Integrative analysis of novel hypomethylation and gene expression signatures in glioblastomas
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Anan Yin1,*, Amandine Etcheverry2,3,4,*, Yalong He1,*, Marc Aubry3,5, Jill Barnholtz-Sloan6, Luhua Zhang1,7, Xinggang Mao1, Weijun Chen1, Bolin Liu8, Wei Zhang1, Jean Mosser2,3,4,5 and Xiang Zhang1
1Department of Neurosurgery, Xijing Institute of Clinical Neuroscience, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi Province, The People’s Republic of China
2CNRS, UMR 6290, Institut de Génétique et Développement de Rennes (IGdR), Rennes, France
3Université Rennes1, UEB, UMS 3480 Biosit, Faculté de Médecine, Rennes, France
4CHU Rennes, Service de Génétique Moléculaire et Génomique, Rennes, France
5Plate-forme Génomique Santé Biosit, Université Rennes1, Rennes, France
6Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, United States of America
7Department of Neurosurgery, No. 425 Hospital of the People’s Liberation Army, San Ya, Hainan Province, The People’s Republic of China
8Department of Neurosurgery, Arrowhead Regional Medical Center, Colton, California, United States of America
*These authors contributed equally to this work
Keywords: glioblastomas, non-CpG island hypomethylation, gene network, molecular classification, precision oncology
Received: January 28, 2017 Accepted: June 29, 2017 Published: July 11, 2017
Molecular and clinical heterogeneity critically hinders better treatment outcome for glioblastomas (GBMs); integrative analysis of genomic and epigenomic data may provide useful information for improving personalized medicine. By applying training-validation approach, we identified a novel hypomethylation signature comprising of three CpGs at non-CpG island (CGI) open sea regions for GBMs. The hypomethylation signature consistently predicted poor prognosis of GBMs in a series of discovery and validation datasets. It was demonstrated as an independent prognostic indicator, and showed interrelationships with known molecular marks such as MGMT promoter methylation status, and glioma CpG island methylator phenotype (G-CIMP) or IDH1 mutations. Bioinformatic analysis found that the hypomethylation signature was closely associated with the transcriptional status of an EGFR/VEGFA/ANXA1-centered gene network. The integrative molecular analysis finally revealed that the gene network defined two distinct clinically relevant molecular subtypes reminiscent of different immature neuroglial lineages in GBMs. The novel hypomethylation signature and relevant gene network may provide new insights into prognostic classification, molecular characterization, and treatment development for GBMs.
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