Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599479
Title: Statistical analysis of microRNA expression and related data
Author: Goldstein, L. D.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2010
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Abstract:
The first part of this thesis is concerned with the analysis of miRNA expression data obtained by bead-based flow cytometric profiling. Based on data obtained from 93 human breast cancer samples, we assess the association of individual miRNAs with clinical factors and molecular tumour subtype. We investigate potential mechanisms of miRNA deregulation by analysing matched data on DNA copy number and mRNA expression. We describe an analysis of miRNA and mRNA expression during normal postnatal mouse mammary gland development, a model system for the study of human breast cancer. In the second part of this thesis we are concerned with the analysis of mRNA expression data with a focus on miRNA targets. We develop a statistical method to assess whether predicted miRNA targets show expression levels that are different compared to those of suitably chosen control genes. We find that, across human breast cancers and during mouse mammary gland development, the targets of most miRNAs do not show systematic changes in their expression. In cases where targets are differentially expressed, changes in expression are mostly consistent with miRNA-mediated regulation. We characterize the molecular function of the miRNA miR-124 in the nematode Caenorhabditis elegans. Many targets of miR-124 are coexpressed with and actively repressed by miR-124. Reduced expression levels of miR-124 targets in cells that express the miRNA compared to the rest of the animal are mostly due to direct miRNA-mediated repression in the case of evolutionary conserved targets and due to both direct repression and other regulatory mechanisms in the case of nonconserved targets.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.599479  DOI: Not available
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