Whole Genome Expression Array Profiling Highlights Differences in Mucosal Defense Genes in Barrett's Esophagus and Esophageal Adenocarcinoma

Abstract

Esophageal adenocarcinoma (EAC) has become a major concern in Western countries due to rapid rises in incidence coupled with very poor survival rates. One of the key risk factors for the development of this cancer is the presence of Barrett's esophagus (BE), which is believed to form in response to repeated gastro-esophageal reflux. In this study we performed comparative, genome-wide expression profiling (using Illumina whole-genome Beadarrays) on total RNA extracted from esophageal biopsy tissues from individuals with EAC, BE (in the absence of EAC) and those with normal squamous epithelium. We combined these data with publically accessible raw data from three similar studies to investigate key gene and ontology differences between these three tissue states. The results support the deduction that BE is a tissue with enhanced glycoprotein synthesis machinery (DPP4, ATP2A3, AGR2) designed to provide strong mucosal defenses aimed at resisting gastro-esophageal reflux. EAC exhibits the enhanced extracellular matrix remodeling (collagens, IGFBP7, PLAU) effects expected in an aggressive form of cancer, as well as evidence of reduced expression of genes associated with mucosal (MUC6, CA2, TFF1) and xenobiotic (AKR1C2, AKR1B10) defenses. When our results are compared to previous whole-genome expression profiling studies keratin, mucin, annexin and trefoil factor gene groups are the most frequently represented differentially expressed gene families. Eleven genes identified here are also represented in at least 3 other profiling studies. We used these genes to discriminate between squamous epithelium, BE and EAC within the two largest cohorts using a support vector machine leave one out cross validation (LOOCV) analysis. While this method was satisfactory for discriminating squamous epithelium and BE, it demonstrates the need for more detailed investigations into profiling changes between BE and EAC.

MicroRNA Expression Signatures during Malignant Progression from Barrett's Esophagus to Esophageal Adenocarcinoma

“…To identify potential genes whose mRNAs might be targeted by the differentially expressed miRNAs in BE and EA, we used a published study of Nancarrow et al (31), in which a list of 54 differentially expressed mRNAs (reported in at least 3 studies) were compiled from 16 esophageal expression profiling studies that compared mRNA expression in NE, BE and EAC tissue groups. For each of the top 20 progressively differentially expressed miRNAs identified from our study (Table 2), putative mRNA targets were predicted by five different algorithms: TargetScan, PicTar, microrna.org, miRanda, and miRGen and compared to the 54 mRNAs.…”

Temporal and Spatial Evolution of Somatic Chromosomal Alterations: A Case-Cohort Study of Barrett's Esophagus

“…Nonprogressor somatic genomes were characterized by low levels of SCA, including deletions at fragile sites most commonly involving FHIT and WWOX , localized CDKN2A hemi- and homozygous deletions, and 9p arm loss and cnLOH, events also frequently detected in BE and many cancers including EA (27, 29, 50)]. These changes appear to have originated in an initial BE expansion that occurred before the baseline endoscopy in which Barrett’s epithelium has a selective advantage over squamous epithelium in the harsh, mutagenic environment of acid and bile reflux (17, 51, 52). Importantly, the somatic genomes of nonprogressors remain remarkably stable across space and time with relative homogeneity of SCA at the level of resolution of 1M SNP arrays.…”

Elevated expression of the IGF2 mRNA binding protein 2 (IGF2BP2/IMP2) is linked to short survival and metastasis in esophageal adenocarcinoma

“…GSE28302 [24] was analyzed for differential gene expression between normal esophageal squamous tissue ( n = 9) and Barret's esophagus without dysplasia ( n = 22). Pearson correlation was applied to detect possible co-expressions between genes of interest and other genes in the dataset.…”