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      How MicroRNA and Transcription Factor Co-regulatory Networks Affect Osteosarcoma Cell Proliferation

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          Abstract

          Osteosarcomas (OS) are complex bone tumors with various genomic alterations. These alterations affect the expression and function of several genes due to drastic changes in the underlying gene regulatory network. However, we know little about critical gene regulators and their functional consequences on the pathogenesis of OS. Therefore, we aimed to determine microRNA and transcription factor (TF) co-regulatory networks in OS cell proliferation. Cell proliferation is an essential part in the pathogenesis of OS and deeper understanding of its regulation might help to identify potential therapeutic targets. Based on expression data of OS cell lines divided according to their proliferative activity, we obtained 12 proliferation-related microRNAs and corresponding target genes. Therewith, microRNA and TF co-regulatory networks were generated and analyzed regarding their structure and functional influence. We identified key co-regulators comprising the microRNAs miR-9-5p, miR-138, and miR-214 and the TFs SP1 and MYC in the derived networks. These regulators are implicated in NFKB- and RB1-signaling and focal adhesion processes based on their common or interacting target genes (e.g., CDK6, CTNNB1, E2F4, HES1, ITGA6, NFKB1, NOTCH1, and SIN3A). Thus, we proposed a model of OS cell proliferation which is primarily co-regulated through the interactions of the mentioned microRNA and TF combinations. This study illustrates the benefit of systems biological approaches in the analysis of complex diseases. We integrated experimental data with publicly available information to unravel the coordinated (post)-transcriptional control of microRNAs and TFs to identify potential therapeutic targets in OS. The resulting microRNA and TF co-regulatory networks are publicly available for further exploration to generate or evaluate own hypotheses of the pathogenesis of OS ( http://www.complex-systems.uni-muenster.de/co_networks.html).

          Author Summary

          Osteosarcomas (OS) are bone tumors most frequently affecting children and young adolescents. We do not know much about its molecular pathogenesis hampering personalized therapies to more effectively cure patients. To this day, almost all patients receive adjuvant chemotherapies independent of its necessity. Hence, we need to gain a comprehensive understanding of the molecular pathogenesis of OS to uncover molecular components that can be used as therapeutic targets. MicroRNAs and transcription factors (TFs) are master regulators of the cellular system. They control the amount of genes expressed in a cell at a specific time point that ultimately results in a distinct cellular phenotype. Here, we investigated microRNA and TF co-regulatory networks in OS cell growth as one hallmark in cancer. We uncovered key microRNA and TF regulators that cooperatively control growth-related pathways in OS and proposed potential therapeutic targets. This study illustrates the benefit of analyzing complex diseases from a network perspective because no molecular component functions isolated from the underlying cellular system.

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          Most cited references41

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          Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

          Y. H. Yang (2002)
          There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.
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            Cytoscape Web: an interactive web-based network browser

            Summary: Cytoscape Web is a web-based network visualization tool–modeled after Cytoscape–which is open source, interactive, customizable and easily integrated into web sites. Multiple file exchange formats can be used to load data into Cytoscape Web, including GraphML, XGMML and SIF. Availability and Implementation: Cytoscape Web is implemented in Flex/ActionScript with a JavaScript API and is freely available at http://cytoscapeweb.cytoscape.org/ Contact: gary.bader@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online.
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              Ensembl 2011

              The Ensembl project (http://www.ensembl.org) seeks to enable genomic science by providing high quality, integrated annotation on chordate and selected eukaryotic genomes within a consistent and accessible infrastructure. All supported species include comprehensive, evidence-based gene annotations and a selected set of genomes includes additional data focused on variation, comparative, evolutionary, functional and regulatory annotation. The most advanced resources are provided for key species including human, mouse, rat and zebrafish reflecting the popularity and importance of these species in biomedical research. As of Ensembl release 59 (August 2010), 56 species are supported of which 5 have been added in the past year. Since our previous report, we have substantially improved the presentation and integration of both data of disease relevance and the regulatory state of different cell types.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                August 2013
                August 2013
                29 August 2013
                : 9
                : 8
                : e1003210
                Affiliations
                [1 ]Institute of Bioinformatics, University of Münster, Münster, Germany
                [2 ]Clinical Cooperation Group Osteosarcoma, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
                [3 ]Children's Cancer Research Center and Department of Pediatrics, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
                [4 ]Bone Tumor Reference Center at the Institute of Pathology, University Hospital Basel, Basel, Switzerland
                Ottawa University, Canada
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: KP JS EK. Performed the experiments: KP JS. Analyzed the data: KP. Contributed reagents/materials/analysis tools: JS MN DM DB EK. Wrote the paper: KP JS EK.

                Article
                PCOMPBIOL-D-13-00425
                10.1371/journal.pcbi.1003210
                3757060
                24009496
                f1eba419-87cd-4a8f-a1de-7df489b35a72
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 12 March 2013
                : 19 July 2013
                Page count
                Pages: 13
                Funding
                KP, JS, MN, DM, and EK were funded by the Translational Sarcoma Research Network supported by the BMBF (FKZ 01GM0870, 01GM0869) and KP, EK by European TRANSCAN I consortium - PROspective VAlidation of Biomarkers in Ewing Sarcoma for personalised translational medicine by BMBF (FZK 01KT1310). DB was supported by the Foundation for the Preservation of the Basel Bone Tumor Center. We acknowledge support by Deutsche Forschungsgemeinschaft and Open Access Publication Fund of University of Muenster. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article

                Quantitative & Systems biology
                Quantitative & Systems biology

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