Integration of epigenomic and transcriptomic profiling uncovers EZH2 target genes linked to cysteine metabolism in hepatocellular carcinoma.

Enhancer of zeste homolog 2 (EZH2), a key protein implicated in various cancers including hepatocellular carcinoma (HCC), is recognized for its association with epigenetic dysregulation and pathogenesis. Despite clinical explorations into EZH2-targeting therapies, the mechanisms underlying its role in gene suppression in HCC have remained largely unexplored. Here, we integrate epigenomic and transcriptomic analyses to uncover the transcriptional landscape modulated by selective EZH2 inhibition in HCC.

Activation of OSM-STAT3 Epigenetically Regulates Tumor-Promoting Transcriptional Programs in Cervical Cancer.

Despite improvements in preventative strategies, such as regular screenings with Pap tests and human papillomavirus (HPV) tests as well as HPV vaccinations, effective treatment for advanced cervical cancer remains poor. Deregulation of STAT3 is an oncogenic factor that promotes tumorigenesis and epithelial-to-mesenchymal transition (EMT) in various cancers. Oncostatin M (OSM), a pleiotropic cytokine, induces STAT3 activation, exacerbating cervical cancer.

Prognostic Value of BUB1 for Predicting Non-Muscle-Invasive Bladder Cancer Progression.

Non-muscle-invasive bladder cancer (NMIBC) is a common disease with a high recurrence rate requiring lifetime surveillance. Although NMIBC is not life-threatening, it can progress to muscle-invasive bladder cancer (MIBC), a lethal form of the disease. The management of the two diseases differs, and patients with MIBC require aggressive treatments such as chemotherapy and radical cystectomy.

Real-Time Traffic Risk Detection Model Using Smart Mobile Device.

Automatically recognizing dangerous situations for a vehicle and quickly sharing this information with nearby vehicles is the most essential technology for road safety. In this paper, we propose a real-time deceleration pattern-based traffic risk detection system using smart mobile devices. Our system detects a dangerous situation through machine learning on the deceleration patterns of a driver by considering the vehicle's headway distance.