Prostate-specific antigen (PSA) is a commonly used cancer biomarker for prostate cancer, and is often included as part of routine physical examinations in China.
Serum levels of PSA may be influenced by genetic factors as well as other factors. A genome-wide association study (GWAS) conducted in a European population successfully identified six genetic loci that were significantly associated with PSA level. In this study, we aimed to identify common genetic variants that are associated with serum level of PSA in a Chinese population. We also evaluated the effects of those variants by creating personalized PSA cutoff values. A two-stage GWAS of PSA level was performed among men age 20-69 years and self-reported cancer-free participants that underwent routine physical examinations at several hospitals in Guangxi Province, China. Single nucleotide polymorphisms (SNPs) significantly associated with PSA levels in the first stage of sample (N = 1,999) were confirmed in the second stage of sample (N = 1,496). Multivariate linear regression was used to assess the independent contribution of confirmed SNPs and known covariates, such as age, to the level of PSA. SNPs in three regions were significantly associated with levels of PSA in this two-stage GWAS, and had combined P values between 4.62 × 10(-17) and 6.45 × 10(-37). The three regions are located on 1q32.1 at SLC45A3, 10q11.23 at MSMB, and 19q13.33 at KLK3. The region 1q32.1 at SLC45A3 was identified as a novel locus. Genetic variants contributed significantly more to the variance of PSA level than known covariates such as age. Personalized cutoff values of serum PSA, calculated based on the inheritance of these associated SNPs, differ considerably among individuals. Identification of these genetic markers provides new insight into the molecular mechanisms of PSA. Taking individual variation into account, these genetic variants may improve the performance of PSA to predict prostate cancer.
Written by:
Sun J, Tao S, Gao Y, Peng T, Tan A, Zhang H, Yang X, Qin X, Hu Y, Feng J, Kim ST, Lin X, Wu Y, Zhang J, Li Z, Li L, Mo L, Liang Z, Shi D, Huang Z, Huang X, Liu M, Liu Q, Zhang S, Lilly Zheng S, Xu J, Mo Z. Are you the author?
Center for Cancer Genomics, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Reference: Hum Genet. 2012 Dec 27. (Epub ahead of print)
PubMed Abstract
PMID: 23269536
