Meta-analysis of gene expression studies in endometrial cancer identifies gene expression profiles associated with aggressive disease and patient outcome

Tracy A O'Mara; Min Zhao; Amanda B Spurdle

doi:10.1038/srep36677

Back

Meta-analysis of gene expression studies in endometrial cancer identifies gene expression profiles associated with aggressive disease and patient outcome

Journal article

Open access

Peer reviewed

Meta-analysis of gene expression studies in endometrial cancer identifies gene expression profiles associated with aggressive disease and patient outcome

Tracy A O'Mara, Min Zhao and Amanda B Spurdle

Scientific Reports, Vol.6(36677)

2016

DOI: https://doi.org/10.1038/srep36677

Files and links (2)

pdf

PDF - Published Version (Open Access)1.48 MBDownload View

Published VersionPDF - Published Version (Open Access)CC BY V4.0, Open Access

url

https://doi.org/10.1038/srep36677View

Published Version

Abstract

Although endometrioid endometrial cancer (EEC; comprising ~80% of all endometrial cancers diagnosed) is typically associated with favourable patient outcome, a significant portion (~20%) of women with this subtype will relapse. We hypothesised that gene expression predictors of the more aggressive non-endometrioid endometrial cancers (NEEC) could be used to predict EEC patients with poor prognosis. To explore this hypothesis, we performed meta-analysis of 12 gene expression microarray studies followed by validation using RNA-Seq data from The Cancer Genome Atlas (TCGA) and identified 1,253 genes differentially expressed between EEC and NEEC. Analysis found 121 genes were associated with poor outcome among EEC patients. Forward selection likelihood-based modelling identified a 9-gene signature associated with EEC outcome in our discovery RNA-Seq dataset which remained significant after adjustment for clinical covariates, but was not significant in a smaller RNASeq dataset. Our study demonstrates the value of employing meta-analysis to improve the power of gene expression microarray data, and highlight genes and molecular pathways of importance for endometrial cancer therapy.

Details

Title: Meta-analysis of gene expression studies in endometrial cancer identifies gene expression profiles associated with aggressive disease and patient outcome
Authors: Tracy A O'Mara (Author) - QIMR Berghofer Medical Research Institute
Min Zhao (Author) - University of the Sunshine Coast - Faculty of Science, Health, Education and Engineering
Amanda B Spurdle (Author) - QIMR Berghofer Medical Research Institute
Publication details: Scientific Reports, Vol.6(36677); 9
Publisher: Nature Publishing Group
Date published: 2016
DOI: 10.1038/srep36677
ISSN: 2045-2322
Copyright note: Copyright © 2016 The Authors. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Organisation Unit: School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Centre for Bioinnovation
Language: English
Record Identifier: 99451157902621
Output Type: Journal article

Metrics

31 File views/ downloads

294 Record Views

30 Times Cited - Web of Science

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types: Domestic collaboration
Web Of Science research areas: Oncology

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

Source: InCites