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Article: Clinicopathologic and gene expression parameters predict liver cancer prognosis
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TitleClinicopathologic and gene expression parameters predict liver cancer prognosis
 
AuthorsHao, K2
Lamb, J2
Zhang, C2
Xie, T2
Wang, K2
Zhang, B2
Chudin, E2
Lee, NP1
Mao, M2
Zhong, H2
Greenawalt, D2
Ferguson, MD2
Ng, IO1
Sham, PC1
Poon, RT1
Molony, C2
Schadt, EE2
Dai, H2
Luk, JM3
 
Issue Date2011
 
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmccancer/
 
CitationBmc Cancer, 2011, v. 11 [How to Cite?]
DOI: http://dx.doi.org/10.1186/1471-2407-11-481
 
AbstractBackground: 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. © 2011 Hao et al; licensee BioMed Central Ltd.
 
ISSN1471-2407
2013 Impact Factor: 3.319
2013 SCImago Journal Rankings: 1.686
 
DOIhttp://dx.doi.org/10.1186/1471-2407-11-481
 
PubMed Central IDPMC3240666
 
ISI Accession Number IDWOS:000298170400001
Funding AgencyGrant Number
Research Grants Council of Hong Kong
Hong Kong Government
Funding Information:

The work was supported by Research Grants Council of Hong Kong and Innovation and Technology Fund of the Hong Kong Government to J.M.L. We would like to thank for the technical supports from Ashley Wong and Kit-Yuk Mak of the Queen Mary Hospital. IOL Ng is a Loke Yew Professor in Pathology.

 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorHao, K
 
dc.contributor.authorLamb, J
 
dc.contributor.authorZhang, C
 
dc.contributor.authorXie, T
 
dc.contributor.authorWang, K
 
dc.contributor.authorZhang, B
 
dc.contributor.authorChudin, E
 
dc.contributor.authorLee, NP
 
dc.contributor.authorMao, M
 
dc.contributor.authorZhong, H
 
dc.contributor.authorGreenawalt, D
 
dc.contributor.authorFerguson, MD
 
dc.contributor.authorNg, IO
 
dc.contributor.authorSham, PC
 
dc.contributor.authorPoon, RT
 
dc.contributor.authorMolony, C
 
dc.contributor.authorSchadt, EE
 
dc.contributor.authorDai, H
 
dc.contributor.authorLuk, JM
 
dc.date.accessioned2012-05-29T06:14:26Z
 
dc.date.available2012-05-29T06:14:26Z
 
dc.date.issued2011
 
dc.description.abstractBackground: 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. © 2011 Hao et al; licensee BioMed Central Ltd.
 
dc.description.naturepublished_or_final_version
 
dc.identifier.citationBmc Cancer, 2011, v. 11 [How to Cite?]
DOI: http://dx.doi.org/10.1186/1471-2407-11-481
 
dc.identifier.citeulike10021145
 
dc.identifier.doihttp://dx.doi.org/10.1186/1471-2407-11-481
 
dc.identifier.hkuros197821
 
dc.identifier.isiWOS:000298170400001
Funding AgencyGrant Number
Research Grants Council of Hong Kong
Hong Kong Government
Funding Information:

The work was supported by Research Grants Council of Hong Kong and Innovation and Technology Fund of the Hong Kong Government to J.M.L. We would like to thank for the technical supports from Ashley Wong and Kit-Yuk Mak of the Queen Mary Hospital. IOL Ng is a Loke Yew Professor in Pathology.

 
dc.identifier.issn1471-2407
2013 Impact Factor: 3.319
2013 SCImago Journal Rankings: 1.686
 
dc.identifier.pmcidPMC3240666
 
dc.identifier.pmid22070665
 
dc.identifier.scopuseid_2-s2.0-80655128680
 
dc.identifier.urihttp://hdl.handle.net/10722/148658
 
dc.identifier.volume11
 
dc.languageeng
 
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmccancer/
 
dc.publisher.placeUnited Kingdom
 
dc.relation.ispartofBMC Cancer
 
dc.relation.referencesReferences in Scopus
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.titleClinicopathologic and gene expression parameters predict liver cancer prognosis
 
dc.typeArticle
 
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<contributor.author>Chudin, E</contributor.author>
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<description.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. &#169; 2011 Hao et al; licensee BioMed Central Ltd.</description.abstract>
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Author Affiliations
  1. The University of Hong Kong
  2. Merck Research Laboratories
  3. National University of Singapore