Gene expression profiles in esophageal adenocarcinoma

Background The incidence of esophageal adenocarcinoma (EAC) has risen dramatically in the last two decades. As with other malignancies, changes in gene expression play a key role in the development and progression of these tumors. Methods Microarray analysis was used to study gene expression of 12,000 genes in EAC specimens. Adenocarcinoma tissue samples (n = 10) and controls of normal stomach (n = 6) and esophageal (n = 7) mucosa were collected fresh, then rapidly frozen in liquid nitrogen. The messenger ribonucleic acid (mRNA) from the samples was isolated, reverse transcribed, and used to generate biotin-labeled mRNA fragments, which were hybridized to Affymetrix U95 gene chips (AME Bioscience, Norway) for analysis. Additional samples analyzed included tissue containing dysplastic Barrettʹs epithelium from three patients, metastatic lymph nodes from two patients with EAC, one squamous carcinoma, and two esophageal cancer cell lines. Samples were segregated into groups with similar patterns of gene expression using clustering algorithms and gene sets that differentiated tumors from normal tissue were generated. Results There were 150 genes that were fourfold up regulated and 183 genes that were fourfold down regulated in the esophageal adenocarcinoma specimens, as compared to normal esophageal mucosa tissue controls. Using paired specimens (n = 5) and the paired t-test (p Value of 0.05) as a filter, only 64 genes were fourfold up regulated and 110 were fourfold down regulated. These groups included cytoskeletal, cell adhesion, tumor suppressor, and signal transduction genes. Hierarchical clustering segregated the samples into the expected divisions. The esophageal cancer cell lines, OE19 and OE33, clustered separately from the EAC specimens. Extremely high gene expression levels of the ERBB2 gene, seen in the microarray analysis of the 2 cell lines, correlated with amplification of the gene determined by Southern blotting. Conclusions Gene expression patterns from a small subset of genes distinguish EAC specimens from normal controls. This technique can rapidly identify genes for targeted chemotherapeutic approaches to cancer treatment.