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

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.

SUBMITTER: O'Mara TA

PROVIDER: S-EPMC5103206 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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

O'Mara Tracy A TA Zhao Min M Spurdle Amanda B AB

Scientific reports 20161110

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 fo ...[more]

PMID: 27830726

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Project description:BackgroundAggressive behavior is an ancient and conserved trait, habitual for most animals in order to eat, protect themselves, compete for mating and defend their territories. Genetic factors have been shown to play an important role in the development of aggression both in animals and humans, displaying moderate to high heritability estimates. Although such types of behaviors have been studied in different animal models, the molecular architecture of aggressiveness remains poorly understood. This study compared gene expression profiles of 16 prefrontal cortex (PFC) samples from aggressive and non-aggressive cattle breeds: Lidia, selected for agonistic responses, and Wagyu, selected for tameness.ResultsA total of 918 up-regulated and 278 down-regulated differentially expressed genes (DEG) were identified, representing above-chance overlap with genes previously identified in studies of aggression across species, as well as those implicated in recent human evolution. The functional interpretation of the up-regulated genes in the aggressive cohort revealed enrichment of pathways such as Alzheimer disease-presenilin, integrins and the ERK/MAPK signaling cascade, all implicated in the development of abnormal aggressive behaviors and neurophysiological disorders. Moreover, gonadotropins, are up-regulated as natural mechanisms enhancing aggression. Concomitantly, heterotrimeric G-protein pathways, associated with low reactivity mental states, and the GAD2 gene, a repressor of agonistic reactions associated with PFC activity, are down-regulated, promoting the development of the aggressive responses selected for in Lidia cattle. We also identified six upstream regulators, whose functional activity fits with the etiology of abnormal behavioral responses associated with aggression.ConclusionsThese transcriptional correlates of aggression, resulting, at least in part, from controlled artificial selection, can provide valuable insights into the complex architecture that underlies naturally developed agonistic behaviors. This analysis constitutes a first important step towards the identification of the genes and metabolic pathways that promote aggression in cattle and, providing a novel model species to disentangle the mechanisms underlying variability in aggressive behavior.

Dataset Information

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

Publications

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

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