
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39227454
71544
10.1038/s41598-024-71544-7
Article
Predictive value of polygenic risk score for prostate cancer incidence and prognosis in the Han Chinese
Hung Sheng-Chun 123
Chang Li-Wen 123
Hsiao Tzu-Hung 456
Wei Chia-Yi 4
Wang Shian-Shiang 137
Li Jian-Ri 1238
Chen I-Chieh icchen@vghtc.gov.tw

4
1 https://ror.org/00e87hq62 grid.410764.0 0000 0004 0573 0731 Department of Urology, Taichung Veterans General Hospital, Taichung, Taiwan
2 grid.260542.7 0000 0004 0532 3749 Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
3 https://ror.org/059ryjv25 grid.411641.7 0000 0004 0532 2041 Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
4 https://ror.org/00e87hq62 grid.410764.0 0000 0004 0573 0731 Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
5 https://ror.org/04je98850 grid.256105.5 0000 0004 1937 1063 Department of Public Health, Fu Jen Catholic University, New Taipei City, Taiwan
6 grid.260542.7 0000 0004 0532 3749 Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan
7 https://ror.org/03ha6v181 grid.412044.7 0000 0001 0511 9228 Department of Applied Chemistry, National Chi Nan University, Nantou, Taiwan
8 https://ror.org/02f2vsx71 grid.411432.1 0000 0004 1770 3722 Department of Medicine and Nursing, Hungkuang University, Taichung, Taiwan
3 9 2024
3 9 2024
2024
14 2045319 9 2023
28 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Although prostate cancer is a common occurrence among males, the relationship between existing risk prediction models remains unclear. The objective of this hospital-based retrospective study is to investigate the impact of polygenic risk scores (PRSs) on the incidence and prognosis of prostate cancer in the Han Chinese population. A total of 24,778 male participants including 903 patients with prostate cancer at Taichung Veterans General Hospital were enrolled in the study. PRS was calculated using 269 single nucleotide polymorphisms and their corresponding effect sizes from the polygenic score catalog. The association between PRS and the risk prostate cancer was evaluated using Cox proportional hazards regression model. Among the 24,778 participants, 903 were diagnosed with prostate cancer. The risk of prostate cancer was significantly higher in the highest quartile of PRS distribution compared to the lowest (hazard ratio = 4.770, 95% CI = 3.999–5.689, p < 0.0001), with statistical significance across all age groups. Patients in the highest quartile were diagnosed with prostate cancer at a younger age (66.8 ± 8.3 vs. 69.5 ± 8.8, p = 0.002). Subgroup analysis of patients with localized or stage 4 prostate cancer showed no significant differences in biochemical failure or overall survival. This hospital-based cohort study observed that a higher PRS was associated with increased susceptibility to prostate cancer and younger age of diagnosis. However, PRS was not found to be a significant predictor of disease stage and prognosis. These findings suggest that PRS could serve as a useful tool in prostate cancer risk assessment.

Keywords

Prostate cancer
Polygenic risk score
Incidence
Prognosis
Subject terms

Cancer
Cancer genomics
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Prostate cancer is the most prevalent cancer among men worldwide and ranks as the second leading cause of cancer-related mortality in men1. In Taiwan, there were 7,137 newly diagnosed cases of prostate cancer and 1538 deaths due to prostate cancer in 20192. Although the exact etiology of prostate cancer remains unclear, studies have indicated that genetic and hereditary factors play a crucial role in disease development3,4. Genome-wide association studies (GWAS) have identified over 100 susceptibility loci for prostate cancer, which collectively account for 28.4% of the familial relative risk of prostate cancer5.

Previous studies have identified several common genetic polymorphisms that are associated with the risk of developing prostate cancer, but the relative risk conferred by each loci remains low to modest6. Consequently, the concept of the polygenic risk score (PRS) has been introduced. PRS is a composite score that incorporates the presence or absence of multiple genetic variants associated with prostate cancer. By weighting the influence of each single nucleotide polymorphism (SNP) and summing these SNPs into a PRS, researchers can assess the cumulative contribution of these variants to disease prevalence and incidence in population7. For instance, men in the ninetieth to ninety-ninth percentiles of PRS would have a 2.69-fold increased risk of developing prostate cancer compared to the population average, and this risk would further increase to 5.71-fold for men in the first percentile8. Currently, the predictive value for the area under the curve (AUC) in prostate cancer using PRS alone ranges from 0.56 to 0.67. When combined with established clinical markers, the AUC increases significantly to a range of 0.886–0.8809. PRS has been found to be a significant predictor of prostate cancer risk, with higher scores associated with an increased risk of developing the disease.

