Abstract
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. For the prediction of the recurrence time in patients with HCC, gene expression profiles were generated in 65 HCC patients with hepatitis B infections. 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-alpha might have regulatory roles for the early recurrence of HCC. 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.
Highlights
The poor prognosis of hepatocellular carcinoma (HCC) is, in part, due to the high rate of recurrence even after ‘‘curative resection’’of tumors.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
We examined the gene expression profiles of 65 patients with HCC associated with the same viral background of hepatitis B virus (HBV) infection and identified molecular markers that predict HCC prognostic subtypes of high-risk and low-risk of early recurrence
Hierarchical clustering with these 628 recurrence signature genes subdivided HCC patients into two subtypes that appropriately reflect the difference in recurrence times between patients with HCC (Fig. 1A)
Summary
The poor prognosis of hepatocellular carcinoma (HCC) is, in part, due to the high rate of recurrence even after ‘‘curative resection’’of tumors.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. 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. Recent studies on gene expression profiles could successfully predict recurrence, metastasis, or survival prognosis of HCCs (13 – 17). Even though these studies successfully provide prognostic markers for clinical application, the lack of consistency and robustness of predictors generated from different microarray platforms remain one of the major obstacles for the clinical use of microarray-based predictors [18, 19]. Gene Expression Profile Predicts Recurrence of HBV-Related HCC (i.e., patients free of recurrence for >1year, n = 25; blue).
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