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      Systems pharmacology of mifepristone (RU486) reveals its 47 hub targets and network: Comprehensive analysis and pharmacological focus on FAK-Src-Paxillin complex

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          Abstract

          Mifepristone (RU486), a synthetic steroid compound used as an abortifacient drug, has received considerable attention to its anticancer activity recently. To explore the possibility of using mifepristone as a cancer metastasis chemopreventive, we performed a systems pharmacology analysis of mifepristone-related molecules in the present study. Data were collected by using Natural Language Processing (NLP) and 513 mifepristone-related genes were dug out and classified functionally using a gene ontology (GO) hierarchy, followed by KEGG pathway enrichment analysis. Potential signal pathways and targets involved in cancer were obtained by integrative network analysis. Total thirty-three proteins were involved in focal adhesion-the key signaling pathway associated with cancer metastasis. Molecular and cellular assays further demonstrated that mifepristone had the ability to prevent breast cancer cells from migration and interfere with their adhesion to endothelial cells. Moreover, mifepristone inhibited the expression of focal adhesion kinase (FAK), paxillin, and the formation of FAK/Src/Paxillin complex, which are correlated with cell adhesion and migration. This study set a good example to identify chemotherapeutic potential seamlessly from systems pharmacology to cellular pharmacology, and the revealed hub genes may be the promising targets for cancer metastasis chemoprevention.

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          GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways.

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            Molecular circuits shared by placental and cancer cells, and their implications in the proliferative, invasive and migratory capacities of trophoblasts.

            Trophoblast research over the past decades has underlined the striking similarities between the proliferative, migratory and invasive properties of placental cells and those of cancer cells. This review recapitulates the numerous key molecules, proto-oncogenes, growth factors, receptors, enzymes, hormones, peptides and tumour-associated antigens (TAAs) expressed by both trophoblastic and cancer cells in an attempt to evaluate the genes and proteins forming molecular circuits and regulating the similar behaviours of these cells. Among the autocrine and paracrine loops that might be involved in the strong proliferative capacity of trophoblastic and cancer cells, epidermal growth factor (EGF)/EGF receptor (EGFR), hepatocyte growth factor (HGF)/HGF receptor (HGFR) (Met) and vascular endothelial growth factor (VEGF)/VEGF receptor (VEGFR) loops may play a predominant role. Similar mechanisms of migration and invasion displayed by trophoblastic and malignant cells comprise alterations in the adhesion molecule phenotype, including the increased expression of alpha1beta1 and alphavbeta3 integrin receptors, whereas another critical molecular event is the down-regulation of the cell adhesion molecule E-cadherin. Among proteases that may play an active role in the invasive capacities of these cells, accumulating evidence suggests that matrix metalloproteinase-9 (MMP-9) expression/activation is a prerequisite. Finally, an overview of molecular circuitries shared by trophoblast and cancer cells reveals that the activation of the phosphatidylinositol 3'-kinase (PI3K)/AKT axis has recently emerged as a central feature of signalling pathways used by these cells to achieve their proliferative, migratory and invasive processes.
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              ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text.

              ABNER (A Biomedical Named Entity Recognizer) is an open source software tool for molecular biology text mining. At its core is a machine learning system using conditional random fields with a variety of orthographic and contextual features. The latest version is 1.5, which has an intuitive graphical interface and includes two modules for tagging entities (e.g. protein and cell line) trained on standard corpora, for which performance is roughly state of the art. It also includes a Java application programming interface allowing users to incorporate ABNER into their own systems and train models on new corpora.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                19 January 2015
                2015
                : 5
                : 7830
                Affiliations
                [1 ]Cancer Metastasis Alert and Prevention Center, College of Chemistry, Fuzhou University , Fuzhou 350002, China
                [2 ]Department of Oncology, Fuzhou General Hospital , Fuzhou 350025, China
                [3 ]Rutgers, The State University of New Jersey , New Jersey 08854, USA
                Author notes
                Article
                srep07830
                10.1038/srep07830
                4297966
                25597938
                4ac5fef1-795b-4c90-9ef7-c9d6fd754d79
                Copyright © 2015, Macmillan Publishers Limited. All rights reserved

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/

                History
                : 05 June 2014
                : 09 December 2014
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