Background & Aims: Aberrant DNA methylation is an early and frequent process in gastric carcinogenesis and could be useful for detection of gastric neoplasia. We hypothesized that methylation analysis of DNA recovered from gastric washes could be used to detect gastric cancer. Methods: We studied 51 candidate genes in 7 gastric cancer cell lines and 24 samples (training set) and identified 6 for further studies. We examined the methylation status of these genes in a test set consisting of 131 gastric neoplasias at various stages. Finally, we validated the 6 candidate genes in a different population of 40 primary gastric cancer samples and 113 nonneoplastic gastric mucosa samples. Results: Six genes (MINT25, RORA, GDNF, ADAM23, PRDM5, MLF1) showed frequent differential methylation between gastric cancer and normal mucosa in the training, test, and validation sets. GDNF and MINT25 were most sensitive molecular markers of early stage gastric cancer, whereas PRDM5 and MLF1 were markers of a field defect. There was a close correlation (r = 0.5-0.9, P = .03-.001) between methylation levels in tumor biopsy and gastric washes. MINT25 methylation had the best sensitivity (90%), specificity (96%), and area under the receiver operating characteristic curve (0.961) in terms of tumor detection in gastric washes. Conclusions: These findings suggest MINT25 is a sensitive and specific marker for screening in gastric cancer. Additionally, we have developed a new method for gastric cancer detection by DNA methylation in gastric washes.
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Funding This work was supported in part by the National Institutes of Health grants CA098006 and CA105346. J.–P.J.I. is an American Cancer Society Clinical Research Professor supported by a generous gift from the F.M. Kirby Foundation. H.–S.K. was supported by grant 2006-070-C00031 from the Korea Research Foundation and an intramural grant-in-aid from Yonsei University Wonju College of Medicine (2006). DNA sequencing in the Core Sequencing facility at the M.D. Anderson Cancer Center is supported by Core Grant CA16672 from the National Institutes of Health.
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