures were identified as risk predictors that patients who had a high-risk score tended to have unfavorable outcome, demonstrating their potential for personalizing cancer management.
Authors: M R H Estécio; E M Youssef; P Rahal; E E Fukuyama; J F Góis-Filho; J V Maniglia; E M Goloni-Bertollo; J-P J Issa; E H Tajara Journal: Oncogene Date: 2006-05-29 Impact factor: 9.867
Authors: Houtan Noushmehr; Daniel J Weisenberger; Kristin Diefes; Heidi S Phillips; Kanan Pujara; Benjamin P Berman; Fei Pan; Christopher E Pelloski; Erik P Sulman; Krishna P Bhat; Roel G W Verhaak; Katherine A Hoadley; D Neil Hayes; Charles M Perou; Heather K Schmidt; Li Ding; Richard K Wilson; David Van Den Berg; Hui Shen; Henrik Bengtsson; Pierre Neuvial; Leslie M Cope; Jonathan Buckley; James G Herman; Stephen B Baylin; Peter W Laird; Kenneth Aldape Journal: Cancer Cell Date: 2010-04-15 Impact factor: 31.743
Authors: Y Yamano; K Ohyama; T Sano; M Ohta; A Shimada; Y Hirakawa; M Sugimoto; I Morishima Journal: Biochem Biophys Res Commun Date: 2001-12-14 Impact factor: 3.575
Authors: Roel G W Verhaak; Katherine A Hoadley; Elizabeth Purdom; Victoria Wang; Yuan Qi; Matthew D Wilkerson; C Ryan Miller; Li Ding; Todd Golub; Jill P Mesirov; Gabriele Alexe; Michael Lawrence; Michael O'Kelly; Pablo Tamayo; Barbara A Weir; Stacey Gabriel; Wendy Winckler; Supriya Gupta; Lakshmi Jakkula; Heidi S Feiler; J Graeme Hodgson; C David James; Jann N Sarkaria; Cameron Brennan; Ari Kahn; Paul T Spellman; Richard K Wilson; Terence P Speed; Joe W Gray; Matthew Meyerson; Gad Getz; Charles M Perou; D Neil Hayes Journal: Cancer Cell Date: 2010-01-19 Impact factor: 31.743
Authors: Thorsten Wiech; Elisabeth Nikolopoulos; Roland Weis; Rupert Langer; Kilian Bartholomé; Jens Timmer; Axel K Walch; Heinz Höfler; Martin Werner Journal: Lab Invest Date: 2008-07-28 Impact factor: 5.662
Authors: Heidi S Phillips; Samir Kharbanda; Ruihuan Chen; William F Forrest; Robert H Soriano; Thomas D Wu; Anjan Misra; Janice M Nigro; Howard Colman; Liliana Soroceanu; P Mickey Williams; Zora Modrusan; Burt G Feuerstein; Ken Aldape Journal: Cancer Cell Date: 2006-03 Impact factor: 31.743
Authors: Aiguo Li; Jennifer Walling; Susie Ahn; Yuri Kotliarov; Qin Su; Martha Quezado; J Carl Oberholtzer; John Park; Jean C Zenklusen; Howard A Fine Journal: Cancer Res Date: 2009-02-24 Impact factor: 12.701
Authors: Fabiana Marcelino Meliso; Christopher G Hubert; Pedro A Favoretto Galante; Luiz O Penalva Journal: Hum Genet Date: 2017-06-12 Impact factor: 4.132