The application of PRS has shown variations among different ethnic groups. A meta-analysis of trans-ancestry GWAS studies revealed that the top decile of PRS was associated with a 5.06-fold increased risk of prostate cancer in men of European ancestry, while it was 3.74-fold in men of African ancestry5. Furthermore, the relative risk was 4.47-fold for men of East Asian ancestry in the same cohort. However, most PRS models have been developed based on GWAS data primarily collected from Caucasian and European populations10. Another multi-ethnic cohort study conducted by the PRACTICAL Consortium found that Asian men at the 98th percentile of PRS, compared to those at the 30–70th percentile, had hazard ratios of 3.77 and 4.14 for overall prostate cancer and aggressive prostate cancer development, respectively11. Despite the significant results for the Asian population in this study, only a small proportion of participants were of Asian ethnicity. There have been studies on ancestry-specific PRS for the Asian population as well. For instance, Song et al. developed a PRS using 83 candidate SNPs that showed significant association with prostate cancer, enabling the prediction of clinically significant prostate cancer susceptibility in the Korean male population12. Akamatsu et al. conducted a study using a PRS derived from the genotypes of 16 common variants, along with sequencing of 8 prostate cancer-associated genes. They found that a high PRS had a comparable impact on biopsy positivity to a positive magnetic resonance imaging finding in Japanese patients with prostate-specific antigen (PSA) levels between 2 and 10 ng/mL13. The utilization of ethnicity-specific PRS can be beneficial in predicting prostate cancer incidence within a given population.

Understanding the role of genetics factor, particularly PRS, in prostate cancer risk, clinical outcomes and prognosis has significant clinical implications. Moreover, PRS has been proposed not only as a tool for predicting disease incidence but also for reducing overdiagnosis by identifying potentially lethal prostate cancer14. The aim of our study is to investigate the association between PRS and prostate cancer susceptibility in the Asian population, as well as analyze the impact of genome-wide susceptibility variants on clinical outcomes and prognosis of prostate cancer patients in a Han Chinese population.

Patients and methods

Study population

This hospital-based retrospective cohort study was conducted with the participation of 58,091 Taiwanese individuals aged 20 years or older. The study utilized data from the Taiwan Precision Medicine Initiative (TPMI) project, overseen by Academia Sinica in Taiwan, and took place from June 2019 to May 2021. The study cohort consisted of 903 patients who were identified using the international Codes of Diseases–Ninth Revision–Clinical Modification (ICD-9-CM) code 185, and their genetic profiles were linked to medical claims data from TCVGH. All participants (24,778 males), including 903 prostate cancer patients, were followed for recurrence and mortality until the end of available follow-up period, which extended from January 2009 to January 2022. This comprehensive dataset of TPMI included demographic information, procedures, diagnoses, surgeries, and medication prescriptions. All participants underwent genotyping using the Affymetrix Genome-Wide TWB 2.0 SNP Array. The study was approved by the ethics committee of the TCVGH Institutional Review Board (IRB No. CE23119A), and all participants provided informed consent. All methods were performed in accordance with the relevant guidelines and regulations. Only male participants were included in the analysis. Clinical parameters were obtained from the dataset and electronic medical records from TCVGH using de-identification method.

Genotyping and quality control

This study collected blood samples from all participants to extract DNA and conducted genotyping using the Axiom Genome-Wide TWB 2.0 Array Plate (Affymetrix, Santa Clara, CA, USA) , which contains 714,431 SNPs and is designed specifically for Taiwan’s Han Chinese population15. Analysis and quality control were performed using Affymetrix Power Tools software, and markers that failed Hardy–Weinberg equilibrium tests with a P < 1.0 × 10–5, had a minor allele frequency < 0.05, or had a genotype missing rate of > 5% were excluded. After quality control, a total of 591,048 SNPs were retained for analysis. The use of high coverage GWAS SNP data from large-scale Han Chinese ancestry in Taiwan using custom arrays has been previously described16. The samples with a missingness rate > 0.02, an inbreeding coefficient > 0.15, and those with a sex mismatch were removed. Genotype imputation was carried out across the autosomal chromosomes using the Michigan Imputation Server, which implemented the ‘minimac4’ algorithm17. Strand-aligned genotype data were loaded into the server. We performed the imputation using the 1000 Genomes Phase 3 (Version 5) reference panel18. All biallelic variants with imputation quality threshold of INFO score ≥ 0.3 were reported.

