SynthesisScientific reports2016
Meta-analysis of gene expression studies in endometrial cancer identifies gene expression profiles associated with aggressive disease and patient outcome.
Synthesis in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Comprehensive Bioinformatic and miRNA-Driven Analysis of the Multimeric Canonical I Kappa B Kinase (IKK) Complex in Uterine Corpus Endometrial Carcinoma (UCEC).Biomolecules · 2026Article
- Gene Expression Profiling of Peripheral Blood and Endometrial Cancer Risk Factors: Systems Epidemiology Approach in the NOWAC Postgenome Cohort Study.Lifestyle genomics · 2026Article
- Article
- A comprehensive review of mEpigenomics · 2024Review
- Inhibition of DHODH Enhances Replication-Associated Genomic Instability and Promotes Sensitivity in Endometrial Cancer.Cancers · 2023Article
- BUB1, BUB1B, CCNA2, and CDCA8, along with miR-524-5p, as clinically relevant biomarkers for the diagnosis and treatment of endometrial carcinoma.BMC cancer · 2023Article
- Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer.Molecular therapy oncolytics · 2021Article
- Transgenic mice overexpressing the LH receptor in the female reproductive system spontaneously develop endometrial tumour masses.Scientific reports · 2021Article
- Identification of a prognostic gene signature of colon cancer using integrated bioinformatics analysis.World journal of surgical oncology · 2021Article
- Identification of Hub Genes Correlated With Poor Prognosis for Patients With Uterine Corpus Endometrial Carcinoma by Integrated Bioinformatics Analysis and Experimental Validation.Frontiers in oncology · 2021Article
- Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools.Oncology reports · 2020Observational
- Integrated analysis of immune-related genes in endometrial carcinoma.Cancer cell international · 2020Article
- Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma.BioMed research international · 2020Article
- GenomeForest: An Ensemble Machine Learning Classifier for Endometriosis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Immune response characterization of endometrial cancer.Oncotarget · 2019Article
- Meta-Analysis of HTLV-1-Infected Patients Identifies CD40LG and GBP2 as Markers of ATLL and HAM/TSP Clinical Status: Two Genes Beat as One.Frontiers in genetics · 2019Article
- Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer.Frontiers in genetics · 2019Article
- Article
- Identification of a six-gene signature with prognostic value for patients with endometrial carcinoma.Cancer medicine · 2018Article
- Establishment of a nine-gene prognostic model for predicting overall survival of patients with endometrial carcinoma.Cancer medicine · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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 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.
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