AccScience Publishing / EJMO / Online First / DOI: 10.36922/EJMO026030024
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ORIGINAL RESEARCH ARTICLE

Distinct metabolic subtypes of gastric cancer: Immune profiles, therapeutic response prediction, and a 60-gene classifier

Silin Liu1 ,  Yusheng Luo2 ,  Chaoxun Dou1 ,  Yuling Huang3 ,  Qingyang Lin4 ,  Yin Huang5 ,  Zhuoming Guo5 ,  Fen Pan1 ,  Jinyan Guo1*
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1 Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
2 Department of Hematology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China
3 Department of Gastroenterology, Center of Digestive Diseases, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China
4 Department of Basic Medical Sciences, School of Medicine, Sun Yat-sen University, Shenzhen, Guangdong, China
5 Dongguan Key Laboratory of Stem Cell and Regenerative Tissue Engineering, Faculty of Basic Medical Sciences, Guangdong Medical University, Dongguan, Guangdong, China
EJMO, 026030024 https://doi.org/10.36922/EJMO026030024
Received: 12 January 2026 | Revised: 8 February 2026 | Accepted: 12 March 2026 | Published online: 19 May 2026
(This article belongs to the Special Issue Tumor Immune Microenvironment and Intervention Strategies)
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Introduction: Metabolic reprogramming influences cancer progression and the immune microenvironment. In gastric cancer (GC), metabolic states may impact immune responses and treatment outcomes, but specific metabolic subtypes remain poorly characterized.

Objective: This study investigated metabolic subtypes and their immune features in GC and developed a gene classifier for clinical use.

Methods: Transcriptomic profiling via RNA sequencing, together with clinical data from 981 GC patients, was analyzed and independently validated using two external datasets (TCGA-STAD cohort and GSE113255). Non-negative matrix factorization identified two metabolic subtypes (clusters 1 and 2) based on metabolic gene expression. We compared clinical features, genomic alterations, immune profiles, and drug responses between subtypes and developed a 60-gene classifier using a support vector machine model.

Results: Cluster 1 was associated with poor prognosis, a low mutation burden, immune exclusion, high stromal activity, and dense immune infiltration; this subtype demonstrated increased sensitivity to platinum-based therapies. In contrast, cluster 2 was characterized by better clinical outcomes, an immune-inflamed phenotype, elevated programmed death-ligand 1 and cytokine expression, and a greater potential for responding to immunotherapy.

Conclusion: Our metabolic classification delineates distinct GC subtypes that hold significant implications for prognosis and therapy. The 60-gene classifier offers a practical tool for the clinical identification of these subtypes, which could enhance the precision of immunotherapy and chemotherapy strategies tailored to GC patients.

Graphical abstract
Keywords
Metabolic subtype
Gastric cancer
Genomic alterations
Immune infiltration
Immunotherapy
Funding
None.
Conflict of interest
The authors declare no conflict of interest.
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Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
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