Machine Learning for Endometrial Cancer Prediction and Prognostication.

Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.

The pleiotropic role of transcription factor STAT3 in oncogenesis and its targeting through natural products for cancer prevention and therapy.

Signal transducer and activator of transcription 3 (STAT3) is one of the crucial transcription factors, responsible for regulating cellular proliferation, cellular differentiation, migration, programmed cell death, inflammatory response, angiogenesis, and immune activation. In this review, we have discussed the classical regulation of STAT3 via diverse growth factors, cytokines, G-protein-coupled receptors, as well as toll-like receptors. We have also highlighted the potential role of noncoding RNAs in regulating STAT3 signaling. However, the deregulation of STAT3 signaling has been found to be associated with the initiation and progression of both solid and hematological malignancies. Additionally, hyperactivation of STAT3 signaling can maintain the cancer stem cell phenotype by modulating the tumor microenvironment, cellular metabolism, and immune responses to favor drug resistance and metastasis. Finally, we have also discussed several plausible ways to target oncogenic STAT3 signaling using various small molecules derived from natural products.