BACKGROUND: Although case-control studies have identified numerous single nucleotide polymorphisms (SNPs) associated with prostate cancer, the clinical role of these SNPs remains unclear. OBJECTIVE: Evaluate previously identified SNPs for association with prostate cancer and accuracy in predicting prostate cancer in a large prospective population-based cohort of unscreened men. DESIGN, SETTING, AND PARTICIPANTS: This study used a nested case-control design based on the Malmö Diet and Cancer cohort with 943 men diagnosed with prostate cancer and 2829 matched controls. Blood samples were collected between 1991 and 1996, and follow-up lasted through 2005. MEASUREMENTS: We genotyped 50 SNPs, analyzed prostate-specific antigen (PSA) in blood from baseline, and tested for association with prostate cancer using the Cochran-Mantel-Haenszel test. We further developed a predictive model using SNPs nominally significant in univariate analysis and determined its accuracy to predict prostate cancer. RESULTS AND LIMITATIONS: Eighteen SNPs at 10 independent loci were associated with prostate cancer. Four independent SNPs at four independent loci remained significant after multiple test correction (p<0.001). Seven SNPs at five independent loci were associated with advanced prostate cancer defined as clinical stage≥T3 or evidence of metastasis at diagnosis. Four independent SNPs were associated with advanced or aggressive cancer defined as stage≥T3, metastasis, Gleason score≥8, or World Health Organization grade 3 at diagnosis. Prostate cancer risk prediction with SNPs alone was less accurate than with PSA at baseline (area under the curve of 0.57 vs 0.79), with no benefit from combining SNPs with PSA. This study is limited by our reliance on clinical diagnosis of prostate cancer; there are likely undiagnosed cases among our control group. CONCLUSIONS: Only a few previously reported SNPs were associated with prostate cancer risk in the large prospective Diet and Cancer cohort in Malmö, Sweden. SNPs were less useful in predicting prostate cancer risk than PSA at baseline.
BACKGROUND: Although case-control studies have identified numerous single nucleotide polymorphisms (SNPs) associated with prostate cancer, the clinical role of these SNPs remains unclear. OBJECTIVE: Evaluate previously identified SNPs for association with prostate cancer and accuracy in predicting prostate cancer in a large prospective population-based cohort of unscreened men. DESIGN, SETTING, AND PARTICIPANTS: This study used a nested case-control design based on the Malmö Diet and Cancer cohort with 943 men diagnosed with prostate cancer and 2829 matched controls. Blood samples were collected between 1991 and 1996, and follow-up lasted through 2005. MEASUREMENTS: We genotyped 50 SNPs, analyzed prostate-specific antigen (PSA) in blood from baseline, and tested for association with prostate cancer using the Cochran-Mantel-Haenszel test. We further developed a predictive model using SNPs nominally significant in univariate analysis and determined its accuracy to predict prostate cancer. RESULTS AND LIMITATIONS: Eighteen SNPs at 10 independent loci were associated with prostate cancer. Four independent SNPs at four independent loci remained significant after multiple test correction (p<0.001). Seven SNPs at five independent loci were associated with advanced prostate cancer defined as clinical stage≥T3 or evidence of metastasis at diagnosis. Four independent SNPs were associated with advanced or aggressive cancer defined as stage≥T3, metastasis, Gleason score≥8, or World Health Organization grade 3 at diagnosis. Prostate cancer risk prediction with SNPs alone was less accurate than with PSA at baseline (area under the curve of 0.57 vs 0.79), with no benefit from combining SNPs with PSA. This study is limited by our reliance on clinical diagnosis of prostate cancer; there are likely undiagnosed cases among our control group. CONCLUSIONS: Only a few previously reported SNPs were associated with prostate cancer risk in the large prospective Diet and Cancer cohort in Malmö, Sweden. SNPs were less useful in predicting prostate cancer risk than PSA at baseline.
Authors: S Lilly Zheng; Jielin Sun; Fredrik Wiklund; Shelly Smith; Pär Stattin; Ge Li; Hans-Olov Adami; Fang-Chi Hsu; Yi Zhu; Katarina Bälter; A Karim Kader; Aubrey R Turner; Wennuan Liu; Eugene R Bleecker; Deborah A Meyers; David Duggan; John D Carpten; Bao-Li Chang; William B Isaacs; Jianfeng Xu; Henrik Grönberg Journal: N Engl J Med Date: 2008-01-16 Impact factor: 91.245
Authors: Claudia A Salinas; Erika Kwon; Christopher S Carlson; Joseph S Koopmeiners; Ziding Feng; Danielle M Karyadi; Elaine A Ostrander; Janet L Stanford Journal: Cancer Epidemiol Biomarkers Prev Date: 2008-05 Impact factor: 4.254
Authors: J Manjer; S Carlsson; S Elmståhl; B Gullberg; L Janzon; M Lindström; I Mattisson; G Berglund Journal: Eur J Cancer Prev Date: 2001-12 Impact factor: 2.497
Authors: Gianluca Severi; Vanessa M Hayes; Petra Neufing; Emma J D Padilla; Wayne D Tilley; Sarah A Eggleton; Howard A Morris; Dallas R English; Melissa C Southey; John L Hopper; Robert L Sutherland; Peter Boyle; Graham G Giles Journal: Cancer Epidemiol Biomarkers Prev Date: 2006-06 Impact factor: 4.254
Authors: S Lilly Zheng; Jielin Sun; Yu Cheng; Ge Li; Fang-Chi Hsu; Yi Zhu; Bao-Li Chang; Wennuan Liu; Jin Woo Kim; Aubrey R Turner; Marta Gielzak; Guifang Yan; Sarah D Isaacs; Kathleen E Wiley; Jurga Sauvageot; Huann-Sheng Chen; Robin Gurganus; Leslie A Mangold; Bruce J Trock; Henrik Gronberg; David Duggan; John D Carpten; Alan W Partin; Patrick C Walsh; Jianfeng Xu; William B Isaacs Journal: J Natl Cancer Inst Date: 2007-10-09 Impact factor: 13.506
Authors: Jielin Sun; Siqun Lilly Zheng; Fredrik Wiklund; Sarah D Isaacs; Lina D Purcell; Zhengrong Gao; Fang-Chi Hsu; Seong-Tae Kim; Wennuan Liu; Yi Zhu; Pär Stattin; Hans-Olov Adami; Kathleen E Wiley; Latchezar Dimitrov; Jishan Sun; Tao Li; Aubrey R Turner; Tamara S Adams; Jan Adolfsson; Jan-Erik Johansson; James Lowey; Bruce J Trock; Alan W Partin; Patrick C Walsh; Jeffrey M Trent; David Duggan; John Carpten; Bao-Li Chang; Henrik Grönberg; William B Isaacs; Jianfeng Xu Journal: Nat Genet Date: 2008-08-31 Impact factor: 38.330
Authors: Ian M Thompson; Donna K Pauler; Phyllis J Goodman; Catherine M Tangen; M Scott Lucia; Howard L Parnes; Lori M Minasian; Leslie G Ford; Scott M Lippman; E David Crawford; John J Crowley; Charles A Coltman Journal: N Engl J Med Date: 2004-05-27 Impact factor: 91.245
Authors: Weiqiang Li; Mesude Bicak; Daniel D Sjoberg; Emily Vertosick; Anders Dahlin; Olle Melander; David Ulmert; Hans Lilja; Robert J Klein Journal: Prostate Date: 2020-09-11 Impact factor: 4.104