Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2007 Jan;35(Database issue):D727-31.
doi: 10.1093/nar/gkl845. Epub 2006 Nov 10.

OncoDB.HCC: An Integrated Oncogenomic Database of Hepatocellular Carcinoma Revealed Aberrant Cancer Target Genes and Loci

Affiliations
Free PMC article

OncoDB.HCC: An Integrated Oncogenomic Database of Hepatocellular Carcinoma Revealed Aberrant Cancer Target Genes and Loci

Wen-Hui Su et al. Nucleic Acids Res. .
Free PMC article

Abstract

The OncoDB.HCC (http://oncodb.hcc.ibms.sinica.edu.tw) is based on physical maps of rodent and human genomes containing quantitative trait loci of rodent HCC models and various human HCC somatic aberrations including chromosomal data from loss of heterozygosity and comparative genome hybridization analyses, altered expression of genes from microarray and proteomic studies, and finally experimental data of published HCC genes. Comprehensive integration of HCC genomic aberration data avoids potential pitfalls of data inconsistency from single genomic approach and provides lines of evidence to reveal somatic aberrations from levels of DNA, RNA to protein. Twenty-nine of 30 (96.7%) novel HCC genes with significant altered expressions in compared between tumor and adjacent normal tissues were validated by RT-PCR in 45 pairs of HCC tissues and by matching expression profiles in 57 HCC patients of re-analyzed Stanford HCC microarray data. Comparative mapping of HCC loci in between human aberrant chromosomal regions and QTLs of rodent HCC models revealed 12 syntenic HCC regions with 2 loci effectively narrowed down to 2 Mb. Together, OncoDB.HCC graphically presents comprehensive HCC data integration, reveals important HCC genes and loci for positional cloning and functional studies, and discloses potential molecular targets for improving HCC diagnosis and therapy.

Figures

Figure 1
Figure 1
The chromosome view of OncoDB.HCC: (a) indicated the chromosomal region displayed in cytogenetic position; (b) demonstrated the expression intensity of genes along the physical positions of chromosome in terms of patient number by selecting gene expression cut-off value (default value = 1) in re-analyzed Stanford HCC microarray data; (c) showed the microarray/proteomic expression results from references; (d) indicated the positions of genes collected from wet-lab experimental results; (e) revealed the comparative maps and the syntenic regions of mouse and rat HCC QTLs; (f) displayed the LOH frequencies and minimum deletion regions (MDR) in positions of microsatellite markers; and (g) the cytogenetic locations of CGH results. All genes and markers were annotated and hyperlinked against physical maps of genomes in Ensembl. The up-regulated genes and gain/amplified chromosomal regions were displayed in red series color. In contrast, the down-regulated genes and loss/deleted chromosome regions were displayed in green series color.
Figure 2
Figure 2
Experimental validation of representative genes in the HCC gene set. For each gene, the semi-quantitative RT–PCR results in 45 HCC pairs are presented in gel images (left), in quantified expression profiles after normalization with β-actin expression as internal control (upper right) and in comparison with expression profiles of the gene in re-analyzed Stanford HCC microarray data from expression view of OncoDB.HCC (lower right). (A) AURKA is a positive control for HCC data process; (B and C) genes selected from criteria of significant expression difference in at lease three independent microarray/proteomic studies; and (DF) genes selected with criteria of at least 2-fold expression difference in at least 70% of paired arrays in re-analyzed Stanford HCC microarray data.

Similar articles

See all similar articles

Cited by 30 articles

See all "Cited by" articles

References

    1. Hanahan D., Weinberg R.A. The hallmarks of cancer. Cell. 2000;100:57–70. - PubMed
    1. Alaiya A., Al-Mohanna M., Linder S. Clinical cancer proteomics: promises and pitfalls. J. Proteome Res. 2005;4:1213–1222. - PubMed
    1. Liang P., Pardee A.B. Analysing differential gene expression in cancer. Nature Rev. Cancer. 2003;3:869–876. - PubMed
    1. Zhou X., Rao N.P., Cole S.W., Mok S.C., Chen Z., Wong D.T. Progress in concurrent analysis of loss of heterozygosity and comparative genomic hybridization utilizing high density single nucleotide polymorphism arrays. Cancer Genet. Cytogenet. 2005;159:53–57. - PubMed
    1. Hanash S. Integrated global profiling of cancer. Nature Rev. Cancer. 2004;4:638–644. - PubMed

Publication types

MeSH terms

Feedback