Fascin actin-bundling protein 1 in human cancer: promising biomarker or therapeutic target?
TL;DR: Fascin actin-bundling protein 1 (FSCN1) as discussed by the authors is a highly conserved actin bundling protein that cross links F-actin microfilaments into tight, parallel bundles and is recognized as a candidate biomarker for multiple cancer types and as a potential therapeutic target.
About: This article is published in Molecular Therapy - Oncolytics.The article was published on 2021-01-20 and is currently open access. It has received 30 citations till now. The article focuses on the topics: Fascin & Biomarker (cell).
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TL;DR: Fascin is a pro-metastatic actin-bundling protein that is upregulated in all metastatic carcinomas as discussed by the authors, and it has been shown that it increases glycolysis in lung cancer to promote tumor growth and metastasis.
23 citations
TL;DR: Zhang et al. as mentioned in this paper investigated the expression profiles and biological effects of fascin actin bundling protein 1 (Fascin, gene name FSCN1) in breast cancer.
Abstract: Ferroptosis, which is characterized by intracellular iron accumulation and lipid peroxidation, is a newly described form of regulated cell death that may play a key role in tumour suppression. In the present study, we investigated the expression profiles and biological effects of fascin actin-bundling protein 1 (Fascin, gene name FSCN1) in breast cancer. In addition, bioinformatics analysis of the TCGA cancer database and gain- and loss-of-function studies showed that Fascin enhances sensitivity to erastin-induced ferroptosis. Mechanistically, Fascin directly interacts with cysteine/glutamate transporter (xCT, gene name SLC7A11) and decreases its stability via the ubiquitin-mediated proteasome degradation pathway. Furthermore, we observed that Fascin is substantially upregulated in tamoxifen-resistant breast cancer cell lines, and drug-resistant cells were also more vulnerable to erastin-induced ferroptosis. Taken together, our findings reveal a previously unidentified role of Fascin in ferroptosis by regulating xCT. Thus, ferroptosis activation in breast cancer with high Fascin level may serve as a potential treatment.
12 citations
TL;DR: The function and intrinsic features of oncogenic circRNAs and lncRNAs that are enriched within exosomes are discussed, which are critical for cell–cell communication within the cancer microenvironment.
Abstract: Circular RNAs (circRNAs) and long noncoding RNAs (lncRNAs) are differentially expressed in gastrointestinal cancers. These noncoding RNAs (ncRNAs) regulate a variety of cellular activities by physically interacting with microRNAs and proteins and altering their activity. It has also been suggested that exosomes encapsulate circRNAs and lncRNAs in cancer cells. Exosomes are then discharged into the extracellular environment, where they are taken up by other cells. As a result, exosomal ncRNA cargo is critical for cell–cell communication within the cancer microenvironment. Exosomal ncRNAs can regulate a range of events, such as angiogenesis, metastasis, immune evasion, drug resistance, and epithelial-to-mesenchymal transition. To set the groundwork for developing novel therapeutic strategies against gastrointestinal malignancies, a thorough understanding of circRNAs and lncRNAs is required. In this review, we discuss the function and intrinsic features of oncogenic circRNAs and lncRNAs that are enriched within exosomes.
8 citations
TL;DR: An array of comprehensive models and grading methods, including multiple prognostic factors, to predict the prognosis of PAs are introduced, which have shown good effectiveness and would be beneficial for predicting PA recurrence.
Abstract: Pituitary adenomas (PAs) are benign lesions; nonetheless, some PAs exhibit aggressive behaviors, which lead to recurrence. The impact of pituitary dysfunction, invasion-related risks, and other complications considerably affect the quality of life of patients with recurrent PAs. Reliable prognostic factors are needed for recurrent PAs but require confirmation. This review summarizes research progress on two aspects—namely, the clinical and biological factors (biomarkers) for recurrent PAs. Postoperative residue, age, immunohistological subtypes, invasion, tumor size, hormone levels, and postoperative radiotherapy can predict the risk of recurrence in patients with PAs. Additionally, biomarkers such as Ki-67, p53, cadherin, pituitary tumor transforming gene, matrix metalloproteinase-9, epidermal growth factor receptor, fascin actin-bundling protein 1, cyclooxygenase-2, and some miRNAs and lncRNAs may be utilized as valuable tools for predicting PA recurrence. As no single marker can independently predict PA recurrence, we introduce an array of comprehensive models and grading methods, including multiple prognostic factors, to predict the prognosis of PAs, which have shown good effectiveness and would be beneficial for predicting PA recurrence.
8 citations
TL;DR: The TP73 gene belongs to the p53 family comprised by p53, p63, and p73, and it is essential for the organization and homeostasis of different complex microenvironments, like the neurogenic niche, which supports the neural progenitor cells and the ependyma, the male and female reproductive organs, the respiratory epithelium or the vascular network as discussed by the authors.
