The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
4-Hydroxylation of estrogens as marker of human mammary tumors.
Open accessAbstract
Estrogen is a known risk factor in human breast cancer. In rodent models, estradiol has been shown to induce tumors in those tissues in which this hormone is predominantly converted to the catechol metabolite 4-hydroxyestradiol by a specific 4-hydroxylase enzyme, whereas tumors fail to develop in organs in which 2-hydroxylation predominates. We have now found that microsomes prepared from human mammary adenocarcinoma and fibroadenoma predominantly catalyze the metabolic 4-hydroxylation of estradiol (ratios of 4-hydroxyestradiol/2-hydroxyestradiol formation in adenocarcinoma and fibroadenoma, 3.8 and 3.7, respectively). In contrast, microsomes from normal tissue obtained either from breast cancer patients or from reduction mammoplasty operations expressed comparable estradiol 2- and 4-hydroxylase activities (corresponding ratios, 1.3 and 0.7, respectively). An elevated ratio of 4-/2-hydroxyestradiol formation in neoplastic mammary tissue may therefore provide a useful marker of benign or malignant breast tumors and may indicate a mechanistic role of 4-hydroxyestradiol in tumor development.
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Cites
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Cited by
17 beta-estradiol hydroxylation catalyzed by human cytochrome P450 1B1.
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Estrogen-induced cardiorenal protection: potential cellular, biochemical and molecular mechanisms.
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Metadata
- Authors
- Liehr, J. G., & Ricci, M. J.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 1996-04-16
- Volume
- 93
- Issue
- 8
- Pages
- 3294-3296
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2002597568
- Cited by
- 414
- References
- 27
- FWCI
- 11.43
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
