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
ARTICLE HIGHLIGHTS
Research background

Gastric cancer (GC) is a heterogeneous disease with genetic and epigenetic alterations. Robust biomarkers for management and survival prognosis of GC patients are lacking. Deoxyribonucleic acid (DNA) methylation is a major epigenetic event that participates in early stage of GC and is suggested to be associated with survival in many cancers including GC.

Research motivation

Exploring molecular subtypes of GC can improve understanding of this heterogeneous cancer and contribute to better management and prognosis prediction. Studies on DNA methylation subtypes of GC are lacking.

Research objectives

To identify the specific DNA methylation sites that influence the prognosis of GC patients by integrating epigenetic and clinical information. We also aimed to establish a prognostic model based on subtypes of DNA methylation.

Research methods

Data of GC patients were obtained from The Cancer Genome Atlas and the University of California Santa Cruz cancer browser. Prognostic DNA methylation sites were identified by integrating DNA methylation profiles and clinical data. We used unsupervised clustering to identify distinct subgroups based on methylation status. A risk score model was built and further validated in a test set.

Research results

In this study, we identified three subtypes based on DNA methylation profiles using methylation sites that were significantly associated with survival. These methylation subtypes were associated with clinical features, patient outcomes, and potential responses to therapy. Enrichment analysis of specific hyper- or hypomethylation sites revealed that they were mainly involved in pathways related to carcinogenesis and tumor growth and progression. A prognostic model consisting two methylation sites was subsequently generated. The high-risk group showed a significantly poorer prognosis compared to the low-risk group in both the training (hazard ratio = 2.24, 95% confidence interval: 1.28-3.92, P < 0.001) and test (hazard ratio = 2.12, 95% confidence interval: 1.19-3.78, P = 0.002) sets. More samples are needed to optimize the model performance.

Research conclusions

This study indicates that DNA methylation-based classification reflects the epigenetic heterogeneity of GC. A prediction model based on methylation subtypes can predict the OS of GC patients.

Research perspectives

Our study can help predict prognosis and increase our understanding of the heterogeneity of GC patients. This is a retrospective analysis of GC patients from public database, so prospective studies are needed to validated the findings.