Proteomics Study Of WHV/c-myc Transgenic Mouse Model Of Liver Cancer By Liquid Chromatography-tandem MS

Caner is one of the most significant diseases that threat human health hitherto. And liver cancer is the fifth most common cancer worldwide and the second one domestically. Most cases of the Liver cancer are diagnosed at the stage with a severe malignancy and the five-year survival rate is only about 6%. Therefore, besides developing better treatments, early diagnosis may offer better opptunitity for treatment and improve patients'survival rate. The combination of transgenic mouse model of liver cancer and global and high-throughput proteomic technology provide an efficient method to screen for early diagnosis biomarkers of liver cancer.Using two-dimensional liquid chromatography-mass spectrometry coupled with label-free quantification method, we analyzed the liver tissue of three WHV/c-myc transgenic mice and three non-transgenic mice at two-month age. We identified and unambiguously quantitated 4832 proteins and among them, 323 proteins displayed statistically different expression after a two-group t-test(P<0.05). Hierarchical clustering analysis of this 323 proteins effectively distinguished liver tissues of transgenic mice from control samples. Molecular functional annotation of the 136 proteins with a higher or unique expression in transgenic liver tissues and 145 in control samples showed that proteins of transporter and signal transduction activity were doubled in quantity in the control expression comparing to the ones in transgenic mice, while proteins of binding and catalytic activity were remarkably enriched in the uniquely identified proteins in liver of transgenic mice. Futher more, principal component analysis (PCA) was carried out on the peptide counts of 139 differentially expressed proteins coming from a restricted standard. The result of PCA showed that the transgenic and control liver samples were distinctly grouped into two clusters by first and second components.The integrated approaches applied in this study demonstrated that the combination of proper bioinformatics tools and label free quantification proteomics can be an efficient method in analysis of the abnormal changes in liver of transgenic mice. It can be extended to general strategy of quantitative proteomics, especially for the multi-sample scenario. The differentially expressed proteins screened and identified in this study can be good candidates for futher study of early biological events of cancer developments in transgenic mouse model, and potential biomarkers for early diagnosis of liver cancer in the future.