For decades, classical toxicology has used risk assessments based on animal studies for regulatory decisions. The underlying assumption is that important biological functions are often conserved across species. In continuation of this paradigm, the effort in toxicogenomics is placed on studying rodents and other surrogates using advanced genomics technologies, such as DNA microarrays. Microarray studies enable simultaneous measurement of the expression of large numbers of genes. Given the completion of the DNA sequence of the human, mouse and rat genomes [1
], genes identified in microarray studies can be readily compared across-species with respect to the gene orthologs [4
]. This assumes that genes co-expressed across multiple species are likely to have conserved functions [6
]. Thus, microarray analysis offers the possibility of furthering our understanding of cross-species commonalities and differences that could lead to more effective use of animal models to understand the cause and progression of diseases in human at the mechanistic level.
Hepatocellular carcinoma (HCC) is a leading cause of death worldwide and, like most cancers, is a genetic disease caused by the accumulation of genetic and epigenetic cell alterations. The progression of hepatic neoplasia is characterized by increasing genetic instability, including duplication and deletion of parts of chromosomes and an increasing proliferative growth advantage of the affected cells. Molecular cytogenetic techniques, such as Comparative genomic hybridization (CGH) and Spectral karyotyping (SKY) [9
], have allowed evaluation of chromosomal aberrations in HCC. More recently, Crawley [12
] has demonstrated the ability of comparative genomic microarray analysis (CGMA) to elucidate alteration of specific genes together with the genetic changes at the chromosome level based on microarray data. Thus, microarray analysis provides an unprecedented opportunity to further the understanding of the etiology and progression of liver cancer.
Bioinformatics methods and tools are essential to analyze and interpret data from microarrays. The critical and urgent task is to associate altered patterns of gene expression with disease. Interpreting microarray data in the context of signaling and regulatory pathways is a particularly effective bioinformatics approach to transform data into biological meaning and to generate hypotheses for further research. Using pathways, disease mechanisms can be interpreted as disturbances of the intricate interconnections among genes, molecules and cells. Most reported pathway analysis of microarray data has examined the role of differentially expressed genes in pathways selected with a priori knowledge. Alternatively, significant pathways can be identified based on statistical analysis, potentially leading to new discoveries and a more complete interpretation of microarray data in the context of biological processes at the mechanistic level.
The primary mechanism for the analysis of HCC is by the administration of carcinogenic agents. A number of model systems have been developed to understand the pathogenesis of primary liver cancer [13
]. Additionally, the development of transgenic models permit the analysis of the genetic basis for the induction and progression of HCC [16
]. The albumin-Simian virus 40 (SV40) T antigen transgenic rat contains the mouse albumin-promoter/enhancer linked to the coding region of the SV40 large T antigen (SV 40 tag). SV40 T antigen inactivates both p53 and Rb, resulting in spontaneous development of hepatic neoplasms (adenoma and carcinoma) within 6–9 months. Thus, the Albumin-SV40 T antigen transgenic rat can be used to examine liver cancer development and maintenance [20
In this manuscript, we describe a bioinformatics process where microarray data from the SV40 transgenic rat was examined for application to the study of HCC in human. We first used a novel visualization tool to investigate liver cancer by mapping chromosomal location of differentially expressed genes from the rat model to the chromosomal regions of human orthologs. Then, CGMA analysis was used to relate gene expression bias patterns to cytogenetic aberration profiles on human. Lastly, a statistical approach was used to identify several pathways involved in human HCC based on the rat microarray. The pathway analysis reveals that the expected involvement in apoptosis, cell cycle, growth and differentiation, genetic stability and methionine metabolism are important for cancer development, maintenance and progression. The results indicate that the gene expression profiles of the transgenic rat model may be useful in the study of human liver cancer.