Classification and prediction of survival in hepatocellular carcinoma by gene expression profiling

Authors

  • Ju-Seog Lee,

    1. Laboratory of Experimental Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
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  • In-Sun Chu,

    1. Laboratory of Experimental Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
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  • Jeonghoon Heo,

    1. Laboratory of Experimental Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
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  • Diego F. Calvisi,

    1. Laboratory of Experimental Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
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  • Zongtang Sun,

    1. National Laboratory of Molecular Oncology, Cancer Institute, Chinese Academy of Medical Sciences, Beijing, China
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  • Tania Roskams,

    1. Departments of Morphology and Molecular Pathology and Hepatology, University of Leuven, Leuven, Belgium
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  • Anne Durnez,

    1. Departments of Morphology and Molecular Pathology and Hepatology, University of Leuven, Leuven, Belgium
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  • Anthony J. Demetris,

    1. Thomas E. Starzl Transplant Institute, University of Pittsburgh Medical Center, Pittsburgh, PA
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  • Snorri S. Thorgeirsson

    Corresponding author
    1. Laboratory of Experimental Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
    • Laboratory of Experimental Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892-4255
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    • fax: (301) 496-0734


Abstract

We analyzed global gene expression patterns of 91 human hepatocellular carcinomas (HCCs) to define the molecular characteristics of the tumors and to test the prognostic value of the expression profiles. Unsupervised classification methods revealed two distinctive subclasses of HCC that are highly associated with patient survival. This association was validated via 5 independent supervised learning methods. We also identified the genes most strongly associated with survival by using the Cox proportional hazards survival analysis. This approach identified a limited number of genes that accurately predicted the length of survival and provides new molecular insight into the pathogenesis of HCC. Tumors from the low survival subclass have strong cell proliferation and antiapoptosis gene expression signatures. In addition, the low survival subclass displayed higher expression of genes involved in ubiquitination and histone modification, suggesting an etiological involvement of these processes in accelerating the progression of HCC. In conclusion, the biological differences identified in the HCC subclasses should provide an attractive source for the development of therapeutic targets (e.g., HIF1a) for selective treatment of HCC patients. Supplementary material for this article can be found on the HEPATOLOGY Web site (http://interscience.wiley.com/jpages/0270-9139/suppmat/index.html) (HEPATOLOGY 2004;40:667–676.)

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