Research Papers:

Integrative analysis of novel hypomethylation and gene expression signatures in glioblastomas

Anan Yin, Amandine Etcheverry, Yalong He, Marc Aubry, Jill Barnholtz-Sloan, Luhua Zhang, Xinggang Mao, Weijun Chen, Bolin Liu, Wei Zhang, Jean Mosser and Xiang Zhang _

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Oncotarget. 2017; 8:89607-89619. https://doi.org/10.18632/oncotarget.19171

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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

Correspondence to:

Xiang Zhang, email: [email protected], [email protected]

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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