| First Author | Yang H | Year | 2006 |
| Journal | J Proteome Res | Volume | 5 |
| Issue | 7 | Pages | 1555-67 |
| PubMed ID | 16823962 | Mgi Jnum | J:114932 |
| Mgi Id | MGI:3690413 | Doi | 10.1021/pr0504283 |
| Citation | Yang H, et al. (2006) Parallel analysis of transcript and translation profiles: identification of metastasis-related signal pathways differentially regulated by drug and genetic modifications. J Proteome Res 5(7):1555-67 |
| abstractText | Tumor metastasis is a complex multistep process normally involving dysregulation of multiple signal transduction pathways. In this study, we developed a novel approach to efficiently define dysreguated pathways associated with metastasis by comparing global gene and protein expressions of two distinct metastasis-suppressed models. Consequently, we identified common features shared by the two models which are potentially associated with metastasis. The efficiency of metastasis from the highly aggressive polyoma middle T-induced mouse mammary tumors was suppressed by either prolonged caffeine exposure or by breeding the animal to a low metastatic mouse strain. Molecular profiles of the primary tumors from both metastasis-suppressed classes were then derived to identify molecules and pathways that might underlie a common mechanism of metastasis. A number of differentially regulated genes and proteins were identified, including genes encoding basement membrane components, which were inversely related to metastatic efficiency. In addition, the analysis revealed that the Stat signal transduction pathways were potentially associated with metastasis inhibition, as demonstrated by enhanced Stat1 activation, and decreased Stat5 phosphorylation in both genetic and pharmacological modification models. Tumor cells of low-metastatic genotypes also demonstrated anti-apoptotic properties. The common changes of these pathways in all of the metastasis-suppressed systems suggest that they may be critical components in the metastatic cascade, at least in this model system. Our data demonstrate that analysis of common changes in genes and proteins in a metastatic-related context greatly decrease the complexity of data analysis, and may serve as a screening tool to identify biological important factors from large scale data. |
