Recent studies have suggested that the metastasis-associated colon cancer1 (MACC1) gene can promote tumor proliferation, invasion and metastasis through an upregulation of c-Met expression. However, its role in gastric cancer is controversial. Our study investigated expression of MACC1 and c-Met in gastric cancer, as well as correlated this with clinicopathological parameters.

Purpose: Patients with advanced prostate cancer (PCa) often develop castration-resistant PCa (CRPC) with poor prognosis. Prognostic information obtained from multiparametric magnetic resonance imaging (mpMRI) and histopathology specimens can be effectively utilized through artificial intelligence (AI) techniques. The objective of this study is to construct an AI-based CRPC progress prediction model by integrating multimodal data.Methods and materials: Data from 399 patients diagnosed with PCa at three medical centers between January 2018 and January 2021 were collected retrospectively. We delineated regions of interest (ROIs) from 3 MRI sequences viz, T2WI, DWI, and ADC and utilized a cropping tool to extract the largest section of each ROI. We selected representative pathological hematoxylin and eosin (H&E) slides for deep-learning model training. A joint combined model nomogram was constructed. ROC curves and calibration curves were plotted to assess the predictive performance and goodness of fit of the model. We generated decision curve analysis (DCA) curves and Kaplan–Meier (KM) survival curves to evaluate the clinical net benefit of the model and its association with progression-free survival (PFS).Results: The AUC of the machine learning (ML) model was 0.755. The best deep learning (DL) model for radiomics and pathomics was the ResNet-50 model, with an AUC of 0.768 and 0.752, respectively. The nomogram graph showed that DL model contributed the most, and the AUC for the combined model was 0.86. The calibration curves and DCA indicate that the combined model had a good calibration ability and net clinical benefit. The KM curve indicated that the model integrating multimodal data can guide patient prognosis and management strategies.Conclusion: The integration of multimodal data effectively improves the prediction of risk for the progression of PCa to CRPC.

Objective: The objective of this research is to scrutinize adverse events (AEs) linked to Trifluridine/Tipiracil (TFTD/TPI), using data from the FDA Adverse Event Reporting System (FAERS) database.Methods: The AEs data related to TFTD/TPI were collected from the fourth quarter of 2015 through the fourth quarter of 2023. After normalizing the data, multiple signal quantification techniques including Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian approaches such as Bayesian Confidence Propagation Neural Network (BCPNN) and the Multi-item Gamma Poisson Shrinker (MGPS) were used for overall and subgroup analysis and visualization analyses were performed.Results: From the FAERS database, we analyzed 13,520,073 reports, identifying 8,331 as primary suspect (PS) AEs for TFTD/TPI, occurring across 27 organ systems. The study retained 99 significant disproportionality Preferred Terms (PTs) across four algorithms and unveiled unexpected serious AEs such as iron deficiency and intestinal perforation, hepatic failure, cholangitis and so on. The median onset of TFTD/TPI-associated AEs was 44 days (IQR 20-97 days), with most occurring within the first 30 days of treatment.Conclusion: This research uncovers critical new safety signals for TFTD/TPI, supporting its clinical monitoring and risk identification.

By the year 2035 more than 4 billion people might be affected by obesity and being overweight. Adipocyte-derived Extracellular Vesicles (ADEVs/ADEV-singular) are essential for communication between the tumor microenvironment (TME) and obesity, emerging as a prominent mechanism of tumor progression. Adipose tissue (AT) becomes hypertrophic and hyperplastic in an obese state resulting in insulin resistance in the body. This modifies the energy supply to tumor cells and simultaneously stimulates the production of pro-inflammatory adipokines. In addition, obese AT has a dysregulated cargo content of discharged ADEVs, leading to elevated amounts of pro-inflammatory proteins, fatty acids, and carcinogenic microRNAs. ADEVs are strongly associated with hallmarks of cancer (proliferation and resistance to cell death, angiogenesis, invasion, metastasis, immunological response) and may be useful as biomarkers and antitumor therapy strategy. Given the present developments in obesity and cancer-related research, we conclude by outlining significant challenges and significant advances that must be addressed expeditiously to promote ADEVs research and clinical applications.