Abstract: The TP73 gene belongs to the p53 family comprised by p53, p63, and p73. In response to physiological and pathological signals these transcription factors regulate multiple molecular pathways which merge in an ensemble of interconnected networks, in which the control of cell proliferation and cell death occupies a prominent position. However, the complex phenotype of the Trp73 deficient mice has revealed that the biological relevance of this gene does not exclusively rely on its growth suppression effects, but it is also intertwined with other fundamental roles governing different aspects of tissue physiology. p73 function is essential for the organization and homeostasis of different complex microenvironments, like the neurogenic niche, which supports the neural progenitor cells and the ependyma, the male and female reproductive organs, the respiratory epithelium or the vascular network. We propose that all these, apparently unrelated, developmental roles, have a common denominator: p73 function as a tissue architect. Tissue architecture is defined by the nature and the integrity of its cellular and extracellular compartments, and it is based on proper adhesive cell-cell and cell-extracellular matrix interactions as well as the establishment of cellular polarity. In this work, we will review the current understanding of p73 role as a neurogenic niche architect through the regulation of cell adhesion, cytoskeleton dynamics and Planar Cell Polarity, and give a general overview of TAp73 as a hub modulator of these functions, whose alteration could impinge in many of the Trp73 -/- phenotypes.
7 citations
References
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TL;DR: GEPIA (Gene Expression Profiling Interactive Analysis) fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources.
Abstract: Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
5,980 citations
TL;DR: Better understanding of the overall process of biomarker discovery and validation and of the challenges and strategies inherent in each phase should improve experimental study design, in turn increasing the efficiency of biomarkers development and facilitating the delivery and deployment of novel clinical tests.
Abstract: Better biomarkers are urgently needed to improve diagnosis, guide molecularly targeted therapy and monitor activity and therapeutic response across a wide spectrum of disease. Proteomics methods based on mass spectrometry hold special promise for the discovery of novel biomarkers that might form the foundation for new clinical blood tests, but to date their contribution to the diagnostic armamentarium has been disappointing. This is due in part to the lack of a coherent pipeline connecting marker discovery with well-established methods for validation. Advances in methods and technology now enable construction of a comprehensive biomarker pipeline from six essential process components: candidate discovery, qualification, verification, research assay optimization, biomarker validation and commercialization. Better understanding of the overall process of biomarker discovery and validation and of the challenges and strategies inherent in each phase should improve experimental study design, in turn increasing the efficiency of biomarker development and facilitating the delivery and deployment of novel clinical tests.
1,702 citations
Washington University in St. Louis1, Johns Hopkins University2, Discovery Institute3, Genome Institute of Singapore4, University of Miami5, Broad Institute6, Harvard University7, University of Texas MD Anderson Cancer Center8, Memorial Sloan Kettering Cancer Center9, Cornell University10, University of Cambridge11, Catalan Institution for Research and Advanced Studies12, University of California, Santa Cruz13, Baylor College of Medicine14
TL;DR: This study reports a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations, identifying 299 driver genes with implications regarding their anatomical sites and cancer/cell types.
1,623 citations
TL;DR: The purpose of this commentary is to define a formal structure to guide the process of biomarker development and to provide a checklist of issues that should be addressed at each phase of development before proceeding to the next.
Abstract: Recent developments in such areas of research as geneexpression microarrays, proteomics, and immunology offer new approaches to cancer screening (1). The surge in research to develop cancer-screening biomarkers prompted the establishment of the Early Detection Research Network (EDRN) by the National Cancer Institute (2). The purpose of the EDRN is to coordinate research among biomarker-development laboratories, biomarker-validation laboratories, clinical repositories, and population-screening programs. By coordination of research efforts, the hope is to facilitate collaboration and to promote efficiency and rigor in research. With the goals of the EDRN in mind, the purpose of this commentary is to define a formal structure to guide the process of biomarker development. We categorize the development into five phases that a biomarker needs to pass through to produce a useful population-screening tool. The phases of research are generally ordered according to the strength of evidence that each provides in favor of the biomarker, from weakest to strongest. In addition, the results of earlier phases are generally necessary to design later phases. Therapeutic drug development has had such a structure in place for some time (3). The clinical phases of testing a new cancer drug are as follows: phase 1, determinations of toxicity, pharmacokinetics, and optimal dose levels; phase 2, determinations of biologic efficacy; and phase 3, definitive controlled trials of effects on clinical endpoints. For each phase, guidelines exist for subject selection, outcome measures, relevant comparisons for evaluating study results, and so forth. Although deviations are common, the basic structure facilitates coherent, thorough, and efficient development of new therapies. A phased approach has also been proposed for prevention trials (4,5). In a similar vein, we hope that our proposed guidelines or some related construct will facilitate the development of biomarker-based screening tools for early detection of cancer. Although deviations from these guidelines may be necessary in specific applications, our proposal will, at the minimum, provide a checklist of issues that should be addressed at each phase of development before proceeding to the next.
1,491 citations
TL;DR: The roles of miRNAs and lncRNAs in cancer are summarized, with a focus on the recently identified novel mechanisms of action, and the current strategies in designing ncRNA-targeting therapeutics are discussed.
Abstract: The first cancer-targeted microRNA (miRNA) drug - MRX34, a liposome-based miR-34 mimic - entered Phase I clinical trials in patients with advanced hepatocellular carcinoma in April 2013, and miRNA therapeutics are attracting special attention from both academia and biotechnology companies. Although miRNAs are the most studied non-coding RNAs (ncRNAs) to date, the importance of long non-coding RNAs (lncRNAs) is increasingly being recognized. Here, we summarize the roles of miRNAs and lncRNAs in cancer, with a focus on the recently identified novel mechanisms of action, and discuss the current strategies in designing ncRNA-targeting therapeutics, as well as the associated challenges.
1,221 citations