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MicroRNA & Breast Cancer: Functional Characterization in a Population-Based Study

Jia Chen

2 Collaborator(s)

Funding source

National Cancer Institute (NIH)
Despite considerable advancement in our knowledge about the significant role of microRNA (miRNA) in fundamental cellular processes related to cancer, most data come from in vitro/animal models or population studies with limited scope. There is a critical need for systematic investigation on the role of miRNA in breast cancer in well-designed population studies that take into account the complexity of miRNA functions and multifactorial nature of breast cancer. Herein, we have designed a hypothesis-driven, technology-enabled translational project aimed at systematically elucidating the role of miRNA in breast cancer survival. Taking advantage of the latest results from the Cancer Genome Atlas (TCGA) and our improved deep sequencing method, we will validate/identify breast cancer dysregulated miRNAs in breast tumors of representative clinical subtypes. We will employ multiple in vitro assays to characterize these miRNAs for their potential in cell growth/proliferation, differentiation, and cancer cell stemness, as well as to identify their down-stream targets in breast cell lines. We will then independently validate the laboratory findings in a population-based epidemiologic study, the Long Island Breast Cancer Study Project (LIBCSP). The unique strength of the proposed study is our ability to incorporate latest genomic technology (deep sequencing), functional molecular methods (in vitro assays), in which phenotypic relevance of candidate miRNAs can be characterized, with classic epidemiology (population-based study), in which complex exogenous factors and patient information can be taken into account. This multi-scale, multi-disciplinary, and multi-directional approach allows a better characterization of the causal relationship between miRNA and breast cancer development and progression. This project has the real potential to identify a miRNA signature that predicts breast cancer outcomes.

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