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      Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

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          Summary

          Long noncoding RNAs (lncRNAs) are commonly dysregulated in tumors, but only a handful are known to play pathophysiological roles in cancer. We inferred lncRNAs that dysregulate cancer pathways, oncogenes, and tumor suppressors (cancer genes) by modeling their effects on the activity of transcription factors, RNA-binding proteins, and microRNAs in 5,185 TCGA tumors and 1,019 ENCODE assays. Our predictions included hundreds of candidate onco- and tumor-suppressor lncRNAs (cancer lncRNAs) whose somatic alterations account for the dysregulation of dozens of cancer genes and pathways in each of 14 tumor contexts. To demonstrate proof of concept, we showed that perturbations targeting OIP5-AS1 (an inferred tumor suppressor) and TUG1 and WT1-AS (inferred onco-lncRNAs) dysregulated cancer genes and altered proliferation of breast and gynecologic cancer cells. Our analysis indicates that, although most lncRNAs are dysregulated in a tumor-specific manner, some, including OIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergistically dysregulate cancer pathways in multiple tumor contexts.

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          Highlights

          • Hundreds of lncRNAs target cancer genes and pathways in each tumor context

          • lncRNA copy numbers are predictive of target cancer gene dysregulation

          • Most lncRNAs are predicted to be transcriptional or post-transcriptional specialists

          • lncRNAs are predicted to synergistically regulate proliferation pathways in cancer

          Abstract

          Chiu et al. present a pan-cancer analysis of lncRNA regulatory interactions. They suggest that the dysregulation of hundreds of lncRNAs target and alter the expression of cancer genes and pathways in each tumor context. This implies that hundreds of lncRNAs can alter tumor phenotypes in each tumor context.

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          NIH Image to ImageJ: 25 years of image analysis.

          For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            LIBSVM: A library for support vector machines

            LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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              miRecords: an integrated resource for microRNA–target interactions

              MicroRNAs (miRNAs) are an important class of small noncoding RNAs capable of regulating other genes’ expression. Much progress has been made in computational target prediction of miRNAs in recent years. More than 10 miRNA target prediction programs have been established, yet, the prediction of animal miRNA targets remains a challenging task. We have developed miRecords, an integrated resource for animal miRNA–target interactions. The Validated Targets component of this resource hosts a large, high-quality manually curated database of experimentally validated miRNA–target interactions with systematic documentation of experimental support for each interaction. The current release of this database includes 1135 records of validated miRNA–target interactions between 301 miRNAs and 902 target genes in seven animal species. The Predicted Targets component of miRecords stores predicted miRNA targets produced by 11 established miRNA target prediction programs. miRecords is expected to serve as a useful resource not only for experimental miRNA researchers, but also for informatics scientists developing the next-generation miRNA target prediction programs. The miRecords is available at http://miRecords.umn.edu/miRecords.
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                Author and article information

                Contributors
                Journal
                Cell Rep
                Cell Rep
                Cell Reports
                Cell Press
                2211-1247
                05 April 2018
                03 April 2018
                05 April 2018
                : 23
                : 1
                : 297-312.e12
                Affiliations
                [1 ]Texas Children’s Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
                [2 ]Bioinformatics Center, Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan
                [3 ]Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA
                [4 ]Department of Biology and Biochemistry, University of Houston, Houston, TX 77030, USA
                [5 ]Department of Gynecologic Oncology and Reproductive Medicine, Division of Surgery, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
                Author notes
                []Corresponding author sumazin@ 123456bcm.edu
                [6]

                These authors contributed equally

                [7]

                Lead Contact

                Article
                S2211-1247(18)30425-X
                10.1016/j.celrep.2018.03.064
                5906131
                29617668
                029c4fea-63f6-4fb3-be15-662658e1596d
                © 2018 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 19 September 2017
                : 12 February 2018
                : 15 March 2018
                Categories
                Article

                Cell biology
                lncrna,regulation,modulation,cancer gene,pan-cancer,noncoding rna,microrna,rna-binding proteins,interactome

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