Gastric cancer (GC) is the second most frequent cause of cancer-related mortality in the world, with Eastern Asia having the highest incidence rates. E2F is a family of transcription factor proteins that has a variety of functions, which include control of cell cycle, cell differentiation, DNA damage response and cell death. E2F transcription factors are divided into two subfamilies: transcription activators (E2F transcription factors 1 (E2F1), 2 (E2F2) and 3a (E2F3a)) and repressors (E2F3b, E2F transcription factors 4 (E2F4), 5 (E2F5), 6 (E2F6), 7 (E2F7) and 8 (E2F8)). Studies have demonstrated that E2F had prognostic significance in a number of cancers. However, the entirety of the prognostic roles of E2F mRNA expression in GC has not yet been apparently determined. In the present study, the prognostic value of individual family members of E2F mRNA expression for overall survival (OS) was evaluated by using online Kaplan–Meier Plotter (KM Plotter) database. Our result demonstrated that high expressions of three family members of E2F (E2F1, E2F3, E2F4) mRNA were significantly associated with unfavourable OS in all GC patients. However, increased expressions of E2F2, E2F5, E2F6 and E2F7 were significantly associated with favourable OS, especially for higher clinical stages in GC patients. These results provided a better insight into the prognostic functions of E2F mRNA genes in GC. Although the results should be further verified in clinical trials, our findings may be a favourable prognostic predictor for the development of newer therapeutic drugs in the treatment of GC.

ObjectivesThis study aims to develop and evaluate multiparametric MRI (MP-MRI)-based radiomic models as a noninvasive diagnostic method to predict several biological characteristics of prostate cancer.MethodsA total of 252 patients were retrospectively included who underwent radical prostatectomy and MP-MRI examinations. The prediction characteristics of this study were as follows: Ki67, S100, extracapsular extension (ECE), perineural invasion (PNI), and surgical margin (SM). Patients were divided into training cohorts and validation cohorts in the ratio of 4:1 for each group. After lesion segmentation manually, radiomic features were extracted from MP-MRI images and some clinical factors were also included. Max relevance min redundancy (mRMR) and recursive feature elimination (RFE) based on random forest (RF) were adopted to select features. Six classifiers were included (SVM, KNN, RF, decision tree, logistic regression, XGBOOST) to find the best diagnostic performance among them. The diagnostic efficiency of the construction models was evaluated by ROC curves and quantified by AUC.ResultsRF performed best among the six classifiers for the four groups according to AUC values (Ki67 = 0.87, S100 = 0.80, ECE = 0.85, PNI = 0.82). The performance of SVM was relatively the best for SM (AUC = 0.77). The number and importance of DCE features ranked first in the models of each group. The combined models of MP-MRI and clinical characteristics showed no significant difference compared with MP-MRI models according to Delong’s tests.ConclusionsRadiomics models based on MP-MRI have the potential to predict biological characteristics and are expected to be a noninvasive method to evaluate the risk stratification of prostate cancer.

Pancreatic cancer is the fourth leading cause of cancer-related death with the characteristics of chemoresistance and early metastasis. Panaxadiol, a triterpenoid saponin extracted from the roots of American ginseng, has been proved to display anti-tumor activity in colon cancer. In this study, we found panaxadiol significantly inhibited proliferation, and induced apoptosis in human pancreatic cancer cell lines PANC-1 and Patu8988 in a dose-dependent manner. Furthermore, the expression of apoptosis-related proteins (Bax, Bcl2, Cleaved-caspase3) was detected via western blot and immunofluorescence staining. In addition, panaxadiol was also found to inhibit the migration of pancreatic cancer cells by wound healing and transwell assays. In vivo , the growth of xenograft pancreatic cancer models was also notably suppressed by panaxadiol compared to the control group. Moreover, the down-regulation of JAK2-STAT3 signaling pathway was responsible for the underlying pro-apoptosis mechanism of panaxadiol, and this result was in good agreement with molecular docking analysis between panaxadiol and STAT3. In conclusion, our work comprehensively explored the anti-tumor ability in PANC-1 and Patu8988 cells of panaxadiol and provided a potential choice for the clinical treatment of pancreatic cancer patients.