Classification and survival prediction of human HCC: Microarray technologies have been successfully used to predict clinical outcome and survival, as well as to classify different types of cancer. Microarray technologies have also been applied in studies to define global gene expression patterns in primary human HCC and HCC-derived cell lines in attempts to gain insight into the mechanisms of hepatocarcinogenesis. These studies have identified subgroups of HCC that differ according to etiological factors, mutations of tumor suppressor genes, rate of recurrence, and intrahepatic metastasis, as well as novel molecular markers for HCC diagnosis. However, most of these studies identified genes that are associated with limited aspects of tumor pathogenesis, and thus failed to create molecular prognostic indices that could be applied to the HCC patient population in general. We have investigated the possibility that variations in gene expression of HCC obtained at diagnosis would permit the identification of distinct subclasses of HCC patients with different prognoses. We have analyzed global gene expression patterns of 91 HCC to define the molecular characteristics of the tumors and to test the prognostic value of the expression profiles. Unsupervised classification methods revealed for the first time two distinctive subclasses of HCC, subclass A (bad prognosis) and subclass B (better prognosis) that are highly associated with patient survival. This association was validated by five independent supervised learning methods. Tumors from the low survival subclass A have strong cell proliferation and anti-apoptosis gene expression signatures. In addition, the low survival subclass displays higher expression of genes involved in ubiquitination and histone modification, suggesting that these processes are involved in accelerating the progression of HCC. We conclude that the biological differences identified in the HCC subclasses should provide an attractive source for the development of therapeutic targets (e.g., HIF1a) for selective treatment of HCC patients. We further believe that our overall success in this work marks the completion of the first step in the development of a molecular prognostic evaluation by use of gene expression profiling technology and unsupervised and supervised learning methods to predict survival of HCC patients. Survival genes in human HCC: Based on the strong association between the unsupervised analysis of the expression profiles of the HCC patients with survival, we identified (using the Cox proportional hazards model) 406 genes whose expression is significantly correlated with length of survival (P<.001). Also, hierarchical cluster analysis of HCC with the 406 survival genes is highly similar (i.e., reproduces subclasses A and B) to the previous analysis with all the genes. The survival genes fall within several biological groups. As expected the cell proliferation group is the best predictor of an unfavorable outcome of the disease, which is consistent with previous analyses in human lymphomas. For example, expression of typical cell proliferation markers such as PCNA and cell cycle regulators such as CDK4, CCNB1, CCNA2, and CKS2 are much higher in subclass A than subclass B. Also, many genes that are expressed more highly in subclass A are anti-apoptotic. Included in the anti-apoptotic group are the prothymosin a (PTMA/ProT) and SET genes that so far have not been associated with human HCC. PTMA is an inhibitor of apoptosome formation, the essential final step for activation of the caspase-dependent cascade in the apoptotic pathway, and the SET gene is an inhibitor of the Granzyme A-induced caspase-independent apoptosis pathway. SET is also a subunit of the inhibitor of acetyltransferases complex that regulates histone modification and gene expression.
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