Data Mining of Gene Arrays for Biomarkers of Survival in Ovarian Cancer.
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.
John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham NG11 8NS, UK. clare.coveney@ntu.ac.uk.
Clare
C
Coveney
John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham NG11 8NS, UK. david.boocock@ntu.ac.uk.
David J
DJ
Boocock
John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham NG11 8NS, UK. robert.rees@ntu.ac.uk.
Robert C
RC
Rees
Department of Histopathology, Queens Medical Centre, Derby Road, Nottingham, Nottinghamshire NG7 2NH, UK. suha.deen@nuh.nhs.uk.
Suha
S
Deen
John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham NG11 8NS, UK. graham.ball@ntu.ac.uk.
Graham R
GR
Ball
artificial neural networks; biomarkers; meta-analysis; ovarian cancer; survival analysis; transcriptomics