Due to the high effectiveness of cancer screening and therapies, the diagnosis of second primary cancers (SPCs) has increased in women with endometrial cancer (EC). However, there’s no previous literature mentioned about adequate evidence to support screening for SPCs in endometrial cancer. This study was aimed to develop effective risk prediction models of second primary endometrial cancer in women with obesity (Body-mass index; BMI > 25) and this study includes datasets of the incidence of SPCs and the other risks of SPCs in 4480 primary cancer survivors by a hospital-based cancer registry database. In our study, we found the obesity played a key role in SPCs. There’re 10 independent variables used as predicting variables, which corelated to obesity should be monitored for the early detection of SPCs in endometrial cancer. In conclusion, it is a promising SPCs prediction. The proposed scheme can support the important influence of obesity and clinical data representations in all cases after primary treatments. Our results suggested that obesity is still a crucial risk factor to SPCs in endometrial cancer.

Praeruptorin A(PA), a naturally existing pyranocumarin, is isolated from the dried root of Peucedanum praeruptorum Dunn. So far the anti-cancer effect and molecular mechanism behind Praeruptorin A action in human cervical cancer HeLa cells remain unknown. In the present study, we find that PA reduces cell proliferation and colony formation of human cervical cancer HeLa cells through inducing cell cycle arrest at G0/G1 phase. PA-upregulated p21 and p27 proteins are observed, accompanied with inhibition of cyclin D1 and S-phase kinase-associated protein 2(Skp2) proteins expression. PA could significantly inhibit migration and invasion of human HeLa cells. Meanwhile, PA significantly reduces invasive protein expression of matrix metalloproteinase-2 (MMP-2), and increases protein expression of tissue inhibitor of metalloproteinase-2 (TIMP-2). PA is observed to possess the capacity in suppressing ERK1/2 activation. PD98059 (ERK specific inhibitor) significantly enhances PA-induced downregulation of MMP-2 expression, and upregulation of TIMP-2 expression. Moreover, we found that PA treatment notably inhibits 12-O-tetradecanoylphorbol-13-acetate(TPA)-upregulated ERK1/2 activation, MMP-2 expression, cellular migration and invasion in human HeLa cells. Taken together, these findings are the first to demonstrate the anti-cancer activity of PA, which may act as a promising therapeutic agent for the treatment of human cervical cancer.

This study was to identify clinical cancer biomarkers for recurrent gastric cancer survivors using artificial intel-ligence. From a hospital-based cancer registry database in Taiwan, the datasets of the incidence of recurrence and clinical risk features were included in 2476 gastric cancer survivors. We benchmarked Random forest with MLP, C4.5, AdaBoost, and Bagging algorithms on metrics and leveraged synthetic minority oversampling technique (SMOTE) for imbalanced dataset issues, cost-sensitive learning for risk assessment, and SHapley Additive ex-Planations (SHAP) for feature importance analysis in this study. Our proposed Random forest outperformed the other models with an accuracy of 87.9%, a recall rate of 90.5%, an accuracy rate of 86% and F1 of 88.2% on re-current category by a 10-fold cross-validation in a balanced dataset. We identified clinical features of recurrent gastric cancer, which are the top five features, stage, number of regional lymph node involvement, Helicobacter pylori, BMI(body mass index), and gender, which significantly affect the prediction model's output, are worth paying attention to in the following causal effect analysis. Using an artificial intelligence model, the risk factors for recurrent gastric cancer could be identified and cost-effectively ranked according to their feature importance. In addition, they should be crucial clinical features to provide physicians with the knowledge to screen high-risk patients in gastric cancer survivors as well.