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©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Feb 15, 2022; 14(2): 478-497
Published online Feb 15, 2022. doi: 10.4251/wjgo.v14.i2.478
Comprehensive molecular characterization and identification of prognostic signature in stomach adenocarcinoma on the basis of energy-metabolism-related genes
Jin-Jia Chang, Xiao-Yu Wang, Wei Zhang, Cong Tan, Wei-Qi Sheng, Mi-Die Xu
Jin-Jia Chang, Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
Jin-Jia Chang, Wei Zhang, Cong Tan, Wei-Qi Sheng, Mi-Die Xu, Department of Medical Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
Xiao-Yu Wang, Laboratory of Immunology and Virology, Experiment Center for Science and Technology, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
Wei Zhang, Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
Cong Tan, Wei-Qi Sheng, Mi-Die Xu, Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
Cong Tan, Wei-Qi Sheng, Mi-Die Xu, Institute of Pathology, Fudan University, Shanghai 200032, China
Author contributions: Chang JJ, Wang XY, Zhang W, Tan C, Sheng WQ and Xu MD designed the research; Chang JJ, Wang XY, Zhang W and Tan C performed the research; Tan C and Xu MD contributed analytic tools; Chang JJ, Wang XY and Zhang W analyzed the data and contributed equally to this work; Sheng WQ and Xu MD wrote the paper and share the corresponding authorship of this study.
Supported by the National Natural Science Foundation of China, No. 81972249, No. 81802367, No. 81802361 and No. 82172702; the Shanghai Clinical Research Plan of SHDC, No. SHDC2020CR4068; the Shanghai Clinical Science and Technology Innovation Project of Municipal Hospital, No. SHDC12020102; the Shanghai Science and Technology Development Fund, No. 18ZR1408000, No. 21ZR1414900 and No. 19MC1911000; the Clinical Research Project of Shanghai Municipal Health Committee, No. 20194Y0348; and the Shanghai “Rising Stars of Medical Talents” Youth Development Program Youth Medical Talents – Specialist Program, No. SHWSRS(2020)_087.
Institutional review board statement: Not applicable.
Conflict-of-interest statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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: Wei-Qi Sheng, PhD, Chief Doctor, Director, Professor, Teacher, Department of Pathology, Fudan University Shanghai Cancer Center, No. 270 Dong’an Road, Shanghai 200032, China.
shengweiqi2006@163.com
Received: August 5, 2021
Peer-review started: August 5, 2021
First decision: October 3, 2021
Revised: October 9, 2021
Accepted: January 6, 2022
Article in press: January 6, 2022
Published online: February 15, 2022
Processing time: 189 Days and 13.6 Hours
ARTICLE HIGHLIGHTS
Research background
Energy metabolism has always been a hallmark of cancer cells and the complex metabolic characteristics of tumor cells can greatly influence the clinical fate of malignancies.
Research motivation
A deep understanding of the cancer metabolic fingerprint may be crucial to the development of new therapies and in identifying promising prognostic signatures.
Research objectives
To select key prognostic factors of gastric cancer (GC) among the 587 energy metabolism genes, and construct a potential metabolism-related model for the survival prediction of GC patients.
Research methods
We trained and verified the energy metabolism-related gene signature among a total of 339 GC samples from The Cancer Genome Atlas (TCGA) Stomach Adenocarcinoma STAD) dataset and 300 tumor samples from the GSE62254 dataset of the Gene Expression Omnibus.
Research results
We successfully created a prognostic model based on energy-metabolism-related gene expression profiles in primary stomach adenocarcinoma based on an analysis of the TCGA-STAD and GSE62254 datasets. We were able to divide and identify different subtypes for prognosis and develop a risk score based on 6 gene signatures to potentially stratify the prognosis of individuals which was validated in a second cohort.
Research conclusions
In summary, by analyzing the expression levels of energy metabolism-related genes in GC tumor tissues, two different clusters with varied clinical characteristics, clinical outcomes, and immune status were identified in the TCGA-STAD dataset. A prognostic signature containing six metabolism-related genes and a novel nomogram was identified for the accurate risk prediction of GC patients.
Research perspectives
This study demonstrates the possibility of the risk score calculated with combination of gene expression in energy metabolism-related prognostic models.
