Pristimerin exhibits anti-cancer activity by inducing ER stress and AKT/GSK3ß pathway through increasing intracellular ROS production in human esophageal cancer cells.

Pristimerin (Pris), a bioactive triterpenoid compound extracted from the Celastraceae and Hippocrateaceae families, has been reported to exhibit an anti-cancer property on various cancers. However, the effects of Pris on esophageal cancer are poorly investigated. This current study sought to explore the activity and underlying mechanism of Pris against human esophageal squamous cell carcinoma (ESCC) cells. We demonstrated that Pris showed cytotoxicity in TE-1 and TE-10 ESCC cell lines, and significantly inhibited cell viability in a concentration dependent manner. Pris induced G0/G1 phase arrest and triggered apoptosis. It was also observed that the intracellular ROS level was remarkedly increased by Pris treatment. Besides, the function of Pris mediating the activation of ER stress and the inhibition of AKT/GSK3ß signaling pathway in TE-1 and TE-10 cells was further confirmed, which resulted in cell growth inhibition. And moreover, we revealed that all of the above pathways were regulated through ROS generation. In conclusion, our findings suggested that Pris might be considered as a novel natural compound for the developing anti-cancer drug candidate for human esophageal cancer.

An artificial neural network-based radiomics model for predicting the radiotherapy response of advanced esophageal squamous cell carcinoma patients: a multicenter study.

Radiotherapy benefits patients with advanced esophageal squamous cell carcinoma (ESCC) in terms of symptom relief and long-term survival. In contrast, a substantial proportion of ESCC patients have not benefited from radiotherapy. This study aimed to establish and validate an artificial neural network-based radiomics model for the pretreatment prediction of the radiotherapy response of advanced ESCC by using integrated data combined with feasible baseline characteristics of computed tomography. A total of 248 patients with advanced ESCC who underwent baseline CT and received radiotherapy were enrolled in this study and were analyzed by two types of radiomics models, machine learning and deep learning. As a result, the Att. Resnet50 pretrained network model indicated superior performance, with AUCs of 0.876, 0.802 and 0.732 in the training, internal validation, and external validation cohorts, respectively. Similarly, our Att. Resnet50 pretrained network model showed excellent calibration and significant clinical benefit according to the C index and decision curve analysis. Herein, a novel pretreatment radiomics model was established based on deep learning methods and could be used for radiotherapy response prediction in advanced ESCC patients, thus providing reliable evidence for therapeutic decision-making.