1. Academic Validation
  2. Genome-wide DNA methylation analysis identifies potent CpG signature for temzolomide response in non-G-CIMP glioblastomas with unmethylated MGMT promoter: MGMT-dependent roles of GPR81

Genome-wide DNA methylation analysis identifies potent CpG signature for temzolomide response in non-G-CIMP glioblastomas with unmethylated MGMT promoter: MGMT-dependent roles of GPR81

  • CNS Neurosci Ther. 2023 Oct 13. doi: 10.1111/cns.14465.
Bao-Bao Liang 1 Yu-Hong Wang 2 Jing-Jing Huang 3 Shuai Lin 1 Guo-Chao Mao 1 Zhang-Jian Zhou 1 Wan-Jun Yan 1 Chang-You Shan 1 Hui-Zi Wu 1 Amandine Etcheverry 4 Ya-Long He 5 Fang-Fang Liu 6 Hua-Feng Kang 1 An-An Yin 7 8 Shu-Qun Zhang 1
Affiliations

Affiliations

  • 1 Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • 2 The Emergency Department, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.
  • 3 Department of Pediatric Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • 4 CNRS, UMR 6290, Institut de Génétique et Développement de Rennes (IGdR), Rennes, France.
  • 5 Department of Neurosurgery, Xijing Hospital, Air Force Medical University, Xi'an, China.
  • 6 Institute of Neurosciences, College of Basic Medicine, Air Force Medical University, Xi'an, China.
  • 7 Department of Biochemistry and Molecular Biology, Air Force Medical University, Xi'an, China.
  • 8 Department of Plastic and Reconstructive Surgery, Xijing Hospital, Air Force Medical University, Xi'an, China.
Abstract

Purposes: To identify potent DNA methylation candidates that could predict response to temozolomide (TMZ) in glioblastomas (GBMs) that do not have glioma-CpGs island methylator phenotype (G-CIMP) but have an unmethylated promoter of O-6-methylguanine-DNA methyltransferase (unMGMT).

Methods: The discovery-validation approach was planned incorporating a series of G-CIMP-/unMGMT GBM cohorts with DNA methylation microarray data and clinical information, to construct multi-CpG prediction models. Different bioinformatic and experimental analyses were performed for biological exploration.

Results: By analyzing discovery sets with radiotherapy (RT) plus TMZ versus RT alone, we identified a panel of 64 TMZ efficacy-related CpGs, from which a 10-CpG risk signature was further constructed. Both the 64-CpG panel and the 10-CpG risk signature were validated showing significant correlations with overall survival of G-CIMP-/unMGMT GBMs when treated with RT/TMZ, rather than RT alone. The 10-CpG risk signature was further observed for aiding TMZ choice by distinguishing differential outcomes to RT/TMZ versus RT within each risk subgroup. Functional studies on GPR81, the gene harboring one of the 10 CpGs, indicated its distinct impacts on TMZ resistance in GBM cells, which may be dependent on the status of MGMT expression.

Conclusions: The 64 TMZ efficacy-related CpGs and in particular the 10-CpG risk signature may serve as promising predictive biomarker candidates for guiding optimal usage of TMZ in G-CIMP-/unMGMT GBMs.

Keywords

DNA methylation; glioblastoma; predictive biomarker; temozolomide; unmethylated MGMT.

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