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Get Free AccessWhereas accepted models of tumorigenesis exist for genetic lesions, the timing of epigenetic alterations in cancer is not clearly understood. We have analyzed the profile of aberrations in DNA methylation occurring in cells lines and primary tumors of one of the best-characterized mouse carcinogenesis systems, the multistage skin cancer progression model. Initial analysis using high-performance capillary electrophoresis and immunolocalization revealed a loss of genomic 5-methylcytosine associated with the degree of tumor aggressiveness. Paradoxically, this occurs in the context of a growing number of hypermethylated CpG islands of tumor suppressor genes at the most malignant stages of carcinogenesis. We have observed this last phenomenon using two approaches, a candidate gene approach, studying genes with well-known methylation-associated silencing in human tumors, and a mouse cDNA microarray expression analysis after treatment with DNA demethylating drugs. The transition from epithelial to spindle cell morphology is particularly associated with major epigenetic alterations, such as E-cadherin methylation, demethylation of the Snail promoter, and a decrease of the global DNA methylation. Analysis of data obtained from the cDNA microarray strategy led to the identification of new genes that undergo methylation-associated silencing and have growth-inhibitory effects, such as the insulin-like growth factor binding protein-3. Most importantly, all of the above genes were also hypermethylated in human cancer cell lines and primary tumors, underlining the value of the mouse skin carcinogenesis model for the study of aberrant DNA methylation events in cancer cells.
Mario F. Fraga, Michel Herranz, Jesús Espada, Esteban Ballestar, Maria F. Paz, Santiago Ropero, Emel Erkek, Önder Bozdoğan, Héctor Peinado, Alain Niveleau, Jian‐Hua Mao, Alan Balmain, Amparo Cano, Manel Esteller (2023). Data from A Mouse Skin Multistage Carcinogenesis Model Reflects the Aberrant DNA Methylation Patterns of Human Tumors. , DOI: https://doi.org/10.1158/0008-5472.c.6493865.
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Type
Preprint
Year
2023
Authors
14
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1158/0008-5472.c.6493865
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