This article is part of the supplement: Second Annual MidSouth Computational Biology and Bioinformatics Society Conference. Bioinformatics: a systems approach .

Proceedings

Bioinformatics approaches for cross-species liver cancer analysis based on microarray gene expression profiling

H Fang¹ , W Tong² , R Perkins¹ , L Shi² , H Hong¹ , X Cao¹ , Q Xie¹ , SH Yim³ , JM Ward⁴ , HC Pitot⁵ and YP Dragan²

¹  Division of Bioinformatics, Z-Tech Corporation, 3900 NCTR Road, Jefferson, AR 72079

²  Division of Systems Toxicology, National Center for Toxicological Research (NCTR), FDA, 3900 NCTR Road, Jefferson, AR 72079

³  Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland 20892

⁴  Verterinary and Tumor Pathology Section, Center for Cancer Research, National Cancer Institute, Frederick, Maryland 21702

⁵  McArdle Laboratory for Cancer Research, University of Wisconsin, Madison, WI 53706

author email corresponding author email

BMC Bioinformatics 2005, 6(Suppl 2):S6doi:10.1186/1471-2105-6-S2-S6

Published:	15 July 2005

Abstract

Background

The completion of the sequencing of human, mouse and rat genomes and knowledge of cross-species gene homologies enables studies of differential gene expression in animal models. These types of studies have the potential to greatly enhance our understanding of diseases such as liver cancer in humans. Genes co-expressed across multiple species are most likely to have conserved functions. We have used various bioinformatics approaches to examine microarray expression profiles from liver neoplasms that arise in albumin-SV40 transgenic rats to elucidate genes, chromosome aberrations and pathways that might be associated with human liver cancer.

Results

In this study, we first identified 2223 differentially expressed genes by comparing gene expression profiles for two control, two adenoma and two carcinoma samples using an F-test. These genes were subsequently mapped to the rat chromosomes using a novel visualization tool, the Chromosome Plot. Using the same plot, we further mapped the significant genes to orthologous chromosomal locations in human and mouse. Many genes expressed in rat 1q that are amplified in rat liver cancer map to the human chromosomes 10, 11 and 19 and to the mouse chromosomes 7, 17 and 19, which have been implicated in studies of human and mouse liver cancer. Using Comparative Genomics Microarray Analysis (CGMA), we identified regions of potential aberrations in human. Lastly, a pathway analysis was conducted to predict altered human pathways based on statistical analysis and extrapolation from the rat data. All of the identified pathways have been known to be important in the etiology of human liver cancer, including cell cycle control, cell growth and differentiation, apoptosis, transcriptional regulation, and protein metabolism.

Conclusion

The study demonstrates that the hepatic gene expression profiles from the albumin-SV40 transgenic rat model revealed genes, pathways and chromosome alterations consistent with experimental and clinical research in human liver cancer. The bioinformatics tools presented in this paper are essential for cross species extrapolation and mapping of microarray data, its analysis and interpretation.