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      Investigating the Role of Telomere and Telomerase Associated Genes and Proteins in Endometrial Cancer

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          Abstract

          Endometrial cancer (EC) is the commonest gynaecological malignancy. Current prognostic markers are inadequate to accurately predict patient survival, necessitating novel prognostic markers, to improve treatment strategies. Telomerase has a unique role within the endometrium, whilst aberrant telomerase activity is a hallmark of many cancers. The aim of the current in silico study is to investigate the role of telomere and telomerase associated genes and proteins (TTAGPs) in EC to identify potential prognostic markers and therapeutic targets. Analysis of RNA-seq data from The Cancer Genome Atlas identified differentially expressed genes (DEGs) in EC (568 TTAGPs out of 3467) and ascertained DEGs associated with histological subtypes, higher grade endometrioid tumours and late stage EC. Functional analysis demonstrated that DEGs were predominantly involved in cell cycle regulation, while the survival analysis identified 69 DEGs associated with prognosis. The protein-protein interaction network constructed facilitated the identification of hub genes, enriched transcription factor binding sites and drugs that may target the network. Thus, our in silico methods distinguished many critical genes associated with telomere maintenance that were previously unknown to contribute to EC carcinogenesis and prognosis, including NOP56, WFS1, ANAPC4 and TUBB4A. Probing the prognostic and therapeutic utility of these novel TTAGP markers will form an exciting basis for future research.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Hallmarks of Cancer: The Next Generation

            The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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              Cytoscape: a software environment for integrated models of biomolecular interaction networks.

              Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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                Author and article information

                Journal
                Methods Protoc
                Methods Protoc
                mps
                Methods and Protocols
                MDPI
                2409-9279
                03 September 2020
                September 2020
                : 3
                : 3
                : 63
                Affiliations
                [1 ]Department of Women’s and Children’s Health, University of Liverpool, Crown St, Liverpool L69 7ZX, UK; a.j.bradfield@ 123456liverpool.ac.uk (A.B.); jadrury@ 123456liverpool.ac.uk (J.D.); C.J.Hill1@ 123456liv.ac.uk (C.J.H.)
                [2 ]Faculty of Health and Life Sciences, University of Liverpool, Brownlow Hill, Liverpool L69 7ZX, UK; Lucy.Button@ 123456liverpool.ac.uk
                [3 ]Institute of Life Course and Medical Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L7 8TX, UK; Daniel.Green@ 123456liverpool.ac.uk
                [4 ]Liverpool Women’s NHS Foundation Trust, Member of Liverpool Health Partners, Liverpool L8 7SS, UK
                Author notes
                Author information
                https://orcid.org/0000-0003-3831-4569
                https://orcid.org/0000-0003-0270-0150
                Article
                mps-03-00063
                10.3390/mps3030063
                7565490
                32899298
                6ec59be3-79b6-4cb9-ac2d-4a21d10e0874
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 14 July 2020
                : 30 August 2020
                Categories
                Article

                telomere,telomerase,endometrial cancer,prognosis,bioinformatics analysis,transcriptome,tcga

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