ArticleAnnals of medicine2025
A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles.
Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Development and clinical application of a postoperative complication prognosis prediction model for gastric cancer patients based on automated machine learning with body fat rate.Frontiers in oncology · 2026Article
- miR-3921 functions as a tumor suppressor and negatively regulates RIPK1 in gastric cancer.PeerJ · 2026Article
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Authors and funding
15 authors.
Funding
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Abstract
backgroundGastric cancer (GC) is a highly heterogeneous disease, and the response of patients to clinical treatment varies substantially. There is no satisfactory strategy for predicting curative effects to date. We aimed to explore a new method for predicting the clinical efficacy of GC treatment based on immune variables detected via flow cytometry.
methodsWe collected 394 tumour tissues from GC patients for flow cytometry analysis and gating analysis of tumour-infiltrating immune cells (TIICs). Unsupervised consensus clusters were generated from the cohort to classify patients into different phenogroups, and their clinical characteristics were examined. The derived model was evaluated via principal component analysis and
resultsAll patients were classified based on their TIIC profiles into the C1 (characterized by low CD45 negative cell, high lymphocyte, high neutrophil and low CD3 + T cell levels), C2 (characterized by high CD8 + CD279+ cell and low CD4+ Th and CD8+ Tc cell numbers) and C3 (characterized by high CD4 + CD25+ and Treg cell levels) phenogroups. Patients from the three clusters had varied pathologies, MMR statuses and TIIC distribution patterns (
conclusionsThe machine learning model developed was found to be effective model at predicting the prognosis of patients with GC and their TIIC profiles for risk stratification in clinical settings.
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