RESUMO
A hyperactive tumour microenvironment (TME) drives unrestricted cancer cell survival, drug resistance, and metastasis in ovarian carcinoma (OC). However, therapeutic targets within the TME for OC remain elusive, and efficient methods to quantify TME activity are still limited. Herein, we employed an integrated bioinformatics approach to determine which immune-related genes (IRGs) modulate the TME and further assess their potential theragnostic (therapeutic + diagnostic) significance in OC progression. Using a robust approach, we developed a predictive risk model to retrospectively examine the clinicopathological parameters of OC patients from The Cancer Genome Atlas (TCGA) database. The validity of the prognostic model was confirmed with data from the International Cancer Genome Consortium (ICGC) cohort. Our approach identified nine IRGs, AKT2, FGF7, FOS, IL27RA, LRP1, OBP2A, PAEP, PDGFRA, and PI3, that form a prognostic model in OC progression, distinguishing patients with significantly better clinical outcomes in the low-risk group. We validated this model as an independent prognostic indicator and demonstrated enhanced prognostic significance when used alongside clinical nomograms for accurate prediction. Elevated LRP1 expression, which indicates poor prognosis in bladder cancer (BLCA), OC, low-grade gliomas (LGG), and glioblastoma (GBM), was also associated with immune infiltration in several other cancers. Significant correlations with immune checkpoint genes (ICGs) highlight the potential importance of LRP1 as a biomarker and therapeutic target. Furthermore, gene set enrichment analysis highlighted LRP1's involvement in metabolism-related pathways, supporting its prognostic and therapeutic relevance also in BLCA, OC, low-grade gliomas (LGG), GBM, kidney cancer, OC, BLCA, kidney renal clear cell carcinoma (KIRC), stomach adenocarcinoma (STAD), and stomach and oesophageal carcinoma (STES). Our study has generated a novel signature of nine IRGs within the TME across cancers, that could serve as potential prognostic predictors and provide a valuable resource to improve the prognosis of OC.
Assuntos
Biomarcadores Tumorais , Biologia Computacional , Regulação Neoplásica da Expressão Gênica , Proteína-1 Relacionada a Receptor de Lipoproteína de Baixa Densidade , Neoplasias Ovarianas , Microambiente Tumoral , Humanos , Feminino , Neoplasias Ovarianas/genética , Neoplasias Ovarianas/patologia , Neoplasias Ovarianas/metabolismo , Neoplasias Ovarianas/mortalidade , Prognóstico , Biologia Computacional/métodos , Microambiente Tumoral/genética , Biomarcadores Tumorais/genética , Biomarcadores Tumorais/metabolismo , Proteína-1 Relacionada a Receptor de Lipoproteína de Baixa Densidade/genética , Proteína-1 Relacionada a Receptor de Lipoproteína de Baixa Densidade/metabolismoRESUMO
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.
RESUMO
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.
Assuntos
Neoplasias dos Genitais Masculinos , Neoplasias Ovarianas , Humanos , Feminino , Masculino , Carcinoma Epitelial do Ovário , Neoplasias Ovarianas/patologia , Neovascularização Patológica/tratamento farmacológico , Transdução de Sinais , Microambiente TumoralRESUMO
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.
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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.
Assuntos
Recidiva Local de Neoplasia/genética , Neoplasias Ovarianas/genética , RNA Longo não Codificante/genética , Antineoplásicos/farmacologia , Antineoplásicos/uso terapêutico , Biomarcadores Tumorais/genética , Progressão da Doença , Resistencia a Medicamentos Antineoplásicos/efeitos dos fármacos , Feminino , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Humanos , Recidiva Local de Neoplasia/tratamento farmacológico , Neoplasias Ovarianas/tratamento farmacológico , Medicina de Precisão , RNA Longo não Codificante/efeitos dos fármacosRESUMO
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.