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      Long non-coding RNAs as prognostic markers in human breast cancer

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          Abstract

          Long non-coding RNAs (lncRNAs) have been recently shown to play an important role in gene regulation and normal cellular functions, and disease processes. However, despite the overwhelming number of lncRNAs identified to date, little is known about their role in cancer for vast majority of them. The present study aims to determine whether lncRNAs can serve as prognostic markers in human breast cancer. We interrogated the breast invasive carcinoma dataset of the Cancer Genome Atlas (TCGA) at the cBioPortal consisting of ~ 1,000 cases. Among 2,730 lncRNAs analyzed, 577 lncRNAs had alterations ranging from 1% to 32% frequency, which include mutations, alterations of copy number and RNA expression. We found that deregulation of 11 lncRNAs, primarily due to copy number alteration, is associated with poor overall survival. At RNA expression level, upregulation of 4 lncRNAs (LINC00657, LINC00346, LINC00654 and HCG11) was associated with poor overall survival. A third signature consists of 9 lncRNAs (LINC00705, LINC00310, LINC00704, LINC00574, FAM74A3, UMODL1-AS1, ARRDC1-AS1, HAR1A, and LINC00323) and their upregulation can predict recurrence. Finally, we selected LINC00657 to determine their role in breast cancer, and found that LINC00657 knockout significantly suppresses tumor cell growth and proliferation, suggesting that it plays an oncogenic role. Together, these results highlight the clinical significance of lncRNAs, and thus, these lncRNAs may serve as prognostic markers for breast cancer.

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          Most cited references21

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          Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

          The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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            Ki67 Index, HER2 Status, and Prognosis of Patients With Luminal B Breast Cancer

            Background Gene expression profiling of breast cancer has identified two biologically distinct estrogen receptor (ER)-positive subtypes of breast cancer: luminal A and luminal B. Luminal B tumors have higher proliferation and poorer prognosis than luminal A tumors. In this study, we developed a clinically practical immunohistochemistry assay to distinguish luminal B from luminal A tumors and investigated its ability to separate tumors according to breast cancer recurrence-free and disease-specific survival. Methods Tumors from a cohort of 357 patients with invasive breast carcinomas were subtyped by gene expression profile. Hormone receptor status, HER2 status, and the Ki67 index (percentage of Ki67-positive cancer nuclei) were determined immunohistochemically. Receiver operating characteristic curves were used to determine the Ki67 cut point to distinguish luminal B from luminal A tumors. The prognostic value of the immunohistochemical assignment for breast cancer recurrence-free and disease-specific survival was investigated with an independent tissue microarray series of 4046 breast cancers by use of Kaplan–Meier curves and multivariable Cox regression. Results Gene expression profiling classified 101 (28%) of the 357 tumors as luminal A and 69 (19%) as luminal B. The best Ki67 index cut point to distinguish luminal B from luminal A tumors was 13.25%. In an independent cohort of 4046 patients with breast cancer, 2847 had hormone receptor–positive tumors. When HER2 immunohistochemistry and the Ki67 index were used to subtype these 2847 tumors, we classified 1530 (59%, 95% confidence interval [CI] = 57% to 61%) as luminal A, 846 (33%, 95% CI = 31% to 34%) as luminal B, and 222 (9%, 95% CI = 7% to 10%) as luminal–HER2 positive. Luminal B and luminal–HER2-positive breast cancers were statistically significantly associated with poor breast cancer recurrence-free and disease-specific survival in all adjuvant systemic treatment categories. Of particular relevance are women who received tamoxifen as their sole adjuvant systemic therapy, among whom the 10-year breast cancer–specific survival was 79% (95% CI = 76% to 83%) for luminal A, 64% (95% CI = 59% to 70%) for luminal B, and 57% (95% CI = 47% to 69%) for luminal–HER2 subtypes. Conclusion Expression of ER, progesterone receptor, and HER2 proteins and the Ki67 index appear to distinguish luminal A from luminal B breast cancer subtypes.
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              Genome-wide analysis of estrogen receptor binding sites.

              The estrogen receptor is the master transcriptional regulator of breast cancer phenotype and the archetype of a molecular therapeutic target. We mapped all estrogen receptor and RNA polymerase II binding sites on a genome-wide scale, identifying the authentic cis binding sites and target genes, in breast cancer cells. Combining this unique resource with gene expression data demonstrates distinct temporal mechanisms of estrogen-mediated gene regulation, particularly in the case of estrogen-suppressed genes. Furthermore, this resource has allowed the identification of cis-regulatory sites in previously unexplored regions of the genome and the cooperating transcription factors underlying estrogen signaling in breast cancer.
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                12 April 2016
                1 March 2016
                : 7
                : 15
                : 20584-20596
                Affiliations
                1 Cancer Institute, University of Mississippi Medical Center, Jackson, MS, USA
                2 Department of Oncology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
                3 Department of Clinical Laboratory, Qilu Hospital, Shandong University, Jinan, Shangdong Province, China
                4 Department of Biochemistry, University of Mississippi Medical Center, Jackson, MS, USA
                5 College of Life Science, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China
                6 Department of Urology, Affiliated Hospital of Jiangsu University, Jiangsu, Zhenjiang, China
                7 School of Computing, University of Southern Mississippi, Hattiesburg, MS, USA
                8 Center of Biostatistics and Bioinformatics, Department of Preventive Medicine, University of Mississippi Medical Center, Jackson, MS, USA
                9 Department of Pharmacology/Toxicology, University of Mississippi Medical Center, Jackson, MS, USA
                Author notes
                Correspondence to: Xu Zhang, xzhang2@ 123456umc.edu
                Yin-Yuan Mo, ymo@ 123456umc.edu
                Article
                7828
                10.18632/oncotarget.7828
                4991477
                26942882
                f2cdcdcd-4e47-4ef8-8e86-4ef15acc6dc8
                Copyright: © 2016 Liu et al.

                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
                : 10 January 2016
                : 18 February 2016
                Categories
                Research Paper

                Oncology & Radiotherapy
                lncrna,prognosis,breast cancer,biomarkers
                Oncology & Radiotherapy
                lncrna, prognosis, breast cancer, biomarkers

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