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Ann Surg Oncol. 2012 Jul;19 Suppl 3:S328-38. doi: 10.1245/s10434-011-1709-0. Epub 2011 Apr 30.

Integrated analysis of prognostic gene expression profiles from hepatitis B virus-positive hepatocellular carcinoma and adjacent liver tissue.

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  • 1Division of Radiation Cancer Research, Korea Institute of Radiological and Medical Sciences, Seoul, Korea.

Abstract

BACKGROUND:

The tissue environment in the region of hepatocellular carcinoma (HCC) influences both vascular invasion and recurrence. Thus, HCC patient prognosis depends on the characteristics not only of the tumor but also those of adjacent surrounding liver tissue.

MATERIALS AND METHODS:

Expression profiles of both tumor and adjacent liver tissue following curative resection were measured to discriminate 56 hepatitis B virus-positive HCC patients into subgroups based on survival risk. This approach was further tested in 40 patients.

RESULTS:

Expression profiles of both tumor and adjacent liver tissue successfully discriminated 56 training samples into 2 subgroups, those at low- or high-risk for survival and recurrence. However, the prognostic gene set selected for tumor tissue was quite different from that for adjacent tissues. This variation in prognostic genes resulted in a change in allocation of patients within each low- or high-risk group. Combination of survival subgroups from tumor and adjacent liver tissue significantly improved the prediction of prognostic outcome. This integrative approach was confirmed to be effective in a further 40 test patients. A clinicopathological study showed that survival subgroups divided by tumor and adjacent liver tissue gene expression were also statistically associated with UICC stage and extent of cell differentiation, respectively.

CONCLUSIONS:

Variation in gene expression during the nontumor stage as well as the tumor stage may affect the prognosis of HCC patients, and integration of the gene expression profiles of HCC and adjacent liver tissue increases discriminatory effectiveness between patient groups, predicting clinical outcomes with enhanced statistical reliability.

PMID:
21533656
DOI:
10.1245/s10434-011-1709-0
[PubMed - indexed for MEDLINE]
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