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From the third issue of 2017, Microarrays has changed its name to High-Throughput.

Open AccessArticle
Microarrays 2015, 4(3), 324-338; doi:10.3390/microarrays4030324

Data Mining of Gene Arrays for Biomarkers of Survival in Ovarian Cancer

1
John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham NG11 8NS, UK
2
Department of Histopathology, Queens Medical Centre, Derby Road, Nottingham, Nottinghamshire NG7 2NH, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Stephen J. Walker
Received: 18 June 2015 / Revised: 9 July 2015 / Accepted: 13 July 2015 / Published: 17 July 2015
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Abstract

The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a targeted treatment improving the prognosis for many cases. While hundreds of genes have been associated with ovarian cancer, few have yet been verified by peer research for clinical significance. Here, a meta-analysis approach was applied to two carefully selected gene expression microarray datasets. Artificial neural networks, Cox univariate survival analyses and T-tests identified genes whose expression was consistently and significantly associated with patient survival. The rigor of this experimental design increases confidence in the genes found to be of interest. A list of 56 genes were distilled from a potential 37,000 to be significantly related to survival in both datasets with a FDR of 1.39859 × 10−11, the identities of which both verify genes already implicated with this disease and provide novel genes and pathways to pursue. Further investigation and validation of these may lead to clinical insights and have potential to predict a patient’s response to treatment or be used as a novel target for therapy. View Full-Text
Keywords: ovarian cancer; meta-analysis; artificial neural networks; survival analysis; biomarkers; transcriptomics ovarian cancer; meta-analysis; artificial neural networks; survival analysis; biomarkers; transcriptomics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Coveney, C.; Boocock, D.J.; Rees, R.C.; Deen, S.; Ball, G.R. Data Mining of Gene Arrays for Biomarkers of Survival in Ovarian Cancer. Microarrays 2015, 4, 324-338.

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