Clinical and Translational Research
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Mar 15, 2024; 16(3): 919-932
Published online Mar 15, 2024. doi: 10.4251/wjgo.v16.i3.919
Construction of an immune-related gene signature for overall survival prediction and immune infiltration in gastric cancer
Xiao-Ting Ma, Xiu Liu, Kai Ou, Lin Yang
Xiao-Ting Ma, Xiu Liu, Kai Ou, Lin Yang, Department of Medical Oncology, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
Author contributions: Ma XT designed the article form and wrote the manuscript; Ou K and Liu X consulted and browsed the literature; Yang L revised the manuscript and provided the funding. All authors read and approved the final manuscript.
Supported by Beijing CSCO Clinical Oncology Research Foundation, No. Y-HH202102-0308.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences.
Informed consent statement: As the study used anonymous and pre-existing data, the requirement for the informed consent from patients was waived.
Conflict-of-interest statement: The authors declare that they have no competing interests.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Lin Yang, Doctor, Department of Medical Oncology, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China. linyangcicams@126.com
Received: September 7, 2023
Peer-review started: September 7, 2023
First decision: December 5, 2023
Revised: December 16, 2023
Accepted: February 2, 2024
Article in press: February 2, 2024
Published online: March 15, 2024
Processing time: 186 Days and 18.5 Hours
ARTICLE HIGHLIGHTS
Research background

In this study, we established a risk score model for differentially expressed immune-related genes (DEIRGs) to determine the impact on the development, prognosis, tumor microenvironment (TME), and treatment response of gastric cancer (GC) patients and to provide a new biomarker for personalized treatment of GC populations.

Research motivation

In this study we determined the impact of DEIRGs on the development, prognosis, TME, and treatment response of GC patients. In addition, we obtained a risk score that predicts clinical outcomes and immunotherapy efficacy in GC patients, and analyzed immune cell infiltration, immune checkpoints, tumor mutation burden (TMB), and immunotherapy between high- and low-risk patients. Based on the findings of the current study, we expect to provide novel biomarkers for personalized treatment of GC populations.

Research objectives

To explore the effects of DEIRGs on the development, prognosis, TME and treatment response of patients with GC, and establish a risk model to provide new biomarkers for personalized treatment of GC.

Research methods

We used public data for analysis, established a risk model for DEIRGs, and divided the data into two groups: the training cohort and the test cohort. The Kaplan Meier survival analysis, receiver operating characteristic curve analysis, and risk curve confirmed that the risk model has good predictive ability. Simultaneously predict the response of immune checkpoint inhibitors based on TMB and immunophenotype (IPS) scores.

Research results

We obtained an immune-related signature based on 10 genes, including 9 risk genes (LCN1, LEAP2, TMSB15A mRNA, DEFB126, PI15, IGHD3-16, IGLV3-22, CGB5, and GLP2R) and 1 protective gene (LGR6). Meanwhile, patients in the low-risk group had higher TMB and IPS, which can be used to predict the immune checkpoint inhibitor response.

Research conclusions

By developing a risk model, we aim to provide new biomarkers for personalized treatment of GC. The validity of the model is verified through many aspects.

Research perspectives

In the future, we should further verify the effectiveness of this model in the population and confirm its clinical practicability.