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Using oligonucleotide microarray data of 45 hepatocellular carcinoma (HCC) samples, we evaluated gene expression in hepatitis B virus-positive and hepatitis C virus-positive HCCs (HBV-and HCV-HCCs) for an association with liver cirrhosis (LC). In all, 89 genes were expressed differentially between HBV-HCCs associated with LC and those not associated with LC. Among them, tumors from LC patients showed significantly lower expression levels of 72 genes and significantly higher levels of 17 genes than the levels found in tumors from non-LC patients. The former included genes responsible for signal transduction, transcription, metabolism, and cell growth. The latter included a tumor suppressor gene and a cell-growth-related gene. Only eight genes were expressed differentially between HCV-HCCs associated with and without LC. Our findings provide as a framework for clarifying the role of LC in HBV-and HCVrelated hepatocarcinogenesis.

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Abstract-A bootstrap technique for nearest neighbor classifier design is proposed. Our primary interest in designing a classifier is in small training sample size situations. Conventional bootstrapping techniques sample the training samples with replacement. On the other hand, our technique generates bootstrap samples by locally combining original training samples. The nearest neighbor classifier is designed on the bootstrap samples and is tested on the test samples independent of training samples. The performance of the proposed classifier is demonstrated on three artificial data sets and one real data set. Experimental results show that the nearest neighbor classifier designed on the bootstrap samples outperforms the conventional k-NN classifiers as well as the edited 1 -NN classifiers, particularly in high dimensions.Index Terms-Bootstrap, nearest neighbor classifier, error rate, peaking phenomenon, small training sample size, high dimensions, outlier.

The outcome of patients with hepatocellular carcinoma (HCC) remains poor because of the high frequency of intrahepatic recurrence (IHR), particularly early IHR within 1 year of hepatectomy. To search for genes involved in early IHR, we performed DNA microarray analysis in a training set of 33 HCCs and selected 46 genes linked to early IHR from approximately 6,000 genes by means of a supervised learning method. Gene selection was validated by a false discovery rate of 0.37%. The 46 genes included many immune response-related genes, which were all downregulated in HCCs with early IHR. Four of these genes (HLA-DRA, HLA-DRB1, HLA-DG and HLA-DQA), encoding MHC class II antigens, were coordinately downregulated in HCCs with early IHR compared to levels in HCCs with nonrecurrence. A cluster analysis reproduced expression patterns of the 4 MHC class II genes in 27 blinded HCC samples. To localize the major site of production of HLA-DR protein in the tumor, we used 50 frozen specimens from 50 HCCs. Immunofluorescence staining showed that HLA-DR protein levels in tumor cells, but not in stromal cells, were associated with the transcription levels of HLA-DRA determined by both DNA microarray analysis and real-time quantitative reverse transcription-PCR. Univariate analysis showed that tumor HLA-DR protein expression, pTNM stage and venous invasion were associated with early IHR. Multivariate analysis showed that tumor HLA-DR protein expression was one of the independent risk factors for early IHR, suggesting HLA-DR protein potential as a biomarker and a molecular target for therapeutic intervention. ' 2005 Wiley-Liss, Inc.

Although the physiological actions of many herbs are gradually being elucidated at the molecular level, it remains unclear how individual components of herbs contribute to their biological activities. In the present study, the antiproliferative activity of Coptidis rhizoma, a medicinal herb, and the major component berberine was investigated in 8 human pancreatic cancer cell lines. Gene expression patterns associated with sensitivities to each agent were analyzed with oligonucleotide arrays that comprised approximately 11,000 genes. We used a tetrazolium dye (MTT) assay to determine ID 50 values after the 8 cell lines were exposed to the 2 agents for 72 hr. The ID 50 value for berberine was correlated positively with that for C. rhizoma (r‫,527.0؍‬ p‫.)1040.0؍‬ C. rhizoma killed tumor cells more effectively than purified berberine when normalized to the level of berberine present in the herb. From the oligonucleotide array data, we selected 20 and 13 genes with strong correlations (r 2 >0.81) to ID 50 values for berberine and C. rhizoma, respectively. Among these 33 genes, the levels of expression of 12 were correlated with the ID 50 values of both agents, suggesting that these genes are associated with tumor-killing activity of berberine in C. rhizoma. Expression of the remaining 21 genes was correlated with the ID 50 value of either purified berberine or C. rhizoma. Thus, we identified common and distinct genes responsible for anti-proliferative activities of purified berberine and C. rhizoma. This strategy may improve our understanding of the actions of herbs with antitumor activities.

Using high-density oligonucleotide array, we comprehensively analyzed expression levels of 12 600 genes in 50 hepatocellular carcinoma (HCC) samples with positive hepatitis C virus (HCV) serology (well (G1), moderately (G2), and poorly (G3) differentiated tumors) and 11 non-tumorous livers (L1 and L0) with and without HCV infection. We searched for discriminatory genes of transition (L0 vs. L1, L1 vs. G1, G1 vs. G2, G2 vs. G3) with a supervised learning method, and then arranged the samples by self-organizing map (SOM) with the discriminatory gene sets. The SOM arranged the five clusters on a unique sigmoidal curve in the order L0, L1, G1, G2, and G3. The sample arrangement reproduced development-related features of HCC such as p53 abnormality. Strikingly, G2 tumors without venous invasion were located closer to the G1 cluster, and most G2 tumors with venous invasion were located closer to the G3 cluster (P = 0.001 by FisherÕs exact test). Our present profiling data will serve as a framework to understand the relation between the development and dedifferentiation of HCC.

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