Gene expression profiling reveals stromal genes expressed in common between Barrett's esophagus and adenocarcinoma |
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Authors: | Hao Ying Triadafilopoulos George Sahbaie Peyman Young Harvey S Omary M Bishr Lowe Anson W |
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Affiliation: | Department of Medicine, Stanford University, Stanford, California 94305-5187, USA. |
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Abstract: | BACKGROUND & AIMS: Barrett's esophagus is a precursor of esophageal adenocarcinoma. DNA microarrays that enable a genome-wide assessment of gene expression enhance the identification of specific genes as well as gene expression patterns that are expressed by Barrett's esophagus and adenocarcinoma compared with normal tissues. Barrett's esophagus length has also been identified as a risk factor for progression to adenocarcinoma, but whether there are intrinsic biological differences between short-segment and long-segment Barrett's esophagus can be explored with microarrays. METHODS: Gene expression profiles for endoscopically obtained biopsy specimens of Barrett's esophagus or esophageal adenocarcinoma and associated normal esophagus and duodenum were identified for 17 patients using DNA microarrays. Unsupervised and supervised approaches for data analysis defined similarities and differences between the tissues as well as correlations with clinical phenotypes. RESULTS: Each tissue displays a unique expression profile that distinguishes it from others. Barrett's esophagus and esophageal adenocarcinoma express a unique set of stromal genes that is distinct from normal tissues but similar to other cancers. Adenocarcinoma also showed lower and higher expression for many genes compared with Barrett's esophagus. No difference in gene expression was found between short-segment and long-segment Barrett's esophagus. CONCLUSIONS: The genome-wide assessment provided by current DNA microarrays reveals many candidate genes and patterns not previously identified. Stromal gene expression in Barrett's esophagus and adenocarcinoma is similar, indicating that these changes precede malignant transformation. |
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Keywords: | PAM, Prediction Analysis of Microarrays software PCR, polymerase chain reaction SAM, Significance Analysis of Microarrays software |
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