ArticleJournal of pathology and translational medicine2025
E-cadherin expression and tumor-stroma ratio as prognostic biomarkers of peritoneal recurrence in advanced gastric cancer: a digital image analysis-based stratification study.
Article in Journal of pathology and translational medicine, 2025. 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
backgroundGastric cancer remains a significant global health burden, with a high peritoneal recurrence rates after curative surgery. E-cadherin and the tumor-stroma ratio (TSR) have been proposed as prognostic indicators, but their combined prognostic utility remains unclear.
methodsThis retrospective study included 130 patients with T3/T4a gastric cancer who underwent curative gastrectomy at Ulsan University Hospital between 2014 and 2019. Immunohistochemistry for E-cadherin and Vimentin was performed. Digital image analysis using QuPath's object classifier quantified E-cadherin expression and TSR.
resultsLow E-cadherin expression was associated with diffuse-type histology and advanced T stage. Low TSR was linked to younger age, female sex, and XELOX treatment. In Kaplan-Meier analysis, low TSR showed a non-significant trend toward higher peritoneal recurrence (p = .054), while low E-cadherin expression was significantly associated with increased peritoneal recurrence (p = .002). Combined biomarker analysis also revealed a significant difference in recurrence-free survival (RFS) among the four groups (p = .005); patients with both high TSR and high E-cadherin expression experienced the most favorable RFS. In multivariable analysis, E-cadherin expression remained the only independent predictor of peritoneal recurrence (high vs. low; hazard ratio, 0.348; 95% confidence interval, 0.149 to 0.816; p = .015).
conclusionsE-cadherin and TSR reflect distinct tumor biology such as epithelial integrity and stromal composition, and their combined evaluation improves prognostic stratification. Digital image analysis enhances reproducibility and objectivity, supporting their integration into clinical workflows.
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