ArticleClinical and translational gastroenterology2026
Machine Learning-Based Pathomics Signature in Predicting MSH2 Expression and Prognosis in Gastric Cancer.
Article in Clinical and translational gastroenterology, 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
introductionGastric cancer (GC) is one of the most prevalent and lethal gastrointestinal malignancies. MutS homolog 2 (MSH2), a DNA mismatch repair protein, has emerged as a promising prognostic biomarker. However, traditional histopathological evaluation is limited by restricted fields compared with whole-slide imaging. This study aimed to investigate whether machine learning-derived digital pathomics features could predict MSH2 expression and clinical outcomes in GC.
methodsHematoxylin and eosin-stained whole-slide images from 234 patients were analyzed to extract quantitative pathological features. A pathomics score (PS) was developed to estimate MSH2 expression. The association between PS and overall survival (OS) was assessed using univariate and multivariate Cox regression. Survival differences between high-PS and low-PS groups were evaluated using Kaplan-Meier analysis. Functional enrichment and immune infiltration analyses were performed to explore potential biological mechanisms.
resultsDigital image analysis identified pathomics features associated with MSH2 expression. The PS served as a surrogate marker for MSH2 and effectively stratified patients into prognostic subgroups with significant different OS. High PS was associated with features suggestive of a stronger antitumor immune response, whereas low PS was linked to an immunosuppressive microenvironment. DISCUSSION: The machine learning-derived pathomics signature shows potential in predicting MSH2 expression. It can serve as a complementary research tool and provide clinically meaningful prognostic information for GC.
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