Abstract
Although prostate-specific antigen (PSA) is the best biomarker for predicting prostate cancer, its predictive performance needs to be improved. Results from the Prostate Cancer Prevention Trial revealed the overall performance measured by the areas under curve of the receiver operating characteristic at 0.68. The goal of the present study is to assess the ability of genetic variants as a PSA-independent method to predict prostate cancer risk. We systematically evaluated all prostate cancer risk variants that were identified from genome-wide association studies during the past year in a large population-based prostate cancer case-control study population in Sweden, including 2,893 prostate cancer patients and 1,781 men without prostate cancer. Twelve single nucleotide polymorphisms were independently associated with prostate cancer risk in this Swedish study population. Using a cutoff of any 11 risk alleles or family history, the sensitivity and specificity for predicting prostate cancer were 0.25 and 0.86, respectively. The overall predictive performance of prostate cancer using genetic variants, family history, and age, measured by areas under curve was 0.65 (95% confidence interval, 0.63-0.66), significantly improved over that of family history and age (0.61%; 95% confidence interval, 0.59-0.62; P = 2.3 x 10(-10)). The predictive performance for prostate cancer using genetic variants and family history is similar to that of PSA. The utility of genetic testing, alone and in combination with PSA levels, should be evaluated in large studies such as the European Randomized Study for Prostate Cancer trial and Prostate Cancer Prevention Trial.
MeSH Terms
Aged
Area Under Curve
Biomarkers, Tumor/analysis
Case-Control Studies
Family Health
Genome-Wide Association Study
Humans
Male
Polymorphism, Single Nucleotide
Predictive Value of Tests
Prostate-Specific Antigen/analysis
Prostatic Neoplasms/genetics
ROC Curve
Risk Factors
Sensitivity and Specificity
Chemicals
Biomarkers, Tumor
Prostate-Specific Antigen
Authors & Affiliations
20 authors, click to expand affiliations / ORCID
Zheng S Lilly
Center for Cancer Genomics, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
Sun Jielin
Wiklund Fredrik
Gao Zhengrong
Stattin Pär
Purcell Lina D
Adami Hans-Olov
Hsu Fang-Chi
Zhu Yi
Adolfsson Jan
Johansson Jan-Erik
Turner Aubrey R
Adams Tamara S
Liu Wennuan
Duggan David
Carpten John D
Chang Bao-Li
Isaacs William B
Xu Jianfeng
Grönberg Henrik
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