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      Long noncoding RNA HOTAIR reprograms chromatin state to promote cancer metastasis

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          Abstract

          Large intervening noncoding RNAs (lincRNAs) are pervasively transcribed in the genome 1, 2, 3 yet their potential involvement in human disease is not well understood 4, 5. Recent studies of dosage compensation, imprinting, and homeotic gene expression suggest that individual lincRNAs can function as the interface between DNA and specific chromatin remodeling activities 6, 7, 8. Here we show that lincRNAs in the HOX loci become systematically dysregulated during breast cancer progression. The lincRNA termed HOTAIR is increased in expression in primary breast tumors and metastases, and HOTAIR expression level in primary tumors is a powerful predictor of eventual metastasis and death. Enforced expression of HOTAIR in epithelial cancer cells induced genome-wide re-targeting of Polycomb Repressive Complex 2 (PRC2) to an occupancy pattern more resembling embryonic fibroblasts, leading to altered histone H3 lysine 27 methylation, gene expression, and increased cancer invasiveness and metastasis in a manner dependent on PRC2. Conversely, loss of HOTAIR can inhibit cancer invasiveness, particularly in cells that possess excessive PRC2 activity. These findings suggest that lincRNAs play active roles in modulating the cancer epigenome and may be important targets for cancer diagnosis and therapy.

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          Most cited references 30

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          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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            Cluster analysis and display of genome-wide expression patterns.

            A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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              A gene-expression signature as a predictor of survival in breast cancer.

              A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy. Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses. Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (+/-SE) overall 10-year survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6+/-4.5 percent in the group with a poor-prognosis signature and 85.2+/-4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome. The gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria. Copyright 2002 Massachusetts Medical Society
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                0028-0836
                1476-4687
                9 March 2010
                15 April 2010
                8 March 2011
                : 464
                : 7291
                : 1071-1076
                Affiliations
                [1 ] Howard Hughes Medical Institute and Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
                [2 ] Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
                [3 ] Stanford Comprehensive Cancer Center and Transgenic Mouse Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA
                [4 ] Dept. of Pathology, Academic Medical Center, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands
                [5 ] Dept. of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
                [6 ] The Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142, USA
                [7 ] Applied Biosystems, Foster City, CA 94404, USA
                [8 ] Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
                Author notes
                Correspondence and request for materials should beaddressed to H.Y.C. ( howchang@ 123456stanford.edu )
                nihpa183311
                10.1038/nature08975
                3049919
                20393566

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                Funding
                Funded by: National Human Genome Research Institute : NHGRI
                Funded by: National Cancer Institute : NCI
                Award ID: R01 HG004361-03 ||HG
                Funded by: National Human Genome Research Institute : NHGRI
                Funded by: National Cancer Institute : NCI
                Award ID: R01 CA118750-03 ||CA
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