Polygenic risk score analysis

PRS was calculated by using the ‘score’ function from plink version 1.9 to aggregate the effects of multiple genetic variants weighted by their effects size19. The PRS used in this study, PGS000662, was derived from a discovery analysis of 269 variants associated with prostate cancer identified from Trans-ancestry GWAS meta-analyses, including East Asian ancestry case–control analysis5. The list of SNPs and effect sizes were downloaded from the Polygenic score (PGS) catalog20. PGS000662 was normally distributed among both the cases and non-cases groups (Supplementary Fig. 1).

Clinical parameters and outcome evaluation

Patients diagnosed with prostate cancer were identified based on International Classification of Disease, Ninth Revision (ICD-9-CM) code 185, along with pathological proof. The index date was defined as the date of prostate cancer diagnosis, which was determined using the ICD-9-CMcode 185, recorded at least twice during outpatient visits or once during hospitalization between January 2009 and January 2022. All participants with the prostate cancers were incident cases and had not undergone regular follow-up prior to their cancer diagnoses. The study extracted relevant biochemical, lifestyle data and death files from the TCVGH database, and evaluated several covariates including sex, age, comorbidities and smoking. We obtained comorbidity information from the electronic health records of TCVGH based on ICD-9 diagnostic codes for hypertension (ICD-9-CM code 401–405), diabetes mellitus (ICD-9-CM code 250) and hypertension (ICD-9-CM code 401–405) were identified if the diagnostic code was used once during admission or at least twice in the outpatient service. Smoking status was dichotomized into former/current smokers and non-smokers. The incidence of prostate cancer among male participants was analyzed as the first step. Subsequently, the genetic profile was correlated with clinical parameters such as age at diagnosis, PSA levels, clinical stage and Gleason grading. The risk of prostate cancer was categorized based on the D’Amico risk classification21. Outcome evaluations included all-cause mortality and prostate cancer specific mortality.

Statistical analysis

Hazard ratios (HRs) and 95% confidence interval (95% CI) were calculated using Cox proportional hazards regression models, with time since study entry serving as the designated timescale. Outcomes were censored if a participant was lost to follow-up or died, or if the end of available follow-up was reached (November 2022). The demographic data are shown as mean ± standard deviation (SD) for continuous variables. An analysis of variance (ANOVA) for continuous variables, while categorical variables were presented as number (percent) and analyzed using Chi-square test. The PRS was assessed as a categorical variable, categorized into four groups based on quartiles of PRS values, namely Q1 (0–25%), Q2 (26–50%), Q3 (51–75%), and Q4 (76–100%). All statistical analyzes were conducted using SAS version 9.4. (SAS Institute Inc. Cary NC) and IBM SPSS statistical software for Windows, version 22.0 (IBM corp., Armonk, NY, United States).

Results

Among the 57,257 participants from the TPMI project in TCVGH, a total of 24,778 male participants were included in the analysis. Among them, 903 patients diagnosed with prostate cancer were enrolled, and no data was missing for these participants (Fig. 1). Among the patients with prostate cancer, 508 individuals with clinically localized prostate cancer underwent radical prostatectomy, 156 cases of biochemical recurrence and 352 cases without recurrence. Additionally, there were 143 patients had metastatic disease at diagnosis, 416 patients had localized low-intermediate risk prostate cancer, and 344 patients had localized high risk prostate cancer (Fig. 1). Throughout the follow-up period, there were 103 fatalities recorded. Table 1 presents the basic demographic characteristics of the entire cohort. Compared with non-prostate cancer patients, prostate cancer patients exhibit a significantly higher mean age of 75.72 years. The average score of PGS000662 was significantly higher in patients with prostate cancer than in non-prostate cancer patients (− 1.53 vs. − 2.09, p < 0.0001). Additionally, this group demonstrates lower incidences of diabetes mellitus (24.14% vs. 33.45%, p < 0.0001) and hyperlipidemia (23.48% vs. 38.64%, p < 0.0001), yet a higher prevalence of smoking (51.38% vs. 45.02%, p = 0.0002). The mortality analysis reveals that prostate cancer patients have significantly higher all-cause death rates (11.41% vs. 4.55%, p < 0.0001). The patients had a median follow-up duration of 9.63 years (interquartile range [IQR] 5.98–12.52) among those diagnosed with prostate cancer, and 9.4 years (IQR 4.94–12.52) among those without prostate cancer.Fig. 1 Flow chart for enrolled participants in the study. Among the 57,257 participants from the TPMI project in TCVGH, a total of 24,778 male participants were included in the analysis, with 903 patients diagnosed with prostate cancer as cases and controls. Among the patients with prostate cancer, 143 patients had metastatic disease at diagnosis, 416 patients had localized low-intermediate risk prostate cancer, and 344 patients had localized high-risk prostate cancer.

