Objective To study the relationship between CDC25A (cell division cycle 25A) expression and the development of gastric adenocarcinoma. hTe effect of artesunate (Art) on CDC25A and gastric cancer cells were also investigated.Methods hTe CDC25A protein expression in gastric adenocarcinoma was detected by lfow cytometry assay. SGC-7901 cells were divided into four groups: control group and 30, 60, 120μmol/L Art groups. Cell apoptosis, cell cycle and CDC25A protein expression in SGC-7901 cells were determined by lfow cytometry atfer the treatment of different concentrations of Art (30, 60, 120μmol/L) for 24h, while the same volume of saline was used in the control.Results CDC25A protein expression level in gastric adenocarcinoma (419.69±21.91) was signiifcantly higher than that in normal gastric tissues (316.11±24.23,P<0.01). hTe cell apoptosis rates of 30, 60, 120μmol/L Art groups (5.48%±0.67%, 12.55%±1.17%, 23.43%±2.18%) were significantly higher than that of control group (0.87%±0.14 %,P<0.05), with an Art dose dependent manner. hTe cell proliferation indices of 30, 60, 120μmol/L Art groups (39.18%±0.53%, 35.71%±0.99%, 31.73%±1.02%) were signiifcantly lower than that of control group (44.12%±2.51%,P<0.01). hTe CDC25A protein expression levels of 30, 60, 120μmol/L Art groups (414.80±4.06, 397.86±3.61, 345.68±7.11) were significantly lower than that of control group (433.99±1.56,P<0.01).ConclusionhTe abnormally increased expression level of CDC25A may be involved in the development of gastric adenocarcinoma. Art can inhibit the growth of SGC-7901 cells by down-regulating the expression of CDC25A protein.

Objective    To explore the application value of machine learning models in predicting postoperative survival of patients with thoracic squamous esophageal cancer. Methods    The clinical data of 369 patients with thoracic esophageal squamous carcinoma who underwent radical esophageal cancer surgery at the Department of Thoracic Surgery of Northern Jiangsu People's Hospital from January 2014 to September 2015 were retrospectively analyzed. There were 279 (75.6%) males and 90 (24.4%) females aged 41-78 years. The patients were randomly divided into a training set (259 patients) and a test set (110 patients) with a ratio of 7 : 3. Variable screening was performed by selecting the best subset of features. Six machine learning models were constructed on this basis and validated in an independent test set. The  performance of the models' predictions was evaluated by area under the curve (AUC), accuracy and logarithmic loss, and the fit of the models was reflected by calibration curves. The best model was selected as the final model. Risk stratification was performed using X-tile, and survival analysis was performed using the Kaplan-Meier method with log-rank test. Results    The 5-year postoperative survival rate of the patients was 67.5%. All clinicopathological characteristics of patients between the two groups in the training and test sets were not statistically different (P>0.05). A total of seven variables, including hypertension, history of smoking, history of alcohol consumption, degree of tissue differentiation, pN stage, vascular invasion and nerve invasion, were included for modelling. The AUC values for each model in the independent test set were: decision tree (AUC=0.796), support vector machine (AUC=0.829), random forest (AUC=0.831), logistic regression (AUC=0.838), gradient boosting machine (AUC=0.846), and XGBoost (AUC=0.853). The XGBoost model was finally selected as the best model, and risk stratification was performed on the training and test sets. Patients in the training and test sets were divided into a low risk group, an intermediate risk group and a high risk group, respectively. In both data sets, the differences in surgical prognosis among three groups were statistically significant (P<0.001). Conclusion    Machine learning models have high value in predicting postoperative prognosis of thoracic squamous esophageal cancer. The XGBoost model outperforms common machine learning methods in predicting 5-year survival of patients with thoracic squamous esophageal cancer, and it has high utility and reliability.