alexa Characterization Of MicroRNA Expression In Precursor Lesions And Ovarian Cancer Stem Cells
ISSN: 1948-5956

Journal of Cancer Science & Therapy
Open Access

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10th International Conference on CANCER STEM CELLS AND ONCOLOGY RESEARCH
June 26-28, 2017 London, UK

Sunita R Setlur
Brigham and Women’s Hospital, Massachusetts
Posters & Accepted Abstracts: J Cancer Sci Ther
DOI: 10.4172/1948-5956-C1-103
Ovarian cancer accounts for majority of deaths seen among gynecological cancers. The advanced stage of disease at diagnosis is the primary factor underlying the increased rate of mortality. Lack of both specific symptoms and effective detection strategies make early diagnosis challenge. Therefore development of improved diagnostic methods is required to reduce disease mortality. One of the molecular changes that occur during cancer progression includes alterations in the expression patterns of microRNAs (miRNA). miRNAs belong to the class of small noncoding RNAs that are involved in gene regulation. miRNAs have emerged as promising targets for early detection owing to their stability in serum and plasma samples. Recent studies have demonstrated that a large proportion of ovarian cancers belonging to the common high-grade serous subtype may emanate from the distal fallopian tube rather than the ovarian surface epithelial cells as previously thought. We utilized a murine model of BRCA-mutated ovarian cancer originating in the fallopian tube epithelial cells that accurately recapitulates the histopathology of precursor serous tubal intraepithelial carcinoma (STIC) lesions, genomic landscape and clinical behavior of human disease to systematically identify miRNAs that are associated with ovarian cancer progression. Further, cancer stem-like cells isolated from cell lines established from these murine models were analyzed for miRNA expression. We identified miRNAs that were expressed in precursor lesions that showed a correlated expression in both cancer stem-like cells and serum samples. These microRNAs offer a new avenue towards developing strategies for early diagnosis of ovarian cancer.

Email: [email protected]

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