ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
This article is part of the Research TopicTargeted Therapies in Gastric Cancer: Molecular Signatures and Immune Microenvironment InsightsView all 19 articles
Prognostic Integration of Tumor Microenvironment and Parthanatos-Related Genes in Gastric Cancer: A Machine Learning-Driven Risk Model and Immune Landscape Profiling
Provisionally accepted
- 1Shaanxi Provincial People's Hospital, Xi'an, China
- 2Shaanxi Provincial People's Hospital, XiAn, China
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The tumor microenvironment (TME) modulates parthanatos (PA) to affect gastric cancer (GC) progression and therapy response, but their prognostic and immunotherapeutic roles remain unclear. We screened TME-PA-related prognostic genes via public datasets, then built a prognostic model using a machine learning framework (10 methodologies, 101 algorithm combinations). The optimal RSF-plsRcox hybrid model (C-index > 0.6) identified 7 key genes, with a clinical nomogram (AUC: 0.71–0.75) showing robust predictive performance. High-risk GC patients exhibited an immunosuppressive TME with distinct immune cell infiltration patterns and poor immunotherapy response. RT-qPCR and IHC verified differential expression of key genes; functional assays confirmed CD36/KIT overexpression promoted GC cell proliferation and migration while inhibiting apoptosis. Notably, CD36/KIT inhibition remodelled TME cytokines and, for the first time, activated the PA pathway to induce GC cell death. This study provides a novel GC prognostic model and therapeutic targets, supporting personalized immunotherapy.
Keywords: gastriccancer1, immune microenvironment6, machinelearningalgorithms4, parthanatos3, Prognosis5, tumormicroenvironment2
Received: 27 May 2025; Accepted: 06 Feb 2026.
Copyright: © 2026 Liu, Wu and Liu. 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: Yi Liu
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