Evidence map›Paper›PMID 25344116›Full record

ArticleMolecular cancer2014

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

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

Open access · goldAbstract read
In one paragraph

Article in Molecular cancer, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
0.7field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 29 citations in OpenAlex.

  1. Signature of Selection Analysis Reveals Candidate Genes Related to Climate Adaptation and Production Traits in Lao Native Goats.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2026
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  4. Analysis of the Effect of SNAI Family in Breast Cancer and Immune Cell.Frontiers in cell and developmental biology · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 6 institutions in 3 countries.

Stephen F MaddenMolecular Therapeutics for Cancer Ireland, National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland. stephen.madden@dcu.ie.
Colin Clarke
Britta Stordal
Mark S Carey
Russell Broaddus
William M Gallagher
John Crown
Gordon B Mills
Bryan T Hennessy
Dublin City University · IEThe University of Texas MD Anderson Cancer Center · USBeaumont Hospital · IETrinity College Dublin · IEUniversity College Dublin · IEUniversity of British Columbia · CA

Funding

UNIVERSITY OF TEXAS MD ANDERSON SPORE IN OVARIAN CANCERP50CA083639 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BAST, ROBERT C · 1999 to 2015
$33.0M
NCI NIH HHS P50 CA083639
6 · The paper itself

Abstract

backgroundOvarian 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.

methodsWe 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.

resultsTo 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.

Indexed as

AlgorithmsDatabases, GeneticGene Expression Regulation, NeoplasticUser-Computer InterfaceBiomarkers, TumorFemaleGenes, NeoplasmHumansMicroRNAsOvarian NeoplasmsPrognosisRNA, MessengerTranscriptomeBiomarkers, TumorMicroRNAsRNA, Messenger

Identifiers

PMID25344116
PMCPMC4219121
OpenAlexW2106237123

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.