Circadian Genes and Risk for Prostate Cancer

Abstract

We hypothesize that genetic susceptibility to prostate cancer may be in part due to variations in the core circadian genes that regulate circadian rhythms and that serum sex steroid hormone levels modify the effect that circadian gene variations have on prostate cancer risk. To test this hypothesis, we genotyped and analyzed 256 SNPs in 10 circadian genes in a study of 1,169 prostate cases and 1,365 controls nested within the Prostate Cancer Prevention Trial (PCPT), a randomized placebo-controlled clinical trial to determine if finasteride (an inhibitor of androgen bioactivation) could prevent prostate cancer. Logistic regression analysis using SNPs in an additive model showed that variants in specific circadian genes are associated with prostate cancer risk and that this risk differed between men who took finasteride versus those who took the placebo. The strongest association was seen for a cluster of 9 SNPs in NPAS2, which was associated with total prostate cancer risk in the finasteride group but not in the placebo group. The most significant NPAS2 SNP was rs746924 (finasteride group OR=1.5, p=9.6x10-5 versus placebo group OR=0.95, p=0.53). In stratified analysis, the same cluster of NPAS2 variants and a second cluster of 9 SNPs on PER3 were associated with low-grade cancers (Gleason sum <7) in the finasteride group but not in the placebo group. Interestingly, risk of high-grade cancers (Gleason sum 7+) in the finasteride group was not related to either the NPAS2 or PER3 clusters of SNPs but was associated with a cluster of 7 SNPs in CRY1. These findings suggest that it may be possible to use genotyping information to identify men who might benefit from chemoprevention by finasteride and other related drugs.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2012
Accession Number
ADA569527

Entities

People

  • Ann Hsing

Organizations

  • Geneva Foundation

Tags

DTIC Thesaurus Topics

  • Androgens
  • Circadian Rhythms
  • Clinical Trials
  • Data Science
  • Diseases And Disorders
  • Drug Therapy
  • Hormones
  • Information Science
  • Neoplasms
  • Prostate
  • Prostate Cancer
  • Regression Analysis
  • Sex Hormones
  • Statistical Analysis
  • Steroids
  • Testosterone
  • United States

Fields of Study

  • Biology

Readers

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Technology Areas

  • Biotechnology