Table 1 Basic demographic characteristics of the entire cohort (N = 24,778).

Variables	Prostate cancer patients (n = 903)	Non-prostate cancer patients (n = 23,875)	P value	
n	%	n	%	
Demography (mean/SD)a	
Age	75.72	8.8	60.83	15.37	 < 0.0001	
Comorbidities (n/%)b	
Hypertension	372	41.2	10,102	42.31	0.5052	
DM	218	24.14	7985	33.45	 < 0.0001	
Hyperlipidemia	212	23.48	9225	38.64	 < 0.0001	
Smoke	464	51.38	10,748	45.02	0.0002	
Polygenic Risk Score (PGS000662) (mean/SD)a	–1.53	0.74	–2.09	0.76	 < 0.0001	
Deathb	
All cause death	103	11.41	1086	4.55	 < 0.0001	
Death of cancer	72	69.9	545	50.18	0.0001	
Follow-up period (Median/IQR)c(years)	9.63	5.98–12.52	9.4	4.94–12.52	0.0002	
DM, diabetes mellitus; IQR, interquartile range.

aContinuous variables were expressed as mean ± standard deviation (SD) and were analyzed using ANOVA follow a normal data distribution.

bCategorical variables were expressed as numbers (percent) and were analyzed using the Chi-square test.

cUsing Kruskal–Wallis test.

The participants were categorized into four groups (Q1–Q4) by quartile of PRS. The characteristics of the four quartiles of PRS among the 903 prostate cancer patients are presented in Table 2. The diagnosis age was found to be significantly younger in Q4 (Q1, 69.5 ± 8.8 years old; Q2, 68.0 ± 7.9 years old; Q3, 69.3 ± 8.1 years old; Q4, 66.8 ± 8.3 years old, p = 0.002). The patients were confirmed as developing prostate cancer with a median follow-up time of 10.28 years (IQR 6.01–12.54) in Q1, 9.25 years (IQR 5.76–12.64) in Q2, 9.92 years (IQR 6.41–12.67) in Q3 and 8.83 years (IQR 5.90–12.05) in Q4, respectively. However, there was no statistically significant association between the four quartiles of PRS and the disease severity, as indicated by PSA level [Q1–Q4: median 13.4, interquartile range (IQR) 6.3–35.6 ng/ml; median 11.8, IQR 72–33.8 ng/ml; median 12.3, IQR 6.7–27.8 ng/ml; median 15.4, IQR 8.0–53.1 ng/ml, p = 0.07], clinical stage (stage I to stage IV, p = 0.71), Gleason grade (grade 1, 2 + 3, 4 + 5, p = 0.23) and Risk classification (low-intermediate, high, metastatic, p = 0.55). A total of 103 patients expired during the follow-up period, and 72 of them expired due to prostate cancer. There was no statistically significant association observed among the four quartiles in terms of all-cause death (p = 0.73) or cancer-specific death (p = 0.29) (Table 2).Table 2 Characteristics of the study subjects (N = 903).

