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Molecular Cancer
Published in: Molecular Cancer 1/2014

Open Access 01-12-2014 | Research

OvMark: a user-friendly system for the identification of prognostic biomarkers in publically available ovarian cancer gene expression datasets

Authors: Stephen F Madden, Colin Clarke, Britta Stordal, Mark S Carey, Russell Broaddus, William M Gallagher, John Crown, Gordon B Mills, Bryan T Hennessy

Published in: Molecular Cancer | Issue 1/2014

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Abstract

Background

Ovarian cancer has the lowest survival rate of all gynaecologic cancers and is characterised by a lack of early symptoms and frequent late stage diagnosis. There is a paucity of robust molecular markers that are independent of and complementary to clinical parameters such as disease stage and tumour grade.

Methods

We have developed a user-friendly, web-based system to evaluate the association of genes/miRNAs with outcome in ovarian cancer. The OvMark algorithm combines data from multiple microarray platforms (including probesets targeting miRNAs) and correlates them with clinical parameters (e.g. tumour grade, stage) and outcomes (disease free survival (DFS), overall survival). In total, OvMark combines 14 datasets from 7 different array platforms measuring the expression of ~17,000 genes and 341 miRNAs across 2,129 ovarian cancer samples.

Results

To demonstrate the utility of the system we confirmed the prognostic ability of 14 genes and 2 miRNAs known to play a role in ovarian cancer. Of these genes, CXCL12 was the most significant predictor of DFS (HR = 1.42, p-value = 2.42x10−6). Surprisingly, those genes found to have the greatest correlation with outcome have not been heavily studied in ovarian cancer, or in some cases in any cancer. For instance, the three genes with the greatest association with survival are SNAI3, VWA3A and DNAH12.

Conclusions/Impact

OvMark is a powerful tool for examining putative gene/miRNA prognostic biomarkers in ovarian cancer (available at http://​glados.​ucd.​ie/​OvMark/​index.​html). The impact of this tool will be in the preliminary assessment of putative biomarkers in ovarian cancer, particularly for research groups with limited bioinformatics facilities.
Appendix
Available only for authorised users
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Metadata
Title
OvMark: a user-friendly system for the identification of prognostic biomarkers in publically available ovarian cancer gene expression datasets
Authors
Stephen F Madden
Colin Clarke
Britta Stordal
Mark S Carey
Russell Broaddus
William M Gallagher
John Crown
Gordon B Mills
Bryan T Hennessy
Publication date
01-12-2014
Publisher
BioMed Central
Published in
Molecular Cancer / Issue 1/2014
Electronic ISSN: 1476-4598
DOI
https://doi.org/10.1186/1476-4598-13-241

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