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Title: Evaluation of proteomic and transcriptomic biomarker discovery technologies in ovarian cancer
Author: Coveney, C. R. E.
ISNI:       0000 0004 6352 3482
Awarding Body: Nottingham Trent University
Current Institution: Nottingham Trent University
Date of Award: 2016
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Novel, specific and sensitive biomarkers are prerequisite to improve diagnosis and prognosis of patients with ovarian cancer. Firstly, a proteomic bottom-up MALDI-TOF mass spectrometric profiling analysis was conducted on a cohort of sixty serum samples specifically collected for this purpose. An in-house stepwise Artificial Neural Network (ANN) algorithm generated a biomarker panel of m/z peaks which differentiated cancer from aged matched controls with an accuracy of 91% and error of 9%, identities were inferred where possible and validation conducted using ELISA on the same cohort. Lack of complete verification, or the ability to verify the full panel lead to an in-depth evaluation of the strategy used with the aim to repeat with an improved methodology. Following this, a feasibility analysis and evaluation was performed on the next generation of equipment for sample fractionation prior to analysis on multiple replicates of stock human serum collected in the same way as the ovarian cohort. The results of which combined with the limited amount of available ovarian cancer sample cohort altered the trajectory of the project to the mining of transcriptomic data acquired from an online data repository. A meta-analysis approach was applied to two carefully selected gene expression microarray data sets ANNs, Cox Univariate Survival analyses and T-tests were used to filter genes whose expression were consistently significantly associated with patient survival times. A list of 56 genes were refined from a potential 37000 gene probes to be taken forward for verification for which more freely available online resources such as SRING, Kaplan Meier Plotter and KEGG were utilised. The list of 56 genes of interest were refined to seven using a larger cohort of transcriptomic data, of the seven one, EDNRA, was selected for translational verification using immunohistochemistry of a tissue microarray of ovarian cancer specimens. Significant association is seen with cancer stage, grade and histology. The merits and flaws of the verification are discussed and future work and direction for research is suggested.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available