Clinical and translational gastroenterology · 2026
Machine Learning-Based Pathomics Signature in Predicting MSH2 Expression and Prognosis in Gastric Cancer.
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Zhang Zheng-Rong, Wang Yu, Yan Wen-Wu, Li Hao-Ran, Cheng Zheng-Wu, Han Ting, Zhang Chao, Wang Xiao-Ming
Аннотация
INTRODUCTION: Gastric 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.
METHODS: Hematoxylin 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.
RESULTS: Digital 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.
METHODS: Hematoxylin 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.
RESULTS: Digital 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.