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Title: Biomarker discovery for disease progression and metastasis in prostate cancer : a multi-omic approach
Author: Wagner, Sarah
ISNI:       0000 0004 7971 4830
Awarding Body: Nottingham Trent University
Current Institution: Nottingham Trent University
Date of Award: 2019
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Prostate cancer (PCa) is the most common cancer in men and the third most common cause of cancer-related deaths in Europe, which is primarily due to the development of metastasis, which decreases the 5-year survival rate to 30 %. The development of metastasis is the major cause of death in cancer patients and the process highly implicated in the ability of cancer cells to spread is called epithelial to mesenchymal transition (EMT). The aim of this study was to use inducible in vitro EMT models for the discovery of novel disease associated biomarkers through the use of multi-omics datasets. For this, two PCa cell lines were stimulated with transforming growth factor β (TGF-β), resulting in apparent morphological changes indicating a cellular change in the direction of an increased mesenchymal morphology. Induction of EMT was confirmed using quantitative real-time PCR, immunofluorescence staining and western blot analysis. To improve the understanding of underlying changes and for the discovery of novel biomarkers, proteomic and transcriptomic profiles of both models in their induced and non-induced states were generated. Their subsequent integration highlighted 13 potential biomarkers indicative for the process of EMT in PCa and metastasis development. Out of the 13 core markers, four of these were taken forward and further validated using tissue microarrays and the in silico analysis of publicly available datasets. The generated results have supported the association of all 4 markers with EMT and disease progression, however two markers were identified to be of particular interest (DPYL3 and SDPR). These two markers have shown significant differences between primary PCa and castration-resistant prostate cancer (CRPC) and Gleason scoring. Furthermore, both of them were shown to be predictive for disease-recurrence. Overall, the generated results have highlighted the successful application of an integrated omics approach for the discovery of novel disease-associated biomarkers for PCa progression.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available