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Central genetic alterations common to all HCV-positive, HBV-positive and non-B, non-C hepatocellular carcinoma: a new approach to identify novel tumor markers
Authors:Kurokawa Yukinori  Honma Kimi  Takemasa Ichiro  Nakamori Shoji  Kita-Matsuo Hiroko  Motoori Masaaki  Nagano Hiroaki  Dono Keizo  Ochiya Takahiro  Monden Morito  Kato Kikuya
Affiliation:Taisho Laboratory of Functional Genomics, Nara Institute of Science and Technology, Nara 630-0101, Japan.
Abstract:Hepatocellular carcinoma (HCC) is a common malignancy, but the prognosis remains poor due to the lack of sensitive diagnostic markers. To gain insight into the central molecular features common to all types of HCC, and to identify novel diagnostic markers or therapeutic targets for HCC, we performed a gene expression profiling analysis using a high throughput RT-PCR system. After examining the mRNA expression of 3,072 genes in 204 (119 tumor and 85 non-tumor) liver samples, we identified differential gene expression between the HCV group (n=80), HBV group (n=19) and non-B, non-C group (n=20) with a principal component analysis and a correlation spectrum analysis. After selection of genes differentially expressed between tumor and non-tumor tissues (p<0.01) within each HCC group, a total of 51 differentially expressed genes (23 upregulated and 28 downregulated genes) were found to be common to the three HCC groups. Gene Ontology grouping analysis revealed that genes with functions related to cell proliferation or differentiation and genes encoding extracellular proteins were found to be significantly enriched in these 51 common genes. Using an atelocollagen-based cell transfection array for functional analysis of eight upregulated genes, five (CANX, FAM34A, PVRL2, LAMR1, and GBA) significantly inhibited cellular apoptosis by two independent assays. In conclusion, we identified 51 differentially expressed genes, common to all HCC types. Among these genes, there was a high incidence of anti-apoptotic activity. This combination approach with the advanced statistical methods and the bioinformatical analysis may be useful for finding novel molecular targets for diagnosis and therapy.
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