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      LncRNA MIR100HG promotes cell proliferation in triple-negative breast cancer through triplex formation with p27 loci

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          Abstract

          Triple-negative breast cancer (TNBC) exhibits poor prognosis, with high metastasis and low survival. Long non-coding RNAs (lncRNAs) play critical roles in tumor progression. Here, we identified lncRNA MIR100HG as a pro-oncogene for TNBC progression. Knockdown of MIR100HG decreased cell proliferation and induced cell arrest in the G1 phase, whereas overexpression of MIR100HG significantly increased cell proliferation. Furthermore, MIR100HG regulated the p27 gene to control the cell cycle, and subsequently impacted the progression of TNBC. In analyzing its underlying mechanism, bioinformatics prediction and experimental data demonstrated that MIR100HG participated in the formation of RNA–DNA triplex structures. MIR100HG in The Cancer Genome Atlas (TCGA) and breast cancer cell lines showed higher expression in TNBC than in other tumor types with poor prognosis. In conclusion, our data indicated a novel working pattern of lncRNA in TNBC progression, which may be a potential therapeutic target in such cancers.

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          Most cited references 38

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          featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

           ,  ,   (2013)
          Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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            Kcnq1ot1 antisense noncoding RNA mediates lineage-specific transcriptional silencing through chromatin-level regulation.

            Recent investigations have implicated long antisense noncoding RNAs in the epigenetic regulation of chromosomal domains. Here we show that Kcnq1ot1 is an RNA polymerase II-encoded, 91 kb-long, moderately stable nuclear transcript and that its stability is important for bidirectional silencing of genes in the Kcnq1 domain. Kcnq1ot1 interacts with chromatin and with the H3K9- and H3K27-specific histone methyltransferases G9a and the PRC2 complex in a lineage-specific manner. This interaction correlates with the presence of extended regions of chromatin enriched with H3K9me3 and H3K27me3 in the Kcnq1 domain in placenta, whereas fetal liver lacks both chromatin interactions and heterochromatin structures. In addition, the Kcnq1 domain is more often found in contact with the nucleolar compartment in placenta than in liver. Taken together, our data describe a mechanism whereby Kcnq1ot1 establishes lineage-specific transcriptional silencing patterns through recruitment of chromatin remodeling complexes and maintenance of these patterns through subsequent cell divisions occurs via targeting the associated regions to the perinucleolar compartment.
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              Functionality or transcriptional noise? Evidence for selection within long noncoding RNAs.

              Long transcripts that do not encode protein have only rarely been the subject of experimental scrutiny. Presumably, this is owing to the current lack of evidence of their functionality, thereby leaving an impression that, instead, they represent "transcriptional noise." Here, we describe an analysis of 3122 long and full-length, noncoding RNAs ("macroRNAs") from the mouse, and compare their sequences and their promoters with orthologous sequence from human and from rat. We considered three independent signatures of purifying selection related to substitutions, sequence insertions and deletions, and splicing. We find that the evolution of the set of noncoding RNAs is not consistent with neutralist explanations. Rather, our results indicate that purifying selection has acted on the macroRNAs' promoters, primary sequence, and consensus splice site motifs. Promoters have experienced the greatest elimination of nucleotide substitutions, insertions, and deletions. The proportion of conserved sequence (4.1%-5.5%) in these macroRNAs is comparable to the density of exons within protein-coding transcripts (5.2%). These macroRNAs, taken together, thus possess the imprint of purifying selection, thereby indicating their functionality. Our findings should now provide an incentive for the experimental investigation of these macroRNAs' functions.
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                Author and article information

                Contributors
                njyvip@sina.com
                wcl_xj@163.com
                jiaobaowei@mail.kiz.ac.cn
                Journal
                Cell Death Dis
                Cell Death Dis
                Cell Death & Disease
                Nature Publishing Group UK (London )
                2041-4889
                24 July 2018
                24 July 2018
                August 2018
                : 9
                : 8
                Affiliations
                [1 ]ISNI 0000 0004 0610 111X, GRID grid.411527.4, Key Laboratory of Southwest China Wildlife Resources Conservation, , China West Normal University, Ministry of Education, ; Nanchong, 637009 China
                [2 ]ISNI 0000000119573309, GRID grid.9227.e, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, , Chinese Academy of Sciences, ; Kunming, 650223 China
                [3 ]Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650223 China
                [4 ]Kunming Angel Women’s and Children’s Hospital, Kunming, 650032 China
                [5 ]ISNI 0000 0000 8877 7471, GRID grid.284723.8, Bioinformatics Section, School of Basic Medical Sciences, , Southern Medical University, ; Guangzhou, 510515 China
                [6 ]GRID grid.452826.f, Department of Breast Cancer, Yunnan Cancer Center, , Third Affiliated Hospital of Kunming Medical University, ; Kunming, 650118 China
                [7 ]ISNI 0000000119573309, GRID grid.9227.e, Center for Excellence in Animal Evolution and Genetics, , Chinese Academy of Sciences, ; Kunming, 650223 China
                Article
                869
                10.1038/s41419-018-0869-2
                6057987
                30042378
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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                © The Author(s) 2018

                Cell biology

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