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      Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis

      research-article
      , MD 1 , , MD 2
      Technology in Cancer Research & Treatment
      SAGE Publications
      lncRNA, prostate cancer, biomarker, WGCNA

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          Abstract

          Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa.

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          Most cited references29

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          WGCNA: an R package for weighted correlation network analysis

          Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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            Molecular mechanisms of long noncoding RNAs.

            Long noncoding RNAs (lncRNAs) are an important class of pervasive genes involved in a variety of biological functions. Here we discuss the emerging archetypes of molecular functions that lncRNAs execute-as signals, decoys, guides, and scaffolds. For each archetype, examples from several disparate biological contexts illustrate the commonality of the molecular mechanisms, and these mechanistic views provide useful explanations and predictions of biological outcomes. These archetypes of lncRNA function may be a useful framework to consider how lncRNAs acquire properties as biological signal transducers and hint at their possible origins in evolution. As new lncRNAs are being discovered at a rapid pace, the molecular mechanisms of lncRNAs are likely to be enriched and diversified. Copyright © 2011 Elsevier Inc. All rights reserved.
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              Emerging functional and mechanistic paradigms of mammalian long non-coding RNAs

              The recent discovery that the human and other mammalian genomes produce thousands of long non-coding RNAs (lncRNAs) raises many fascinating questions. These mRNA-like molecules, which lack significant protein-coding capacity, have been implicated in a wide range of biological functions through diverse and as yet poorly understood molecular mechanisms. Despite some recent insights into how lncRNAs function in such diverse cellular processes as regulation of gene expression and assembly of cellular structures, by and large, the key questions regarding lncRNA mechanisms remain to be answered. In this review, we discuss recent advances in understanding the biology of lncRNAs and propose avenues of investigation that may lead to fundamental new insights into their functions and mechanisms of action. Finally, as numerous lncRNAs are dysregulated in human diseases and disorders, we also discuss potential roles for these molecules in human health.
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                Author and article information

                Journal
                Technol Cancer Res Treat
                Technol Cancer Res Treat
                TCT
                sptct
                Technology in Cancer Research & Treatment
                SAGE Publications (Sage CA: Los Angeles, CA )
                1533-0346
                1533-0338
                21 October 2020
                2020
                : 19
                : 1533033820963578
                Affiliations
                [1 ]Department of Urology, The Second Affiliated Hospital of Ringgold 107652, universityShaanxi University of Traditional Chinese Medicine; , Xianyang, Shaanxi, China
                [2 ]Department of General Surgery, Ringgold 107652, Chinese PLA General Hospital; , Beijing, China
                Author notes
                [*]Bobin Ning, Department of General Surgery, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China. Email: 15140239706@ 123456163.com
                Author information
                https://orcid.org/0000-0001-7692-5833
                Article
                10.1177_1533033820963578
                10.1177/1533033820963578
                7785998
                33084528
                c2619d38-d533-4a32-adfe-7f7e972841c6
                © The Author(s) 2020

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 13 May 2020
                : 27 August 2020
                : 10 September 2020
                Categories
                Original Article
                Custom metadata
                January-December 2020
                ts3

                lncrna,prostate cancer,biomarker,wgcna
                lncrna, prostate cancer, biomarker, wgcna

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