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      Integrative genomic analyses reveal clinically relevant long non-coding RNA in human cancer

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          Despite growing appreciations of the importance of long non-coding RNA (lncRNA) in normal physiology and disease, our knowledge of cancer-related lncRNA remains limited. By repurposing microarray probes, we constructed the expression profile of 10,207 lncRNA genes in approximately 1,300 tumors over four different cancer types. Through integrative analysis of the lncRNA expression profiles with clinical outcome and somatic copy number alteration (SCNA), we identified lncRNA that are associated with cancer subtypes and clinical prognosis, and predicted those that are potential drivers of cancer progression. We validated our predictions by experimentally confirming prostate cancer cell growth dependence on two novel lncRNA. Our analysis provided a resource of clinically relevant lncRNA for development of lncRNA biomarkers and identification of lncRNA therapeutic targets. It also demonstrated the power of integrating publically available genomic datasets and clinical information for discovering disease associated lncRNA.

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          Most cited references 58

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          Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms

          High-throughput mRNA sequencing (RNA-Seq) holds the promise of simultaneous transcript discovery and abundance estimation 1-3 . We introduce an algorithm for transcript assembly coupled with a statistical model for RNA-Seq experiments that produces estimates of abundances. Our algorithms are implemented in an open source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed more than 430 million paired 75bp RNA-Seq reads from a mouse myoblast cell line representing a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Analysis of transcript expression over the time series revealed complete switches in the dominant transcription start site (TSS) or splice-isoform in 330 genes, along with more subtle shifts in a further 1,304 genes. These dynamics suggest substantial regulatory flexibility and complexity in this well-studied model of muscle development.
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            RNA-Seq: a revolutionary tool for transcriptomics.

            RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.
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              Integrated Genomic Analyses of Ovarian Carcinoma

              Summary The Cancer Genome Atlas (TCGA) project has analyzed mRNA expression, miRNA expression, promoter methylation, and DNA copy number in 489 high-grade serous ovarian adenocarcinomas (HGS-OvCa) and the DNA sequences of exons from coding genes in 316 of these tumors. These results show that HGS-OvCa is characterized by TP53 mutations in almost all tumors (96%); low prevalence but statistically recurrent somatic mutations in 9 additional genes including NF1, BRCA1, BRCA2, RB1, and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes. Analyses delineated four ovarian cancer transcriptional subtypes, three miRNA subtypes, four promoter methylation subtypes, a transcriptional signature associated with survival duration and shed new light on the impact on survival of tumors with BRCA1/2 and CCNE1 aberrations. Pathway analyses suggested that homologous recombination is defective in about half of tumors, and that Notch and FOXM1 signaling are involved in serous ovarian cancer pathophysiology.

                Author and article information

                Nat Struct Mol Biol
                Nat. Struct. Mol. Biol.
                Nature structural & molecular biology
                14 May 2013
                02 June 2013
                July 2013
                01 January 2014
                : 20
                : 7
                : 908-913
                [1 ]Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai, China
                [2 ]Center for Functional Cancer Epigeneitcs, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
                [3 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
                [4 ]Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
                [5 ]Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, USA
                [6 ]Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
                [7 ]College of Biological Sciences, China Agriculture University, Beijing, China
                Author notes
                [* ]To whom correspondence should be addressed: X. Shirley Liu ( xsliu@ ) or Yiwen Chen ( ywchen@ ) or Myles Brown ( myles_brown@ )

                These authors contributed equally


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                Funded by: National Institute of General Medical Sciences : NIGMS
                Award ID: R01 GM099409 || GM

                Molecular biology


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