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Xenobiotic‐Metabolizing gene polymorphisms and ovarian cancer risk

Overview of attention for article published in Molecular Carcinogenesis, December 2010
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Title
Xenobiotic‐Metabolizing gene polymorphisms and ovarian cancer risk
Published in
Molecular Carcinogenesis, December 2010
DOI 10.1002/mc.20714
Pubmed ID
Authors

Ellen L. Goode, Kristin L. White, Robert A. Vierkant, Catherine M. Phelan, Julie M. Cunningham, Joellen M. Schildkraut, Andrew Berchuck, Melissa C. Larson, Brooke L. Fridley, Janet E. Olson, Penelope M. Webb, Xiaoqing Chen, Jonathan Beesley, Georgia Chenevix‐Trench, Thomas A. Sellers, the Australian Ovarian Cancer Study Group the Ovarian Cancer Association Consortium, the Australian Cancer Study

Abstract

Because selected xenobiotic-metabolizing enzymes process pro-carcinogens that could initiate ovarian carcinogenesis, we hypothesized that single nucleotide polymorphisms (SNPs) in the genes encoding xenobiotic-metabolizing enzymes are associated with risk of ovarian cancer. Cases with invasive epithelial ovarian cancer (N = 1571 including 956 of serous sub-type) and controls (N = 2046) from three studies were genotyped at 11 SNPs in EPHX1, ADH4, ADH1A, NQO2, NAT2, GSTP1, CYP1A1, and NQO1, following an initial SNP screen in a subset of participants. Logistic regression analysis of genotypes obtained via Illumina GoldenGate and Sequenom iPlex technologies revealed the following age- and study-adjusted associations: EPHX1 rs1051740 with increased serous ovarian cancer risk [per-allele odds ratio (OR) 1.17, 95% confidence interval (95% CI) 1.04-1.32, P = 0.01), ADH4 r1042364 with decreased ovarian cancer risk (OR 0.90, 95% CI: 0.81-1.00, P = 0.05), and NQO1 rs291766 with increased ovarian cancer risk (OR 1.11, 95% CI: 1.00-1.23, P = 0.04). These findings are consistent with prior studies implicating these genes in carcinogenesis and suggest that this collection of variants is worthy of follow-up in additional studies.

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Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 3%
United States 1 3%
Saudi Arabia 1 3%
Unknown 28 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 23%
Student > Ph. D. Student 4 13%
Professor > Associate Professor 4 13%
Student > Master 4 13%
Other 2 6%
Other 4 13%
Unknown 6 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 26%
Medicine and Dentistry 5 16%
Biochemistry, Genetics and Molecular Biology 4 13%
Chemistry 4 13%
Computer Science 1 3%
Other 2 6%
Unknown 7 23%