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Gene Expression–Based Recurrence Prediction of Hepatitis B Virus–Related Human Hepatocellular Carcinoma

Authors' Affiliations: ¹ Department of Internal Medicine and Liver Research Institute; ² Seoul National University Biomedical Informatics, and ³ Department of Surgery, Seoul National University College of Medicine, ⁴ Department of Internal Medicine, Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, Korea; ⁵ Korea Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Korea; ⁶ Laboratory of Experimental Carcinogenesis, ⁷ Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland; ⁸ Department of Systems Biology, Division of Cancer Medicine, University of Texas M. D. Anderson Cancer Center, Houston, Texas; and ⁹ Department of Morphology and Molecular Pathology, University of Leuven, Leuven, Belgium

Requests for reprints: Yoon Jun Kim, Department of Internal Medicine, Seoul National University Hospital, 28 Yongon-dong, Chongno-gu, Seoul 110-744, Korea. Phone: 82-22072-3081, 82-2740-8112; Fax: 82-2-743-6701; E-mail: yoonjun{at}snu.ac.kr.

Purpose: The poor prognosis of hepatocellular carcinoma (HCC) is, in part, due to the high rate of recurrence even after "curative resection" of tumors. Therefore, it is axiomatic that the development of an effective prognostic prediction model for HCC recurrence after surgery would, at minimum, help to identify in advance those who would most benefit from the treatment, and at best, provide new therapeutic strategies for patients with a high risk of early recurrence.

Experimental Design: For the prediction of the recurrence time in patients with HCC, gene expression profiles were generated in 65 HCC patients with hepatitis B infections.

Result: Recurrence-associated gene expression signatures successfully discriminated between patients at high-risk and low-risk of early recurrence (P = 1.9 x 10^–6, log-rank test). To test the consistency and robustness of the recurrence signature, we validated its prognostic power in an independent HCC microarray data set. CD24 was identified as a putative biomarker for the prediction of early recurrence. Genetic network analysis suggested that SP1 and peroxisome proliferator–activated receptor- might have regulatory roles for the early recurrence of HCC.

Conclusion: We have identified a gene expression signature that effectively predicted early recurrence of HCC independent of microarray platforms and cohorts, and provided novel biological insights into the mechanisms of tumor recurrence.