Retrospective Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Nov 7, 2020; 26(41): 6414-6430
Published online Nov 7, 2020. doi: 10.3748/wjg.v26.i41.6414
Signature based on molecular subtypes of deoxyribonucleic acid methylation predicts overall survival in gastric cancer
Jin Bian, Jun-Yu Long, Xu Yang, Xiao-Bo Yang, Yi-Yao Xu, Xin Lu, Xin-Ting Sang, Hai-Tao Zhao
Jin Bian, Jun-Yu Long, Xu Yang, Xiao-Bo Yang, Yi-Yao Xu, Xin Lu, Xin-Ting Sang, Hai-Tao Zhao, Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
Author contributions: Bian J and Long JY contributed equally to this work; Bian J and Long JY collected the data, performed the analysis, and wrote the manuscript; Yang X participated in preparing the figures and tables; Yang XB, Xu YY, and Lu X helped to collect the literature and participated in discussions; Sang XT and Zhao HT designed and contributed equally to the study; all authors read and approved the final manuscript.
Supported by the International Science and Technology Cooperation Projects, No. 2016YFE0107100; Capital Special Research Project for Health Development, No. 2014-2-4012; Beijing Natural Science Foundation, No. L172055 and No. 7192158; National Ten-thousand Talent Program, the Fundamental Research Funds for the Central Universities, No. 3332018032; and CAMS Innovation Fund for Medical Science (CIFMS), No. 2017-I2M-4-003 and No. 2018-I2M-3-001.
Institutional review board statement: All data were downloaded from the Cancer Genome Atlas and the University of California Santa Cruz (UCSC) Cancer Browser, which are open to the public under certain restrictions, therefore no ethical approval was required.
Informed consent statement: The data used in the current study are obtained from The Cancer Genome Atlas database (TCGA) and the University of California Santa Cruz (UCSC) Cancer Browser, which are open to the public under some guidelines. Therefore, it is confirmed that all written informed consent was achieved.
Conflict-of-interest statement: We declare that the authors have no conflict of interest.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Hai-Tao Zhao, MD, Professor, Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Wangfujing, Beijing 100730, China. zhaoht@pumch.cn
Received: July 20, 2020
Peer-review started: July 20, 2020
First decision: August 8, 2020
Revised: August 17, 2020
Accepted: September 10, 2020
Article in press: September 10, 2020
Published online: November 7, 2020
Abstract
BACKGROUND

Gastric cancer (GC) ranks as the third leading cause of cancer-related death worldwide. Epigenetic alterations contribute to tumor heterogeneity in early stages.

AIM

To identify the specific deoxyribonucleic acid (DNA) methylation sites that influence the prognosis of GC patients and explore the prognostic value of a model based on subtypes of DNA methylation.

METHODS

Patients were randomly classified into training and test sets. Prognostic DNA methylation sites were identified by integrating DNA methylation profiles and clinical data from The Cancer Genome Atlas GC cohort. In the training set, unsupervised consensus clustering was performed to identify distinct subgroups based on methylation status. A risk score model was built based on Kaplan-Meier, least absolute shrinkage and selector operation, and multivariate Cox regression analyses. A test set was used to validate this model.

RESULTS

Three subgroups based on DNA methylation profiles in the training set were identified using 1061 methylation sites that were significantly associated with survival. These methylation subtypes reflected differences in T, N, and M category, age, stage, and prognosis. Forty-one methylation sites were screened as specific hyper- or hypomethylation sites for each specific subgroup. Enrichment analysis revealed that they were mainly involved in pathways related to carcinogenesis, tumor growth, and progression. Finally, two methylation sites were chosen to generate a prognostic model. The high-risk group showed a markedly poor prognosis compared to the low-risk group in both the training [hazard ratio (HR) = 2.24, 95% confidence interval (CI): 1.28-3.92, P < 0.001] and test (HR = 2.12, 95%CI: 1.19-3.78, P = 0.002) datasets.

CONCLUSION

DNA methylation-based classification reflects the epigenetic heterogeneity of GC and may contribute to predicting prognosis and offer novel insights for individualized treatment of patients with GC.

Keywords: Gastric cancer, Deoxyribonucleic acid methylation, Molecular subtypes, Prognosis, Risk score, The Cancer Genome Atlas

Core Tip: To address the epigenetic heterogeneity of gastric cancer, three subgroups based on deoxyribonucleic acid (DNA) methylation were identified and each subtype was associated with distinct survival and clinical features. A signature based on molecular subtypes of DNA methylation was built to predict the survival of gastric cancer patients, and showed good performance. This work may improve our understanding of the epigenetic landscape of gastric cancer and facilitate precision medicine for these patients.