Predicting the potential human health risk, especially liver injury posed by chemical stressors has long been a major challenge for toxicologists, and the use of microarrays to measure responses to liver injury relevant genes, and to identify selectiv...
Predicting the potential human health risk, especially liver injury posed by chemical stressors has long been a major challenge for toxicologists, and the use of microarrays to measure responses to liver injury relevant genes, and to identify selective, sensitive biomarkers of liver injury is a major application of predictive and discovery liver toxicology. To investigate this possibility, we investigated whether tissues from liver injury display characteristic sets of genes in human. RNAs from chronic hepatitis, cirrhosis, dysplasia, liver cancer (HCC) and normal live tissues were subjected to whole genome expression microarray and miRNA microarray. We collected at least 8 cases of each multi step liver disease and applied these multi-step hepatocellular carcinoma models and analysed characteristic molecular signature for each chronic hepatitis with low grade fibrosis (FL), chronic hepatitis with high grade fibrosis (FH), liver cirrhosis (CS), low grade dysplasia (DL), high grade dysplasia (DH), and tumor grade 1 (TG1), 2 (TG2), and 3 (TG3) using whole genome expression microarrays and miRNA microarray. Through whole genome expression microarray analysis, we applied these multi-step hepatocellular carcinogenesis model and analysed characteristic molecular signature for each liver disease step. Combined analysis of pathway mining and database analysis revealed that large-scale molecular signature exists in multistep liver injury.olat over, each liver disease specific miRNA signatures were exhibited using PhenomiR database. In conclusion, the identification of large-scale molecular changes in multistep liver carcinogenesis revealed that characteristic molecular signatures are associated with different epigenetic changes and molecular pathways and that these large-scale characteristic molecular changes could be used as predictable liver injury.