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Open Access Highly Accessed Research article

Clinicopathologic and gene expression parameters predict liver cancer prognosis

Ke Hao1*, John Lamb1, Chunsheng Zhang1, Tao Xie1, Kai Wang1, Bin Zhang1, Eugene Chudin1, Nikki P Lee2, Mao Mao1, Hua Zhong1, Danielle Greenawalt1, Mark D Ferguson1, Irene O Ng3, Pak C Sham4, Ronnie T Poon2, Cliona Molony1, Eric E Schadt1, Hongyue Dai1 and John M Luk5*

Author Affiliations

1 Merck Research Laboratories, Boston, MA, USA

2 Department of Surgery, University of Hong Kong, Pokfulam, Hong Kong SAR, China

3 Department of Pathology, The University of Hong Kong, Pokfulam, Hong Kong SAR, China

4 Department of Psychiatry and Genome Research Center, The University of Hong Kong, Pokfulam, Hong Kong SAR, China

5 Departments of Pharmacology and Surgery and Cancer Science Institute, National University of Singapore, Singapore

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BMC Cancer 2011, 11:481  doi:10.1186/1471-2407-11-481

Published: 9 November 2011

Abstract

Background

The prognosis of hepatocellular carcinoma (HCC) varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model.

Methods

Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types) in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction.

Results

HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis.

Conclusion

When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome.