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Active Stat3 and Her-2 as combined survival predictors show superiority to TNM staging system for postoperative patients with gastric cancer.

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.