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Integrative genomic analyses reveal clinically relevant long noncoding RNAs in human cancer

Nature Structural & Molecular Biology volume 20, pages 908913 (2013) | Download Citation

Abstract

Despite growing appreciation of the importance of long noncoding RNAs (lncRNAs) in normal physiology and disease, our knowledge of cancer-related lncRNAs remains limited. By repurposing microarray probes, we constructed expression profiles 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 alterations, we identified lncRNAs 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 newly identified lncRNAs. Our analysis provides a resource of clinically relevant lncRNAs for the development of lncRNA biomarkers and the identification of lncRNA therapeutic targets. It also demonstrates the power of integrating publically available genomic data sets and clinical information for discovering disease-associated lncRNAs.

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Acknowledgements

This work was partially funded by the National Natural Science Foundation of China (31028011) (X.S.L.), the National Basic Research (973) Program of China (2010CB944904; Y.Z.) and US National Institutes of Health grant GM099409 (X.S.L.).

Author information

Author notes

    • Zhou Du
    • , Teng Fei
    •  & Yiwen Chen

    These authors contributed equally to this work.

Affiliations

  1. Department of Bioinformatics, School of Life Sciences and Technology, Tongji University, Shanghai, China.

    • Zhou Du
    •  & Yong Zhang
  2. Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.

    • Teng Fei
    • , Myles Brown
    •  & X Shirley Liu
  3. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.

    • Teng Fei
    •  & Myles Brown
  4. Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • Teng Fei
    •  & Myles Brown
  5. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, USA.

    • Teng Fei
    • , Yiwen Chen
    •  & X Shirley Liu
  6. Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Roel G W Verhaak
  7. College of Biological Sciences, China Agriculture University, Beijing, China.

    • Zhen Su

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Contributions

Y.C. conceived the project. Z.D. and Y.C. designed the algorithms and performed computational analyses. R.G.W.V. contributed to the subtype analyses of ovarian cancer. T.F. performed all the experimental validation. Z.D., T.F., Z.S., Y.Z., M.B., Y.C. and X.S.L. participated in the discussions and contributed to the analysis of the intermediate results throughout the project. Y.C., M.B. and X.S.L. supervised the project. Z.D., T.F., Y.C. and X.S.L. wrote the manuscript with the help from other coauthors.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Myles Brown or Yiwen Chen or X Shirley Liu.

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DOI

https://doi.org/10.1038/nsmb.2591

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