ORIGINAL RESEARCH article

Front. Immunol.

Sec. Cancer Immunity and Immunotherapy

Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1527233

SUMOylation-Related Genes Define Prognostic Subtypes in Stomach Adenocarcinoma: Integrating Single-Cell Analysis and Machine Learning Analyses

Provisionally accepted
Luo KaipingLuo Kaiping1,2,3Donghui XingDonghui Xing1,2,3Xiang HeXiang He1,2,3Yixin ZhaiYixin Zhai1,2,3Yanan JiangYanan Jiang4Hongjie ZhanHongjie Zhan2,3,5*Zhigang ZhaoZhigang Zhao4*
  • 1Department of Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, Tianjin, China
  • 2National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, Tianjin Municipality, China
  • 3Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
  • 4Department of Medical Oncology, Tianjin First Central Hospital, School of Medicine. Nankai University, Tianjin, 300192, China, Tianjin, China
  • 5Department of Gastroenterology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, Tianjin, China

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Background: Stomach adenocarcinoma (STAD) exhibits high molecular heterogeneity and poor prognosis, necessitating robust biomarkers for risk stratification. While SUMOylation, a posttranslational modification, regulates tumor progression, its prognostic and immunological roles in STAD remain underexplored.Methods: Prognostic SUMOylation-related genes (SRGs) were screened via univariate Cox regression, and patients were stratified into molecular subtypes using unsupervised consensus clustering. A SUMOylation Risk Score (SRS) model was developed using 69 machine learning models across 10 algorithms, with performance evaluated by C-index and AUC. Immune infiltration, pathway enrichment identified key SRGs, and in vitro functional assays were validated.Results: Two molecular subtypes (A/B) with distinct SUMOylation patterns, survival outcomes (log-rank p < 0.001), and immune microenvironments were identified. The random survival forest (RSF)-based SRS model (AUC: 0.97) stratified patients into high-/low-risk groups, where high-risk patients exhibited advanced tumor stages, immune suppression, and elevated TIDE scores (p < 0.001). Functional enrichment linked low-risk groups to genome stability pathways (DNA repair, cell cycle control). In vitro validation confirmed that L3MBTL2 and VHL knockdown promoted proliferation, migration, and invasion in AGS cells (p < 0.01).SUMOylation-driven subtypes with distinct immune and molecular features. The SRS model and functional validation of L3MBTL2/VHL provide actionable insights for personalized STAD management and immunotherapy targeting.

Keywords: SUMOylation Subtypes in STAD via Single-Cell and Machine Learning. 2/42 Stomach adenocarcinoma, Sumoylation, machine learning, L3MBTL2, VHL

Received: 13 Nov 2024; Accepted: 24 Jun 2025.

Copyright: © 2025 Kaiping, Xing, He, Zhai, Jiang, Zhan and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Hongjie Zhan, Department of Gastroenterology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300070, Tianjin, China
Zhigang Zhao, Department of Medical Oncology, Tianjin First Central Hospital, School of Medicine. Nankai University, Tianjin, 300192, China, Tianjin, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material