Shelling, APrint, CMuthukaruppan, Anitadevi2011-07-212011http://hdl.handle.net/2292/6997Breast cancer is a leading cause of malignancy worldwide. Improvements to gene expression profiling technology have resulted in the identification of many prognostic and predictive gene expression signatures for breast cancer. Whilst some of these signatures are being developed commercially, only two prognostic signatures: MammaPrint and Oncotype DX, are currently being validated in clinical trials. Many of these gene expression signatures require independent validation and the underlying biology behind these signatures remains unclear. The aim of this thesis is to identify key molecular pathways that are relevant in breast cancer using pathway and network analyses of new and existing in vivo and in vitro microarray gene expression data. The oestrogen signalling pathway was the main focus of this thesis due to its documented importance in the pathogenesis of breast cancer. Analyses of gene expression differences in New Zealand breast tumours according to oestrogen receptor (ER) status revealed differentially regulated genes such as ESR1, GATA3 and EGFR, which have also been reported in other breast cancer microarray studies. The analyses of a collaboratively assembled 960-tumour dataset of clinical breast cancer microarray data revealed differentially regulated pathways involving BCL2, ESR1, EGFR, MYC and NFKB between ER positive and negative tumours. We also identified a principal component of oestrogen activity using the gene expression data from our collated 960-tumour dataset, that could be used alongside ESR1 mRNA and ER protein expression (from immunohistochemistry) to stratify breast cancer patients more accurately. The generation of an in vitro siRNA perturbation dataset using MCF7 breast cancer cells, and its analyses using gene networks has identified relationships between genes that appear to operate both in vitro and in vivo. There were more highly correlated gene pairs shared between the MCF7 dataset and luminal A tumours than between this dataset and other tumour subtypes. The identification of key molecular pathways and master regulators operating in breast tumours from gene expression data may improve our understanding of the biology behind breast cancer. This knowledge can be used in the future to help integrate gene expression data with clinicohistopathological data to improve diagnostic and therapeutic decision-making for patients.Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher.https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htmhttp://creativecommons.org/licenses/by-nc-nd/3.0/nz/ThesisCopyright: The authorQ112887339