ArticleFrontiers in immunology2026
Prognostic integration of tumor microenvironment and parthanatos-related genes in gastric cancer: a machine learning-driven risk model and immune landscape profiling.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The tumor microenvironment (TME) plays a pivotal role in the progression of gastric cancer (GC) and its response to treatment, particularly by modulating parthanatos (PA). However, the prognostic significance of TME and PA, as well as their potential roles in immunotherapy for GC, remain incompletely understood. Methods: Using publicly available data, an initial gene screening was combined with differential expression analysis and univariate Cox regression to identify prognostic markers associated with TME and PA. A comprehensive machine learning framework, testing 101 algorithm combinations across 10 methodologies, was then applied. Model selection prioritized C-index performance, with the final model enabling effective patient risk stratification, as validated by Receiver Operating Characteristic (ROC) curves. Multivariate analysis subsequently identified independent prognostic factors, which were used to construct a clinical nomogram. Immune characteristics across different risk groups were compared, immunofluorescence staining of gastric cancer and paired paracancerous tissues assessed immune cell infiltration and prognostic gene-monocyte correlation. Biomarker expression patterns were confirmed Results: The multi-algorithm analysis identified the RSF-plsRcox hybrid model as the most accurate, consistently achieving C-index values greater than 0.6 across all datasets. This model identified seven clinically significant genes ( Conclusions: This study innovatively integrated TME-RGs and PA-RGs to construct a machine learning GC prognostic model (7 key genes:
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