| Summary: | Thesis (Ph.D.)--Boston University
PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. === Ovarian cancer continues to have a high mortality rate. In spite of significant advances in detection and the development of novel therapies, the death rate has only been reduced marginally over the past 50 years. Glioblastoma multiforme remains one of the most aggressive and deadliest forms of cancer. Like ovarian cancer, advances in treatment methods and detection have not yielded improvements in patient survival.
MicroRNAs, a class of noncoding RNA molecules, have been shown to be differentially expressed in ovarian cancer, glioblastoma multiforme, and a multitude of other cancers. These small RNAs operate via translational repression or degradation of their specific target mRNA(s). This can further lead to modulation of entire pathways. Using multidimensional expression data from The Cancer Genome Atlas project, I have identified microRNA/mRNA pairs which are dysregulated in ovarian cancer compared with normal tissue. My results suggest that the disease process uses specific mechanisms which may be significant for their utility as early detection biomarkers or in the development of microRNA therapies for treating ovarian cancers.
MicroRNAs have been shown to be associated with aggressive or poor prognosis phenotypes. By correlating ovarian cancer patient survival information and microRNA expression data, I stratified the disease by poorer or improved prognosis. For each subclass, a unique microRNA signature exists along with specific microRNAs which most robustly define the subclass. My results suggest the presence of multiple microRNA-based mechanisms responsible for survival differences in ovarian cancer. Furthermore, I hypothesize that certain microRNAs validated in a second dataset comprise the "drivers" of one subclass. Additionally, the strong correlation between patient survival and microRNA expression leads me to speculate about their potential role in regulation of cis-platin-resistant behavior that drives ovarian cancer mortality.
Along with my ovarian cancer findings, I further applied these methods to uncover and experimentally validate microRNA/mRNA dysregulations correlating with survival differences in glioblastoma. Due to the scarcity of known biomarkers, pathways, and drug targets in glioblastoma and ovarian cancer, the dysregulations uncovered represent significant steps towards finding novel mechanism(s) involved in tumorigenesis which can potentially lead to new drug targets for these deadly diseases.
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