Pterostilbene suppresses gastric cancer proliferation and metastasis by inhibiting oncogenic JAK2/STAT3 signaling: In vitro and in vivo therapeutic intervention.

BACKGROUND: Gastric cancer (GC) represents a significant health burden with dire prognostic implications upon metastasis and recurrence. Pterostilbene (PTE) has been proven to have a strong ability to inhibit proliferation and metastasis in other cancers, while whether PTE exhibits anti-GC activity and its potential mechanism remain unclear. PURPOSE: To explore the efficacy and potential mechanism of PTE in treating GC. METHODS: We employed a comprehensive set of assays, including CCK-8, EdU staining, colony formation, flow cytometry, cell migration, and invasion assays, to detect the effect of PTE on the biological function of GC cells in vitro. The xenograft tumor model was established to evaluate the in vivo anti-GC activity of PTE. Network pharmacology was employed to predict PTE's potential targets and pathways within GC. Subsequently, Western blotting, immunofluorescence, and immunohistochemistry were utilized to analyze protein levels related to the cell cycle, EMT, and the JAK2/STAT3 pathway. RESULTS: Our study demonstrated strong inhibitory effects of PTE on GC cells both in vitro and in vivo. In vitro, PTE significantly induced cell cycle arrest at G0/G1 and S phases and suppressed proliferation, migration, and invasion of GC cells. In vivo, PTE led to a dose-dependent reduction in tumor volume and weight. Importantly, PTE exhibited notable safety, leaving mouse weight, liver function, and kidney function unaffected. The involvement of the JAK2/STAT3 pathway in PTE's anti-GC effect was predicted utilizing network pharmacology. PTE suppressed JAK2 kinase activity by binding to the JH1 kinase structural domain and inhibited the downstream STAT3 signaling pathway. Western blotting confirmed PTE's inhibition of the JAK2/STAT3 pathway and EMT-associated protein levels. The anti-GC effect was partially reversed upon STAT3 activation, validating the pivotal role of the JAK2/STAT3 signaling pathway in PTE's activity. CONCLUSION: Our investigation validates the potent inhibitory effects of PTE on the proliferation and metastasis of GC cells. Importantly, we present novel evidence implicating the JAK2/STAT3 pathway as the key mechanism through which PTE exerts its anti-GC activity. These findings not only establish the basis for considering PTE as a promising lead compound for GC therapeutics but also contribute significantly to our comprehension of the intricate molecular mechanisms underlying its exceptional anti-cancer properties.

Photodynamic Therapy for Inflammatory and Cancerous Diseases of the Intestines: Molecular Mechanisms and Prospects for Application.

Photodynamic therapy (PDT) is a minimally invasive treatment that effectively targets cancer and inflammatory diseases. It has gained recognition for its efficacy, low toxicity, and potential for repeated use. Colorectal cancer (CRC) and inflammatory bowel diseases (IBD), including Crohn's disease (CD), and ulcerative colitis (UC), impose a significant burden on global intestinal health, with increasing incidence and prevalence rates. PDT shows promise as an emerging approach for gastrointestinal disease treatment, particularly IBD and CRC. Extensive preclinical research has demonstrated the safety and effectiveness of PDT for IBD and CRC, while clinical studies are currently underway. This review provides an overview of the underlying mechanisms responsible for the anti-inflammatory and anti-tumor effects of PDT, offering insights into the clinical application of PDT in IBD and CRC treatment. It is expected that this review will serve as a valuable reference for future research on PDT for CRC and IBD, contributing to advancements in the treatment of inflammatory and cancerous diseases of the intestines.

Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study.

BACKGROUND: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images. METHODS: 2333 hematoxylin and eosin-stained pathological pictures of 1037 GC patients were collected from two cohorts to develop our algorithms, Renmin Hospital of Wuhan University (RHWU) and the Cancer Genome Atlas (TCGA). Additionally, we gained 175 digital pictures of 91 GC patients from National Human Genetic Resources Sharing Service Platform (NHGRP), served as the independent external validation set. Two models were developed using artificial intelligence (AI), one named GastroMIL for diagnosing GC, and the other named MIL-GC for predicting outcome of GC. FINDINGS: The discriminatory power of GastroMIL achieved accuracy 0.920 in the external validation set, superior to that of the junior pathologist and comparable to that of expert pathologists. In the prognostic model, C-indices for survival prediction of internal and external validation sets were 0.671 and 0.657, respectively. Moreover, the risk score output by MIL-GC in the external validation set was proved to be a strong predictor of OS both in the univariate (HR = 2.414, P < 0.0001) and multivariable (HR = 1.803, P = 0.043) analyses. The predicting process is available at an online website (https://baigao.github.io/Pathologic-Prognostic-Analysis/). INTERPRETATION: Our study developed AI models and contributed to predicting precise diagnosis and prognosis of GC patients, which will offer assistance to choose appropriate treatment to improve the survival status of GC patients. FUNDING: Not applicable.