Breast cancer is the most prevalent tumor type among females and has a unique complexity, with considerable heterogeneity. Breast tumors can be classified into molecular subgroups to better target the therapeutic choice. However, even among patients of the same group there is a wide range of prognostic and therapeutic behavior, demonstrating a real need to identify more precise biomarkers. The discovery of new molecular markers for this disease can both contribute to a better understanding of biological mechanisms, as well as to the development of individualized therapies. Currently, many high-throughput projects try to define general patterns of gene expression based on sequencing techniques or "microarrays" from total mRNA. However, this approach provides limited information about the molecular mediators of tumor changes, considering that the level of mRNA expression do not necessarily correspond to the levels of proteins. However, the identification of differentially translated mRNAs in tumors can lead to the discovery of gene expression profiles that better reflect the population of proteins. In this project we intend to identify preferentially translated mRNAs in human breast tumors from the A.C. Camargo Cancer Center Biobank. These results will allow us to define expression profiles that can be correlated with tumor features and also guide the discovery of proteins with altered expression that could be important mediators of tumor processes. This approach has the potential to be a powerful tool applied to the clinic, for the development of therapeutical approaches. (AU)
News published in Agência FAPESP Newsletter about the scholarship:
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
BELLATO, HERMANO MARTINS;
LUPINACCI, FERNANDA C. S.;
VAN HOEF, VINCENT;
ANDRADE, VICTOR P.;
HAJJ, GLAUCIA N. M.;
Polysome-profiling in small tissue samples.
Nucleic Acids Research,
JAN 9 2018.
Web of Science Citations: 5.