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      Identification of mRNA isoform switching in breast cancer

      research-article
      , , ,
      BMC Genomics
      BioMed Central

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          Abstract

          Background

          Alternative splicing provides a major mechanism to generate protein diversity. Increasing evidence suggests a link of dysregulation of splicing associated with cancer. While previous genomic-based studies demonstrated the expression of a handful of tumor-specific isoforms, genome-wide alterations in the balance between isoforms and cancer subtypes is understudied.

          Result

          We systematically analyzed the isoform-level expression patterns and isoform switching events of 819 breast tumor and normal samples assayed by mRNA-seq from TCGA project. On average, 2.2 isoforms per gene were detected and 67.5 % of detected genes (i.e. expressed) showed 1–2 isoforms only. While the majority of isoforms for a given gene were positively correlated with each other and the overall gene level, 470 pairs of isoforms displayed an inverse correlation suggesting a switching event. Most of the isoform switching events were associated with molecular subtypes, including a Basal-like-associated switching in CTNND1. 88 genes showed switching independent of subtypes, among which the isoform pattern of PRICKLE1 was associated with a large genomic signature of biological significance.

          Conclusion

          Our results reveal that the majority of genes do not undergo complex mRNA splicing within breast cancers, and that there is a general concordance in isoform and gene expression levels in breast tumors. We identified hundreds of isoform switching events across breast tumors, most of which were associated with differences in tumor subtypes. As exemplified by the detailed analysis of CTNND1 and PRICKLE1, these isoform switching events potentially provide new insights into the post-transcriptional regulatory mechanisms of tumor subtypes and cancer biology.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12864-016-2521-9) contains supplementary material, which is available to authorized users.

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          Most cited references14

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          In silico prediction of protein-protein interactions in human macrophages

          Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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            The mdm-2 oncogene product forms a complex with the p53 protein and inhibits p53-mediated transactivation.

            A cellular phosphoprotein with an apparent molecular mass of 90 kd (p90) that forms a complex with both mutant and wild-type p53 protein has been characterized, purified, and identified. The protein was identified as a product of the murine double minute 2 gene (mdm-2). The mdm-2 gene enhances the tumorigenic potential of cells when it is overexpressed and encodes a putative transcription factor. To determine if mdm-2 could modulate p53 transactivation, a p53-responsive element from the muscle creatine kinase gene was employed. A wild-type p53-expressing plasmid enhanced the expression of the p53-responsive element when cotransfected into cells that contain no endogenous p53. When a cosmid expressing mdm-2 was transfected with this p53-expressing plasmid, the transactivation of the p53-responsive element was inhibited. Thus, a product of the mdm-2 oncogene forms a tight complex with the p53 protein, and the mdm-2 oncogene can inhibit p53-mediated transactivation.
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              Alternative pre-mRNA splicing regulation in cancer: pathways and programs unhinged.

              Alternative splicing of mRNA precursors is a nearly ubiquitous and extremely flexible point of gene control in humans. It provides cells with the opportunity to create protein isoforms of differing, even opposing, functions from a single gene. Cancer cells often take advantage of this flexibility to produce proteins that promote growth and survival. Many of the isoforms produced in this manner are developmentally regulated and are preferentially re-expressed in tumors. Emerging insights into this process indicate that pathways that are frequently deregulated in cancer often play important roles in promoting aberrant splicing, which in turn contributes to all aspects of tumor biology.
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                Author and article information

                Contributors
                wzhao3@mdanderson.org
                hoadley@med.unc.edu
                parkerjs@email.unc.edu
                919-843-5740 , cperou@med.unc.edu
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                3 March 2016
                3 March 2016
                2016
                : 17
                : 181
                Affiliations
                [ ]Department of Systems Biology, University of Texas MD Anderson Cancer Center, 77054 Houston, TX USA
                [ ]Department of Genetics, University of North Carolina, 27599 Chapel Hill, NC USA
                [ ]Curriculum in Bioinformatics and Computational Biology, University of North Carolina, 27599 Chapel Hill, NC USA
                [ ]Lineberger Comprehensive Cancer Center, University of North Carolina, 125 Mason Farm Road, 27599 Chapel Hill, NC USA
                Article
                2521
                10.1186/s12864-016-2521-9
                4778320
                26939613
                95a387c1-65fe-4b28-9837-da283e11f1db
                © Zhao et al. 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 18 November 2015
                : 24 February 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: U24-CA143848-06
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: P50-CA58223-09A1
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Genetics
                Genetics

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