Neo-vascularization-based therapeutic perspectives in advanced ovarian cancer.

The process of angiogenesis is well described for its potential role in the development of normal ovaries, and physiological functions as well as in the initiation, progression, and metastasis of ovarian cancer (OC). In advanced stages of OC, cancer cells spread outside the ovary to the pelvic, abdomen, lung, or multiple secondary sites. This seriously limits the efficacy of therapeutic options contributing to fatal clinical outcomes. Notably, a variety of angiogenic effectors are produced by the tumor cells to initiate angiogenic processes leading to the development of new blood vessels, which provide essential resources for tumor survival, dissemination, and dormant micro-metastasis of tumor cells. Multiple proangiogenic effectors and their signaling axis have been discovered and functionally characterized for potential clinical utility in OC. In this review, we have provided the current updates on classical and emerging proangiogenic effectors, their signaling axis, and the immune microenvironment contributing to the pathogenesis of OC. Moreover, we have comprehensively reviewed and discussed the significance of the preclinical strategies, drug repurposing, and clinical trials targeting the angiogenic processes that hold promising perspectives for the better management of patients with OC.

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.

Long non-coding RNAs in recurrent ovarian cancer: Theranostic perspectives.

Nearly 70% of ovarian cancer (OC) patients experience recurrence within the first 2 years after initial treatment. Emerging evidence indicates that long non-coding RNAs (lncRNAs) play a pivotal role in the pathogenesis of OC progression, resistance to therapy and recurrent OC (ROC). Transcriptome profiling studies have reported differential expression patterns of lncRNAs in OC which are related to increased cell invasion, metastasis and drug resistance. In this review, we highlighted the roles of lncRNAs in OC progression and outlined the potential molecular mechanisms by which lncRNAs impact on ROC. Recent advances using lncRNAs as potential biomarkers for screening, detection, prediction, response to therapy and as therapeutic targets are discussed.

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.

A brief overview of antitumoral actions of bruceine D.

Cancer remains the second leading cause of mortality globally. In combating cancer, conventional chemotherapy and/or radiotherapy are administered as first-line therapy. However, these are usually accompanied with adverse side effects that decrease the quality of patient's lives. As such, natural bioactive compounds have gained an attraction in the scientific and medical community as evidence of their anticancer properties and attenuation of side effects mounted. In particular, quassinoids have been found to exhibit a plethora of inhibitory activities such as anti-proliferative effects on tumor development and metastasis. Recently, bruceine D, a quassinoid isolated from the shrub Brucea javanica (L.) Merr. (Simaroubaceae), has come under immense investigation on its antineoplastic properties in various human cancers including pancreas, breast, lung, blood, bone, and liver. In this review, we have highlighted the antineoplastic effects of bruceine D and its mode of actions in different tumor models.