Meta-analysis of gene expression studies in endometrial cancer identifies gene expression profiles associated with aggressive disease and patient outcome.
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 RNA-Seq 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.
Genetics and Computational Biology Department, QIMR Berghofer Medical Research Institute, Herston, QLD 4006, Australia.
Tracy A
TA
O'Mara
School of Engineering, Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Queensland, 4558, Australia.
Min
M
Zhao
Genetics and Computational Biology Department, QIMR Berghofer Medical Research Institute, Herston, QLD 4006, Australia.
Amanda B
AB
Spurdle
D018269:Carcinoma, Endometrioid / Q000235:genetics / Q000378:metabolism* / Q000628:therapy; D030541:Databases, Genetic; D016889:Endometrial Neoplasms / Q000235:genetics / Q000378:metabolism* / Q000628:therapy; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans