Nomogram model including LATS2 expression was constructed to predict the prognosis of advanced gastric cancer after surgery.

ABSTRACT

BACKGROUND: Gastric cancer is a leading cause of cancer-related deaths worldwide. Prognostic assessments are typically based on the tumor-node-metastasis (TNM) staging system, which does not account for the molecular heterogeneity of this disease. LATS2, a tumor suppressor gene involved in the Hippo signaling pathway, has been identified as a potential prognostic biomarker in gastric cancer. AIM: To construct and validate a nomogram model that includes LATS2 expression to predict the survival prognosis of advanced gastric cancer patients following radical surgery, and compare its predictive performance with traditional TNM staging. METHODS: A retrospective analysis of 245 advanced gastric cancer patients from the Fourth Hospital of Hebei Medical University was conducted. The patients were divided into a training group (171 patients) and a validation group (74 patients) to develop and test our prognostic model. The performance of the model was determined using C-indices, receiver operating characteristic curves, calibration plots, and decision curves. RESULTS: The model demonstrated a high predictive accuracy with C-indices of 0.829 in the training set and 0.862 in the validation set. Area under the curve values for three-year and five-year survival prediction were significantly robust, suggesting an excellent discrimination ability. Calibration plots confirmed the high concordance between the predictions and actual survival outcomes. CONCLUSION: We developed a nomogram model incorporating LATS2 expression, which significantly outperformed conventional TNM staging in predicting the prognosis of advanced gastric cancer patients postsurgery. This model may serve as a valuable tool for individualized patient management, allowing for more accurate stratification and improved clinical outcomes. Further validation in larger patient cohorts will be necessary to establish its generalizability and clinical utility.