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      Post-Translational Modifications That Drive Prostate Cancer Progression

      review-article
      Biomolecules
      MDPI
      prostate cancer, post-translational modification, phosphorylation, glycosylation, ubiquitination, SUMOylation, acetylation, lipidation

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          Abstract

          While a protein primary structure is determined by genetic code, its specific functional form is mostly achieved in a dynamic interplay that includes actions of many enzymes involved in post-translational modifications. This versatile repertoire is widely used by cells to direct their response to external stimuli, regulate transcription and protein localization and to keep proteostasis. Herein, post-translational modifications with evident potency to drive prostate cancer are explored. A comprehensive list of proteome-wide and single protein post-translational modifications and their involvement in phenotypic outcomes is presented. Specifically, the data on phosphorylation, glycosylation, ubiquitination, SUMOylation, acetylation, and lipidation in prostate cancer and the enzymes involved are collected. This type of knowledge is especially valuable in cases when cancer cells do not differ in the expression or mutational status of a protein, but its differential activity is regulated on the level of post-translational modifications. Since their driving roles in prostate cancer, post-translational modifications are widely studied in attempts to advance prostate cancer treatment. Current strategies that exploit the potential of post-translational modifications in prostate cancer therapy are presented.

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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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            STRING v10: protein–protein interaction networks, integrated over the tree of life

            The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.
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              Epidemiology of Prostate Cancer

              Prostate cancer is the second most frequent cancer diagnosis made in men and the fifth leading cause of death worldwide. Prostate cancer may be asymptomatic at the early stage and often has an indolent course that may require only active surveillance. Based on GLOBOCAN 2018 estimates, 1,276,106 new cases of prostate cancer were reported worldwide in 2018, with higher prevalence in the developed countries. Differences in the incidence rates worldwide reflect differences in the use of diagnostic testing. Prostate cancer incidence and mortality rates are strongly related to the age with the highest incidence being seen in elderly men (> 65 years of age). African-American men have the highest incidence rates and more aggressive type of prostate cancer compared to White men. There is no evidence yet on how to prevent prostate cancer; however, it is possible to lower the risk by limiting high-fat foods, increasing the intake of vegetables and fruits and performing more exercise. Screening is highly recommended at age 45 for men with familial history and African-American men. Up-to-date statistics on prostate cancer occurrence and outcomes along with a better understanding of the etiology and causative risk factors are essential for the primary prevention of this disease.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Biomolecules
                Biomolecules
                biomolecules
                Biomolecules
                MDPI
                2218-273X
                09 February 2021
                February 2021
                : 11
                : 2
                : 247
                Affiliations
                Laboratory for Epigenomics, Division of Molecular Medicine, Ruđer Bošković Institute, 10000 Zagreb, Croatia; Ivana.Samarzija@ 123456irb.hr
                Article
                biomolecules-11-00247
                10.3390/biom11020247
                7915076
                33572160
                0419eb5b-fd5a-44f8-af59-ecc3013f8d8c
                © 2021 by the author.

                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
                : 10 January 2021
                : 06 February 2021
                Categories
                Review

                prostate cancer,post-translational modification,phosphorylation,glycosylation,ubiquitination,sumoylation,acetylation,lipidation

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