Association between differential gene expression and body mass index among endometrial cancers from The Cancer Genome Atlas Project

Abstract

Objective The Cancer Genome Atlas (TCGA) identified four integrated clusters for endometrial cancer (EC): POLE, MSI, CNL and CNH. We evaluated differences in gene expression profiles of obese and non-obese women with EC and examined the association of body mass index (BMI) within the clusters identified in TCGA. Methods TCGA RNAseq data was used to identify genes related to increasing BMI among ECs. The POLE, MSI and CNL clusters were composed mostly of endometrioid EC. Patient BMI was compared between these three clusters with one-way ANOVA. Association between gene expression and BMI was also assessed while adjusting for confounding effects of potential confounding factors. p-values testing the association between gene expression and BMI were adjusted for multiple hypothesis testing over the 20,531 genes considered. Results Mean BMI was statistically different between the ECs in the CNL (35.8) versus POLE (29.8) cluster (p=0.006) and approached significance for the MSI (33.0) versus CNL (35.8) cluster (p=0.05). 181 genes were significantly up- or down-regulated with increasing BMI in endometrioid EC (q-value<0.01), including LPL, IRS-1, IGFBP4, IGFBP7 and the progesterone receptor. DAVID functional annotation analysis revealed significant enrichment in "cell cycle" (adjusted p-value=1.5E-5) and "DNA metabolic processes" (adjusted p-value=1E-3) for the identified genes. Conclusions Obesity related genes were found to be upregulated with increasing BMI among endometrioid ECs. Patients with POLE tumors have the lowest median BMI when compared to MSI and CNL. Given the heterogeneity amongst endometrioid EC, consideration should be giving to abandoning the Type I and II classification of EC tumors.

Gene expression profiling of The Cancer Genome Atlas supports an inverse association between body mass index (BMI) and major oesophageal tumour subtypes

“…Interrogating a total of 40,669 non-coding and 19,814 protein-coding genes in 302 obese and 213 non-obese TCGA-UCEC patients we identified four protein-coding genes and two non-coding genes with significant associations between expression and BMI. Two protein-coding genes identified are critical regulators of hormone-dependent tumour growth and also identified in the previous study [15]: growth regulating oestrogen receptor binding 1 ( GREB1 ) and progesterone receptor ( PGR ) were both positively associated (Spearman r =0.30, Q =5.71 × 10 -7 and r =0.30, Q =1.01 × 10 -6 , respectively) with body mass index (Table 2). A very recent study on an independent clinical data set revealed that PGR expression is indeed correlated with BMI in endometrial cancer [16, 17].…”

“…Indeed, such efforts are likely to reveal further BMI-associated cancer subtypes. Nevertheless, when distinct subtypes associated with overweight and obesity are evident, we and others [15] have shown that correlating gene expression and BMI alone across an entire data set is informative. We propose that an obesogenic environment is essential for the development of oesophageal adenocarcinoma (EAC), but not oesophageal squamous cell carcinoma (ESCC) – adding further weight to distinct prevention and treatment strategies for these histological subtypes [9].…”

New classification of endometrial cancers: the development and potential applications of genomic-based classification in research and clinical care

“…Westin et al stratified cases by body mass index (BMI) revealed improved progression free survival in obese (BMI >30) women with endometrioid endometrial carcinoma suggesting an interaction between metabolic state and genetics [106]. Subsequently, a constellation of ‘obesity related’ genes are observed to be upregulated with increasing BMI among endometrioid carcinomas in the TCGA cohort [107], and different targets for treatment were suggested in obese vs non-obese individuals [108]. Given the global epidemic of obesity and associated ‘metabolic syndrome’, this clinical context is essential to know in guiding clinical management and in research/interpretation of data.…”

Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI

“…Recently, gene expression profiling analysis has yielded insights into the measurement of alterations in genetic expression patterns, and has facilitated the identification of differentially expressed genes (DEGs) being crucial to obesity. In a study conducted by Roque DR, et al, obesity related genes, such as LPL, IRS-1, IGFBP4, and IGFBP7 , etc., were found to be upregulated with increasing BMI among endometrial cancer patients [ 12 ]. The study of Gruchala-Niedoszytko, M, et al also found a series of genes ( PI3, LOC100008589, RPS6KA3, LOC441763, IFIT1, and LOC100133565 ) with a different expression that may be related to an increased BMI [ 13 ].…”