A multigene expression panel for the molecular diagnosis of Barrett's esophagus and Barrett's adenocarcinoma of the esophagus

In order to identify genes or combination of genes that have the power to discriminate between premalignant Barrett's esophagus and Barrett's associated adenocarcinoma, we analysed a panel of 23 genes using quantitative real-time RT-PCR (qRT-PCR, Taqman and bioinformatic tools. The genes chosen were either known to be associated with Barrett's carcinogenesis or were filtered from a previous cDNA microarray study on Barrett's adenocarcinoma. A total of 98 tissues, obtained from 19 patients with Barrett's esophagus (BE group) and 20 patients with Barrett's associated esophageal adenocarcinoma (EA group), were studied. Triplicate analysis for the full 23 gene of interest panel, and analysis of an internal control gene, was performed for all samples, for a total of more than 9016 single PCR reactions. We found distinct classes of gene expression patterns in the different types of tissues. The most informative genes clustered in six different classes and had significantly different expression levels in Barrett's esophagus tissues compared to adenocarcinoma tissues. Linear discriminant analysis (LDA) distinguished four genetically different groups. The normal squamous esophagus tissues from patients with BE or EA were not distinguishable from one another, but Barrett's esophagus tissues could be distinguished from adenocarcinoma tissues. Using the most informative genes, obtained from a logistic regression analysis, we were able to completely distinguish between benign Barrett's and Barrett's adenocarcinomas. This study provides the first non-array parallel mRNA quantitation analysis of a panel of genes in the Barrett's esophagus model of multistage carcinogenesis. Our results suggest that mRNA expression quantitation of a panel of genes can discriminate between premalignant and malignant Barrett's disease. Logistic regression and LDAs can be used to further identify, from the complete panel, gene subsets with the power to make these diagnostic distinctions. Expression analysis of a limited number of highly selected genes may have clinical usefulness for the treatment of patients with this disease.