BackgroundIn the present study, we determined the gene hypermethylation profiles of normal tissues adjacent to invasive breast carcinomas and investigated whether these are associated with the gene hypermethylation profiles of the corresponding primary breast tumors.MethodsA quantitative methylation-specific PCR assay was used to analyze the DNA methylation status of 6 genes (DAPK, TWIST, HIN-1, RASSF1A, RARβ2 and APC) in 9 normal breast tissue samples from unaffected women and in 56 paired cancerous and normal tissue samples from breast cancer patients.ResultsNormal tissue adjacent to breast cancer displayed statistically significant differences to unrelated normal breast tissues regarding the aberrant methylation of the RASSF1A (P = 0.03), RARβ2 (P = 0.04) and APC (P = 0.04) genes. Although methylation ratios for all genes in normal tissues from cancer patients were significantly lower than in the cancerous tissue from the same patient (P ≤ 0.01), in general, a clear correlation was observed between methylation ratios measured in both tissue types for all genes tested (P < 0.01). When analyzed as a categorical variable, there was a significant concordance between methylation changes in normal tissues and in the corresponding tumor for all genes tested but RASSF1A. Notably, in 73% of patients, at least one gene with an identical methylation change in cancerous and normal breast tissues was observed.ConclusionsHistologically normal breast tissues adjacent to breast tumors frequently exhibit methylation changes in multiple genes. These methylation changes may play a role in the earliest stages of the development of breast neoplasia.
Quantitative methylation profiling in tumor and matched morphologically normal tissues from breast cancer patients
I. Van der Auwera,C. Bovie,C. Svensson,X. Trinh,Ridha Limame,P. V. van Dam,S. V. van Laere,E. van Marck,L. Dirix,P. Vermeulen
Published 2010 in BMC Cancer
ABSTRACT
PUBLICATION RECORD
- Publication year
2010
- Venue
BMC Cancer
- Publication date
2010-03-12
- Fields of study
Biology, Medicine
- Identifiers
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- Source metadata
Semantic Scholar, PubMed
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