Literature DB >> 30291347

A novel analytical model of MGMT methylation pyrosequencing offers improved predictive performance in patients with gliomas.

Rui-Chao Chai1,2, Yu-Qing Liu1,2, Ke-Nan Zhang1,2,3, Fan Wu1,2, Zheng Zhao1,2, Kuan-Yu Wang1,2,3, Tao Jiang1,2,3, Yong-Zhi Wang4,5,6.   

Abstract

The methylation status of the promoter of MGMT gene is a crucial factor influencing clinical decision-making in patients with gliomas. MGMT pyrosequencing results are often dichotomized by a cut-off value based on an average of several tested CpGs. However, this method frequently results in a "gray zone", representing a dilemma for physicians. We therefore propose a novel analytical model for MGMT methylation pyrosequencing. MGMT CpG heterogeneity was investigated in 213 glioma patients in two tested cohorts: cohort A in which CpGs 75-82 were tested and cohort B in which CpGs 72-78 were tested. The predictive performances of the novel and traditional averaging models were compared in 135 patients who received temozolomide using receiver operating characteristic curves and Kaplan-Meier curves, and in patients stratified according to isocitrate dehydrogenase gene mutation status. The results were validated in an independent cohort of 65 consecutive patients with high-grade gliomas from the Chinese Glioma Genome Atlas database. Heterogeneity of MGMT promoter CpG methylation level was observed in most gliomas. The optimal cut-off value for each individual CpG varied from 4-16%. The current analysis defined MGMT promoter methylation as occurring when at least three CpGs exceeded their respective cut-off values. This novel analysis could accurately predict the prognosis of patients in the methylation "gray zone" according to the standard averaging method, and improved the area under the curves from 0.67, 0.76, and 0.67 to 0.70, 0.84, and 0.72 in cohorts A, B, and the validation cohort, respectively, demonstrating superiority of this analytical method in all three cohorts. Furthermore, the advantages of the novel analysis were retained regardless of WHO grade and isocitrate dehydrogenase gene mutation status. In conclusion, this novel analytical model offers an improved clinical predictive performance for MGMT pyrosequencing results and is suitable for clinical use in patients with gliomas.

Entities:  

Year:  2018        PMID: 30291347     DOI: 10.1038/s41379-018-0143-2

Source DB:  PubMed          Journal:  Mod Pathol        ISSN: 0893-3952            Impact factor:   7.842


  20 in total

1.  RNA processing genes characterize RNA splicing and further stratify lower-grade glioma.

Authors:  Rui-Chao Chai; Yi-Ming Li; Ke-Nan Zhang; Yu-Zhou Chang; Yu-Qing Liu; Zheng Zhao; Zhi-Liang Wang; Yuan-Hao Chang; Guan-Zhang Li; Kuan-Yu Wang; Fan Wu; Yong-Zhi Wang
Journal:  JCI Insight       Date:  2019-08-13

2.  Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging.

Authors:  Yiming Li; Yuchao Liang; Zhiyan Sun; Kaibin Xu; Xing Fan; Shaowu Li; Zhong Zhang; Tao Jiang; Xing Liu; Yinyan Wang
Journal:  Neuroradiology       Date:  2019-06-19       Impact factor: 2.804

3.  Expression and Potential Biomarkers of Regulators for M7G RNA Modification in Gliomas.

Authors:  Zhen Chen; Zhe Zhang; Wei Ding; Jie-Hui Zhang; Zi-Long Tan; Yu-Ran Mei; Wei He; Xiao-Jing Wang
Journal:  Front Neurol       Date:  2022-05-09       Impact factor: 4.086

Review 4.  MGMT and Whole-Genome DNA Methylation Impacts on Diagnosis, Prognosis and Therapy of Glioblastoma Multiforme.

Authors:  Rosa Della Monica; Mariella Cuomo; Michela Buonaiuto; Davide Costabile; Raduan Ahmed Franca; Marialaura Del Basso De Caro; Giuseppe Catapano; Lorenzo Chiariotti; Roberta Visconti
Journal:  Int J Mol Sci       Date:  2022-06-27       Impact factor: 6.208

5.  Systematically profiling the expression of eIF3 subunits in glioma reveals the expression of eIF3i has prognostic value in IDH-mutant lower grade glioma.

Authors:  Rui-Chao Chai; Ning Wang; Yu-Zhou Chang; Ke-Nan Zhang; Jing-Jun Li; Jun-Jie Niu; Fan Wu; Yu-Qing Liu; Yong-Zhi Wang
Journal:  Cancer Cell Int       Date:  2019-06-04       Impact factor: 5.722

6.  [ALKBH5 suppresses migration and invasion of human trophoblast cells by inhibiting epithelial-mesenchymal transition].

Authors:  Jianping He; Xiaojuan Li; Mengxin Lü; Jue Wang; Jian Tang; Shengjun Luo; Yuan Qian
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2020-12-30

7.  m6A RNA methylation regulators contribute to malignant progression and have clinical prognostic impact in gliomas.

Authors:  Rui-Chao Chai; Fan Wu; Qi-Xue Wang; Shu Zhang; Ke-Nan Zhang; Yu-Qing Liu; Zheng Zhao; Tao Jiang; Yong-Zhi Wang; Chun-Sheng Kang
Journal:  Aging (Albany NY)       Date:  2019-02-27       Impact factor: 5.682

8.  A Novel DNA Methylation-Based Signature Can Predict the Responses of MGMT Promoter Unmethylated Glioblastomas to Temozolomide.

Authors:  Rui-Chao Chai; Yu-Zhou Chang; Qiang-Wei Wang; Ke-Nan Zhang; Jing-Jun Li; Hua Huang; Fan Wu; Yu-Qing Liu; Yong-Zhi Wang
Journal:  Front Genet       Date:  2019-09-27       Impact factor: 4.599

9.  Pyrosequencing versus methylation-specific PCR for assessment of MGMT methylation in tumor and blood samples of glioblastoma patients.

Authors:  Anna Estival; Carolina Sanz; Jose-Luis Ramirez; Jose Maria Velarde; Marta Domenech; Cristina Carrato; Ramón de Las Peñas; Miguel Gil-Gil; Juan Sepúlveda; Roser Armengol; Isaac Cardiel; Alfonso Berrocal; Raquel Luque; Ana Herrero; Carmen Balana
Journal:  Sci Rep       Date:  2019-07-31       Impact factor: 4.379

10.  A novel Cas9-targeted long-read assay for simultaneous detection of IDH1/2 mutations and clinically relevant MGMT methylation in fresh biopsies of diffuse glioma.

Authors:  Thidathip Wongsurawat; Piroon Jenjaroenpun; Annick De Loose; Duah Alkam; David W Ussery; Intawat Nookaew; Yuet-Kin Leung; Shuk-Mei Ho; John D Day; Analiz Rodriguez
Journal:  Acta Neuropathol Commun       Date:  2020-06-20       Impact factor: 7.578

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