Variables	Quartile of polygenic risk score (PGS000662)	P value	
Q1 (n = 225)	Q2 (n = 226)	Q3 (n = 226)	Q4 (n = 226)	
n	%	n	%	n	%	n	%	
Demography (mean/SD)a	
Diagnosis age	69.5	8.8	68.0	7.9	69.3	8.1	66.8	8.3	0.002	
Biochemistry (Median/IQR)b	
PSA (ng/dL)	13.4	6.3–35.6	11.8	7.2–33.8	12.3	6.7–27.8	15.4	8.0–53.1	0.07	
Comorbidities (n/%)c	
Hypertension	98	43.6	101	44.69	95	42.04	78	34.51	0.12	
DM	62	27.6	52	23.01	53	23.45	51	22.57	0.58	
Hyperlipidemia	57	25.3	49	21.68	60	26.55	46	20.35	0.35	
Smoke	110	48.9	113	50	118	52.21	123	54.42	0.65	
Clinical Stagec	
Stage I	31	14.03	41	18.22	43	19.11	36	16	0.71	
Stage II	118	53.39	107	47.56	111	49.33	102	45.33		
Stage III	27	12.22	30	13.33	24	10.67	35	15.56		
Stage IV	45	20.36	47	20.89	47	20.89	52	23.11		
Gleason gradec	
1	63	28.9	85	38.46	83	37.56	64	29.22	0.23	
2 + 3	85	38.99	76	34.39	78	35.29	84	38.36		
4 + 5	70	32.11	60	27.15	60	27.15	71	32.42		
Risk classificationc	
Low-intermediate	106	47.11	112	49.56	105	46.46	93	41.15	0.55	
High	89	39.56	81	35.84	83	36.73	91	40.27		
Metastatic	30	13.33	33	14.6	38	16.81	42	18.58		
Deathc	
All cause death	30	13.33	26	11.5	24	10.62	23	10.22	0.73	
Death of cancer	23	10.55	20	9.09	17	7.76	12	5.61	0.29	
Follow-up period (Median/IQR)d(years)	10.28	6.01–12.54	9.25	5.76–12.64	9.92	6.41–12.67	8.83	5.90–12.05	0.1581	
IQR, interquartile range; PSA, prostate specific antigen; DM, diabetes mellitus.

aContinuous variables were expressed as mean ± standard deviation (SD) and were analyzed using ANOVA follow a normal data distribution.

bPSA median were analyzed using Kruskal–Wallis test.

cCategorical variables were expressed as numbers (percent) and were analyzed using the Chi-square test.

dUsing Kruskal–Wallis test.

The relationship between PGS000662 and the prostate cancer using Cox proportional hazard re-gression models, adjusted hazard ratios were calculated for prostate cancer risk factors and polygen-ic risk score in relation to the occurrence of first-onset prostate cancer outcomes. As shown in Table 3, the baseline characteristics of participants along with HRs for prostate cancer, adjusted for risk factors. Age significantly elevates prostate cancer risk (HR = 1.070, P < 0.0001), while histories of hypertension and diabetes mellitus are associated with a decreased risk (HRs of 0.897 and 0.665, respectively, both P < 0.0001). Hyperlipidemia exhibits a notable protective effect against prostate cancer (HR = 0.476, P < 0.0001). Conversely, cigarette smoking marginally increases the risk (HR = 1.113, P = 0.0413), and a family history of the disease does not significantly alter risk levels (HR = 0.85, P = 0.1956). PGS000662 was normally distributed (Supplementary Fig. 1), and exhibited an association with prostate cancer, showing HRs of 2.254 (95% CI = 2.108–2.410, p < 0.001) after adjustment for potential confounders.Table 3 Risk of prostate cancer risk factors and polygenic risk score (PGS000662) for incident prostate cancer.

Prostate cancer risk factors	HRs	(95% CI)	P valuea	
Age, years	1.070	1.066–1.075	 < 0.0001	
History of hypertension	0.897	0.808–0.995	0.0399	
History of diabetes mellitus	0.665	0.592–0.747	 < 0.0001	
History of hyperlipidemia	0.476	0.423–0.536	 < 0.0001	
Cigarette smoking	1.113	1.004–1.234	0.0413	
Family history	0.85	0.665–1.087	0.1956	
Polygenic Risk Scores (PRS)				
PGS000662	2.254	2.108–2.410	 < 0.0001	
aHRs were estimated using Cox proportional hazards model, and adjusted for age, smoking status, family history, history of hypertension, history of diabetes, and history of hyperlipidemia, where appropriate.

The development of prostate cancer increased with higher quartiles of PRS, with rates of 2.42%, 3.91%, 6.15% and 11.03% in Q1 to Q4, respectively (p < 0.0001, Supplementary Table 1). The risk of incident prostate cancer was significantly higher in Q4 compared to Q1 (HR = 4.770, 95% CI = 3.999–5.689, p < 0.0001) by using Cox proportional regression analysis, as shown in Table 4. After stratification by age group, the prostate cancer incidence remained higher in Q4 compared to Q1 in different age groups, including age ≤ 50 years old (HR = 9.247, 95% CI = 1.171–72.984, p = 0.0348) and age > 50 years old (HR = 4.755, 95% CI = 3.984–5.675, p < 0.0001), age ≤ 60 years old (HR = 5.417, 95% CI = 3.174–9.246, p < 0.0001) and age > 60 years old (HR = 4.731, 95% CI = 3.925–5.702, p < 0.0001), and age ≤ 70 years old (HR = 5.193, 95% CI = 4.061–6.641, p < 0.0001) and age > 70 years old (HR = 4.485, 95% CI = 3.482–5.778, p < 0.0001) (Table 4).Table 4 Risk for incident prostate cancer in TPMI (N = 24,778) by PRS (PGS000662).

