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      Identification of diagnostic long non-coding RNA biomarkers in patients with hepatocellular carcinoma

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          Abstract

          Liver cancer is a leading cause of cancer-associated mortality worldwide. Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer. The aim of the present study was to identify long non-coding RNA (lncRNAs) as diagnostic biomarkers for HCC. The lncRNA and mRNA expression profiles of a large group of patients with HCC were obtained from The Cancer Genome Atlas. The differentially expressed lncRNAs (DElncRNAs) and the differentially expressed mRNAs (DEmRNAs) were identified by bioinformatics analysis. Using feature selection procedure and a classification model, the optimal diagnostic lncRNA biomarkers for HCC were identified. Classification models, including random forests, decision tree and support vector machine (SVM), were established to distinguish between HCC and normal tissues. DEmRNAs co-expressed with the lncRNAs were considered as targets of DElncRNAs. Functional annotation of DEmRNAs co-expressed with these lncRNAs biomarkers was performed. Receiver operating characteristic curve analysis of lncRNAs biomarkers was conducted. A total of 3,177 lncRNAs and 15,183 mRNAs between HCC and normal tissues were obtained. RP11-486O12.2, RP11-863K10.7, LINC01093 and RP11-273G15.2 were identified as optimal diagnostic lncRNA biomarkers for HCC that were co-expressed with 273, 69, 76 and 1 DEmRNAs, respectively. The area under the curve values of the random forest model, decision tree model and SVM model were 0.992, 0.927 and 0.992, and the specificity and sensitivity of the three models were 100.0 and 95.6, 92.0 and 98.3 and 98.0 and 97.2%, respectively. ‘PPAR signaling pathway’ and ‘retinol metabolism’ were two significantly enriched target pathways of DElncRNAs. The present study identified four DElncRNAs, including RP11-486O12.2, RP11-863K10.7, LINC01093 and RP11-273G15.2, as potential diagnostic biomarkers of HCC. Functional annotation of target DEmRNAs provided novel evidence for examining the precise roles of lncRNA in HCC.

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          Most cited references33

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Gene Ontology: tool for the unification of biology

            Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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              The Gene Ontology Resource: 20 years and still GOing strong

              Abstract The Gene Ontology resource (GO; http://geneontology.org) provides structured, computable knowledge regarding the functions of genes and gene products. Founded in 1998, GO has become widely adopted in the life sciences, and its contents are under continual improvement, both in quantity and in quality. Here, we report the major developments of the GO resource during the past two years. Each monthly release of the GO resource is now packaged and given a unique identifier (DOI), enabling GO-based analyses on a specific release to be reproduced in the future. The molecular function ontology has been refactored to better represent the overall activities of gene products, with a focus on transcription regulator activities. Quality assurance efforts have been ramped up to address potentially out-of-date or inaccurate annotations. New evidence codes for high-throughput experiments now enable users to filter out annotations obtained from these sources. GO-CAM, a new framework for representing gene function that is more expressive than standard GO annotations, has been released, and users can now explore the growing repository of these models. We also provide the ‘GO ribbon’ widget for visualizing GO annotations to a gene; the widget can be easily embedded in any web page.
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                Author and article information

                Journal
                Mol Med Rep
                Mol Med Rep
                Molecular Medicine Reports
                D.A. Spandidos
                1791-2997
                1791-3004
                August 2019
                28 May 2019
                28 May 2019
                : 20
                : 2
                : 1121-1130
                Affiliations
                Department of Medical Imaging, Qianfoshan Hospital Affiliated to Shandong University, Jinan, Shandong 250014, P.R. China
                Author notes
                Correspondence to: Professor Hongjun Sun, Department of Medical Imaging, Qianfoshan Hospital Affiliated to Shandong University, 16766 Jingshi Road, Jinan, Shandong 250014, P.R. China, E-mail: hongjunsun_doctor1@ 123456163.com
                Article
                mmr-20-02-1121
                10.3892/mmr.2019.10307
                6625424
                31173205
                b3e6b45f-45ac-4c56-9b21-c90eb3b965bb
                Copyright: © Li et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

                History
                : 03 August 2018
                : 09 May 2019
                Categories
                Articles

                hepatocellular carcinoma,biomarker,long non-coding rnas,mrnas,the cancer genome atlas

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