| Hepatocellular carcinoma (HCC) is one of the most common and aggressive malignancies worldwide, ranking the fifth most important cancer in terms of numbers of cases and the fourth in terms of mortality overall. And it has even become the second cancer killer in China since 1990s. Although great progress has been made in clinical research of HCC in the past decades and a parial of patients with HCC who was diagnosed and treated at an early stage have survived for a long time, most of the patients are diagnosed at a very late stage with extremely poor prognosis. The overall curable rate of HCC is still less than 5%. The high mortality of HCC is mainly due to the occurrence of intra-hepatic metastases. Little is known about the molecular basis of intra-hepatic metastasis or about specific therapeutic targets in these patients by far.In order to identify genes associated with intra-hepatic metastasis and probe into the mechanism of intrahepatic metastasis of HCC , we analyzed the gene expression profiles of HCC tumors from 40 patients without or with accompanying intra-hepatic metastases (tumor thrombi or intra-hepatic spreads) in a genome wide scale using a cDNA microarray technology. Among the 40 patients, 27 were HCC with intra-hepatic metastases (12 with tumor thrombi and 15 with intra-hepatic spreads) and the other 13 patients were single HCC without metastasis. The cDNA microarrays were fabricated at the Advanced Technology Center of National Cancer Institute (NCI), Natoional Institute of Health (NIH). Each array contains 9180 cDNA clones with 7102 "named" genes, 1179 EST clones, and 122 Incyte clones. The cDNA targets were prepared by a direct labeling approach using two kinds of fluorescences with different color: Cy3 (red) and Cy5 (green). The fluorescent targets were prepared as following: 100 μg of total RNA from non-cancerous liver tissue were labeled with Cy3-conjuagated deoxynucleotides and 200 μg of total RNA from primary HCC or metastasis were labeled with Cy5-conjuagated deoxynucleotides (Amersham) by the oligo dT-primed polymerization using SuperScript II reverse transcriptase (Invitrogen). The targets were then mixed together and hybridized with the cDNA clones on microarray. The Cy3 and Cy5 fluorescent intensities for each clone weredetermined by the Axon GenePix 4000 scanner, and were analyzed by the GenePix Pro 3.0 software to subtract the background signals. The expression data were then filtered based on their channel intensities, spots size and flag (missing data), and the Cy5/Cy3 ratios were calculated and normalized by median-centering the log-ratio of all genes in each array. Eighteen of the 40 samples, when indicated, were sampled twice using two independent cDNA hybridizations.We used the Class Comparison Tool, a supervised machine learning algorithm of the BRB-ArrayTools software developed by the National Cancer Institute, to find genes differently expressed between HCC without and with intrahepatic metastasis using univariate F-tests and 2000 random permutations to confirm statistical significance. A significantly different gene expression profiling was found between HCC with intra-hepatic metastasis and HCC without metastasis and 153 of the 9180 genes were found to be differently expressed between them at a significant level of P <0.001. Unexpectedly, we found that the gene expression signature of primary HCCs was very similar to that of their corresponding metastases, and only 5 genes were found to be differently expressed between them without significance (P =0.132), implying that the genes favoring metastasis progression likely were initiated in the primary tumors. Then the Compound Covariate Predictor Tool (CCP), another supervised machine learning algorithm of BRB, was used to classify samples based on their gene expression profiles. We generated for the first time a molecular predicting model using the 153 genes found above which can be used to predict whether a HCC with the potential to metastasize. It not only correctly classified 20 samples used for generating...
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