title: OvMark: a user-friendly system for the identification of prognostic biomarkers in publically available ovarian cancer gene expression datasets.
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.
  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.
authors.affiliation: Molecular Therapeutics for Cancer Ireland, National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland. stephen.madden@dcu.ie.
authors.forename: Stephen F
authors.initials: SF
authors.lastname: Madden
authors.forename: Colin
authors.initials: C
authors.lastname: Clarke
authors.forename: Britta
authors.initials: B
authors.lastname: Stordal
authors.forename: Mark S
authors.initials: MS
authors.lastname: Carey
authors.forename: Russell
authors.initials: R
authors.lastname: Broaddus
authors.forename: William M
authors.initials: WM
authors.lastname: Gallagher
authors.forename: John
authors.initials: J
authors.lastname: Crown
authors.forename: Gordon B
authors.initials: GB
authors.lastname: Mills
authors.forename: Bryan T
authors.initials: BT
authors.lastname: Hennessy
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics*; D030541:Databases, Genetic; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D052138:Genes, Neoplasm; D006801:Humans; D035683:MicroRNAs / Q000235:genetics / Q000378:metabolism; D010051:Ovarian Neoplasms / Q000235:genetics*; D011379:Prognosis; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D059467:Transcriptome / Q000235:genetics; D014584:User-Computer Interface
chemical_list: D014408:Biomarkers, Tumor; D035683:MicroRNAs; D012333:RNA, Messenger