Observational Study
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Jan 15, 2021; 13(1): 37-57
Published online Jan 15, 2021. doi: 10.4251/wjgo.v13.i1.37
Mining The Cancer Genome Atlas database for tumor mutation burden and its clinical implications in gastric cancer
Dong-Yan Zhao, Xi-Zhen Sun, Shu-Kun Yao
Dong-Yan Zhao, Xi-Zhen Sun, Shu-Kun Yao, Department of Gastroenterology, China-Japan Friendship Hospital, Beijing 100029, China
Dong-Yan Zhao, Xi-Zhen Sun, Shu-Kun Yao, Graduate school, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China
Author contributions: Zhao DY conceived and designed the study and wrote the manuscript; Sun XZ took part in analyzing the data; Yao SK designed the study, revised the manuscript, and obtained the funding; all authors read and approved the final manuscript.
Supported by National Key Development Plan for Precision Medicine Research, No. 2017YFC0910002.
Institutional review board statement: This study was approved by the Ethics Committee of China-Japan Friendship Hospital (No. 2018-116-K85-1).
Informed consent statement: All the data were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/repository) database in our study. TCGA database is freely available open to the public, so there is no requirement for additional informed consent statement.
Conflict-of-interest statement: All authors report no conflicts of interest.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
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: Shu-Kun Yao, MD, PhD, Professor, Department of Gastroenterology, China-Japan Friendship Hospital, No. 2 Yinghua East Road, Chaoyang District, Beijing 100029, China. shukunyao@126.com
Received: September 17, 2020
Peer-review started: September 17, 2020
First decision: November 3, 2020
Revised: November 8, 2020
Accepted: November 28, 2020
Article in press: November 28, 2020
Published online: January 15, 2021
ARTICLE HIGHLIGHTS
Research background

Tumor mutational burden (TMB) is in the spotlight as a novel biomarker and a rational target for predicting response to immunotherapy in multiple cancers. Gastric cancer (GC) is one of the most common gastrointestinal malignant tumors worldwide. Accumulating evidence highlights that it is necessary to further explore clinical impact of TMB in GC.

Research motivation

The association of TMB with clinical outcomes and immune infiltration in the tumor microenvironment in GC patients has not yet been elucidated. MicroRNAs (miRNAs) have a crucial role in the carcinogenesis, migration, and invasion of tumor cells by regulating adaptive and innate immune responses, but the relationship between miRNA expression patterns and mutational load is not clear in GC.

Research objectives

This study aimed to explore the clinical impact of TMB and establish a miRNA-based signature for TMB prediction in GC patients.

Research methods

The Kaplan-Meier analysis in the GC cohort from The Cancer Genome Atlas dataset was performed by defining the highest TMB quintile (top 20%) as the high-TMB group. The difference in immune infiltration between the high- and low-TMB subgroups was evaluated by Wilcoxon rank-sum test. The least absolute shrinkage and selection operator analysis was conducted to select parameters from differentially expressed miRNAs between the high- and low-TMB subgroups and construct a miRNA-based signature classifier for TMB prediction.

Research results

Higher mutational load in GC was significantly associated with better prognosis, older ages, female gender, earlier tumor stage, and lack of lymph node metastasis. Different mutational load levels exhibited different immune infiltration patterns and different miRNA expression patterns. In addition, we developed a miRNA-based signature using 23 differentially expressed miRNAs to predict TMB values of GC patients.

Research conclusions

High TMB is notably correlated with good survival and might lead to the activation of antitumor immune cells in the tumor microenvironment in GC. The miRNA-based signature might be developed as a surrogate biomarker for TMB in GC.

Research perspectives

The miRNA-based signature for TMB prediction might help develop treatment strategies for GC patients and have an impact on the clinical practice in the course of GC.