Ke Sun1,2, Meng Qing Xu1,3, Hai Jun Zhang1, Dan Dan Zhang1,2, Wen Yue1,2, Miao Miao Ma1,2, Lin Tao1,2, Wen Jie Zhang1,2. 1. Department of Pathology, The First Affiliated Hospital, School of Medicine, Shihezi University Shihezi 832002, Xinjiang, China. 2. Key Laboratory for Xinjiang Endemic and Ethnic Diseases, School of Medicine, Shihezi University Shihezi 832002, Xinjiang, China. 3. Department of Gastroenterology, Jinling Hospital Nanjing 210000, Jiangsu, China.
Abstract
OBJECTIVES: TNM staging of gastric cancer (GC) is useful in predicting prognosis, but its definition is only possible after surgery. It is therefore desirable to develop a method that can predict prognosis and assist management options before surgery. METHODS: This study investigated 110 GC patients after radical gastrectomy and followed-up for 136 months. Patients' complete clinicopathological data were collected and gastroscopically biopsied or surgically resected tissues were examined for the expression of Her-2, nm-23, CEA and phosphorylated Stat3 (p-Stat3) using immunohistochemistry (IHC). Univariate and multivariate ROC curves, Kaplan-Meier survival curves, and SPSS Version 22.0 and R (version 3.6.1) statistical software were used to analyze the data. RESULTS: Three major findings were observed: (1) Tissue levels of p-Stat3, Her-2, CEA and nm-23 were correlated with GC patients' survival probability termed as survival prediction power (SPP). (2) Using 5-year survival as an end-point, the SPP of the p-Stat3+Her-2 combination was stronger (AUC=0.867) than that of TNM staging (AUC=0.755). (3) Using cut-off values derived from ROC curves, Kaplan-Meier analyses showed that the p-Stat3+Her-2 molecular combination could clearly predict overall survival rates between the predictive low-risk patients (69.2%) and the predictive high-risk patients (13.2%) with a discriminative difference as high as 56.0%. CONCLUSIONS: We conclude that area under the ROC curve (AUC) can be used to quantify SPP powers for biomarkers, making cross-comparisons possible among different survival predictors. This study has first established a multi-factor survival prediction model by which the p-Stat3+Her-2 combination has the best discriminative capability to differentiate low-risk patients from high-risk patients in terms of survival prognosis. AJTR
OBJECTIVES: TNM staging of gastric cancer (GC) is useful in predicting prognosis, but its definition is only possible after surgery. It is therefore desirable to develop a method that can predict prognosis and assist management options before surgery. METHODS: This study investigated 110 GC patients after radical gastrectomy and followed-up for 136 months. Patients' complete clinicopathological data were collected and gastroscopically biopsied or surgically resected tissues were examined for the expression of Her-2, nm-23, CEA and phosphorylated Stat3 (p-Stat3) using immunohistochemistry (IHC). Univariate and multivariate ROC curves, Kaplan-Meier survival curves, and SPSS Version 22.0 and R (version 3.6.1) statistical software were used to analyze the data. RESULTS: Three major findings were observed: (1) Tissue levels of p-Stat3, Her-2, CEA and nm-23 were correlated with GC patients' survival probability termed as survival prediction power (SPP). (2) Using 5-year survival as an end-point, the SPP of the p-Stat3+Her-2 combination was stronger (AUC=0.867) than that of TNM staging (AUC=0.755). (3) Using cut-off values derived from ROC curves, Kaplan-Meier analyses showed that the p-Stat3+Her-2 molecular combination could clearly predict overall survival rates between the predictive low-risk patients (69.2%) and the predictive high-risk patients (13.2%) with a discriminative difference as high as 56.0%. CONCLUSIONS: We conclude that area under the ROC curve (AUC) can be used to quantify SPP powers for biomarkers, making cross-comparisons possible among different survival predictors. This study has first established a multi-factor survival prediction model by which the p-Stat3+Her-2 combination has the best discriminative capability to differentiate low-risk patients from high-risk patients in terms of survival prognosis. AJTR
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