Molecular clustering of endometrial carcinoma based on estrogen-induced gene expression

Abstract

Identification of biomarkers potentially provides prognostic information that can

help guide clinical decision-making.  Given the relationship between estrogen

exposure and endometrial cancer, especially low grade endometrioid carcinoma,

we hypothesized that high expression of genes induced by estrogen would

identify low risk endometrioid endometrial cancers.  cDNA microarray and qRT-

PCR verification were used to identify six genes that are highly induced by

estrogen in the endometrium.  These estrogen-induced biomarkers were

quantified in 72 endometrial carcinomas by qRT-PCR.  Unsupervised cluster

analysis was performed, with expression data correlated to tumor characteristics. 

Time to recurrence by cluster was analyzed using the Kaplan-Meier method.  A

receiver operating characteristic (ROC) curve was generated to determine the

potential clinical utility of the biomarker panel to predict prognosis.  Expression of

all genes was higher in endometrioid carcinomas compared to non-endometrioid

carcinomas.  Unsupervised cluster analysis revealed two distinct groups based

on gene expression.  The high expression cluster was characterized by lower

age, higher BMI, and low grade endometrioid histology.   The low expression

cluster had a recurrence rate 4.35 times higher than the high expression cluster. 

ROC analysis allowed for the prediction of stage and grade with a false negative

rate of 4.8% based on level of gene expression in endometrioid tumors.  We

have therefore identified a panel of estrogen-induced genes that have potential

utility in predicting endometrial cancer stage and recurrence risk.  This proof-of-

concept study demonstrates that biomarker analysis may play a role in clinical

decision making for the therapy of women with endometrial cancer.