Hepatobiliary Malignancies

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Sixty-five gene-based risk score classifier predicts overall survival in hepatocellular carcinoma†

  1. Soo Mi Kim1,2,‡,
  2. Sun-Hee Leem3,§,
  3. In-Sun Chu4,§,
  4. Yun-Yong Park1,
  5. Sang Cheol Kim4,
  6. Sang-Bae Kim1,
  7. Eun Sung Park5,
  8. Jae Yun Lim6,
  9. Jeonghoon Heo7,
  10. Yoon Jun Kim8,
  11. Dae-Ghon Kim9,
  12. Ahmed Kaseb10,
  13. Young Nyun Park11,
  14. Xin Wei Wang12,
  15. Snorri S. Thorgeirsson13,
  16. Ju-Seog Lee1,¶,*

Article first published online: 18 MAR 2012

DOI: 10.1002/hep.24813

Issue

Hepatology

Hepatology

Volume 55, Issue 5, pages 1443–1452, May 2012

Additional Information

How to Cite

Kim, S. M., Leem, S.-H., Chu, I.-S., Park, Y.-Y., Kim, S. C., Kim, S.-B., Park, E. S., Lim, J. Y., Heo, J., Kim, Y. J., Kim, D.-G., Kaseb, A., Park, Y. N., Wang, X. W., Thorgeirsson, S. S. and Lee, J.-S. (2012), Sixty-five gene-based risk score classifier predicts overall survival in hepatocellular carcinoma. Hepatology, 55: 1443–1452. doi: 10.1002/hep.24813

Author Information

  1. 1

    Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, TX

  2. 2

    Department of Physiology, Chonbuk National University Medical School and Hospital, Jeonju, Korea

  3. 3

    Department of Biological Science, Dong-A University, Busan, Korea

  4. 4

    Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Korea, Yonsei University College of Medicine, Seoul, Korea

  5. 5

    Institute for Medical Convergence, Yonsei University College of Medicine, Seoul, Korea

  6. 6

    Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea

  7. 7

    Kosin University College of Medicine, Busan, Korea

  8. 8

    Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea

  9. 9

    Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chonbuk National University Medical School and Hospital, Jeonju, Korea

  10. 10

    Department of GI Medical Oncology, Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX

  11. 11

    Department of Pathology, Yonsei University College of Medicine, Seoul, Korea

  12. 12

    Lab of Human Carcinogenesis, National Cancer Institute, National Institute of Health, Bethesda, MD

  13. 13

    Lab of Experimental Carcinogenesis, National Cancer Institute, National Institute of Health, Bethesda, MD

  1. §

    These authors contributed equally to this work.

  2. fax: (713) 563-4235

*Department of Systems Biology, Division of Cancer Medicine, Unit 950, University of Texas MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77054

  1. †

    Potential conflict of interest: Nothing to report.

  2. §

    These authors contributed equally to this work.

  3. fax: (713) 563-4235

Publication History

  1. Issue published online: 19 APR 2012
  2. Article first published online: 18 MAR 2012
  3. Accepted manuscript online: 22 NOV 2011 06:31AM EST
  4. Manuscript Accepted: 3 NOV 2011
  5. Manuscript Received: 20 JUL 2011

Funded by

  • intramural faculty fund of the University of Texas MD Anderson Cancer Center to J-S Lee; MD Anderson Cancer Center
  • NCI CCSG. Grant Number: CA106672 Microarray data: GSE1898, GSE4024, GSE9843, GSE14520, GSE16757, and E-TABM-36

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Abstract

Clinical application of the prognostic gene expression signature has been delayed due to the large number of genes and complexity of prediction algorithms. In the current study we aimed to develop an easy-to-use risk score with a limited number of genes that can robustly predict prognosis of patients with hepatocellular carcinoma (HCC). The risk score was developed using Cox coefficient values of 65 genes in the training set (n = 139) and its robustness was validated in test sets (n = 292). The risk score was a highly significant predictor of overall survival (OS) in the first test cohort (P = 5.6 × 10−5, n = 100) and the second test cohort (P = 5.0 × 10−5, n = 192). In multivariate analysis, the risk score was a significant risk factor among clinical variables examined together (hazard ratio [HR], 1.36; 95% confidence interval [CI], 1.13-1.64; P = 0.001 for OS). Conclusion: The risk score classifier we have developed can identify two clinically distinct HCC subtypes at early and late stages of the disease in a simple and highly reproducible manner across multiple datasets. (HEPATOLOGY 2011)

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