Variables	HR	95% CI	P valuea	
Comparison	
Q2|Q1	1.622	1.323	1.987	 < 0.0001	
Q3|Q1	2.591	2.146	3.129	 < 0.0001	
Q4|Q1	4.770	3.999	5.689	 < 0.0001	
Age (diagnosis age <  = 50)	
Q2|Q1	1.043	0.065	16.667	0.9765	
Q3|Q1	2.950	0.307	28.357	0.3489	
Q4|Q1	9.247	1.171	72.984	0.0348	
Age (diagnosis age > 50)	
Q2|Q1	1.611	1.314	1.970	 < 0.0001	
Q3|Q1	2.623	2.171	3.170	 < 0.0001	
Q4|Q1	4.755	3.984	5.675	 < 0.0001	
Age (diagnosis age <  = 60)	
Q2|Q1	1.897	1.034	3.480	0.0386	
Q3|Q1	2.518	1.413	4.488	0.0017	
Q4|Q1	5.417	3.174	9.246	 < 0.0001	
Age (diagnosis age > 60)	
Q2|Q1	1.590	1.281	1.974	 < 0.0001	
Q3|Q1	2.672	2.189	3.262	 < 0.0001	
Q4|Q1	4.731	3.925	5.702	 < 0.0001	
Age (diagnosis age <  = 70)	
Q2|Q1	1.389	1.034	1.866	0.0291	
Q3|Q1	2.731	2.099	3.553	 < 0.0001	
Q4|Q1	5.193	4.061	6.641	 < 0.0001	
Age (diagnosis age > 70)	
Q2|Q1	1.890	1.426	2.505	 < 0.0001	
Q3|Q1	2.501	1.908	3.278	 < 0.0001	
Q4|Q1	4.485	3.482	5.778	 < 0.0001	
aCox proportional hazard regression models were utilized to calculate the hazard ratio (HR) and 95% confidence interval (95% CI) for the risk of incident prostate cancer.

Then, we utilized both univariate and multivariate Cox proportional hazards models to assess the risk of survival outcomes among study populations. As shown in Table 5, distinguishing between Gleason grades 2 + 3 and 4 + 5, showing a significant increase in risk for higher grades in the univariable model (HR = 3.66 for 4 + 5, p < 0.0001), but not in the multivariable model. The results in Table 5 indicate that there was no statistically significant association between the top quartile (Q4) of PRS compared to the bottom quartile (Q1) of PRS with all-cause death (Q4 vs. Q1, HR = 0.895, 95% CI = 0.519–1.543, p = 0.689) or cancer-specific death (Q4 vs. Q1, HR = 0.585, 95% CI = 0.291–1.176, p = 0.132) with Cox regression univariate analysis. Similarly, Cox regression multivariate analysis revealed a no statistically significant association between all-cause death (Q4 vs. Q1, HR = 0.817, 95% CI = 0.458–1.457, p = 0.493), cancer death (Q4 vs. Q1, HR = 0.473, 95% CI = 0.223–1.001, p = 0.0504) and PRS. Adjusted hazard ratios were calculated for age at baseline, Gleason grade, clinical stage, PRS, and cause of death.Table 5 Univariate and multivariate cox proportional hazards model of risk of survival outcomes among study populations.

Variables	Univariable model	Multivariable model	
HR	(95% CI)	P valuea	HR	(95% CI)	P valuea	
Age	1.033	1.010–1.056	0.0046	1.034	1.010–1.060	0.0058	
Gleason grade	
 1	–	–	–	–	–	–	
 2 + 3	1.141	0.793–2.523	0.2406	0.954	0.477–1.906	0.893	
 4 + 5	3.66	2.159–6.204	 < 0.0001	1.58	0.772–3.234	0.2103	
Clinical Stage							
 Stage I	–	–	–	–	–	–	
 Stage II	1.25	0.593–2.637	0.558	0.817	0.365–1.827	0.6225	
 Stage III	2.418	1.001–5.845	0.0499	1.401	0.511–3.840	0.512	
 Stage IV	7.132	3.501–14.527	 < 0.0001	3.062	1.228–7.632	0.0163	
All-cause death							
 PRS	
  PRS_Q1	–	–	–	–	–	–	
  PRS_Q2	0.945	0.558–1.600	0.8334	0.908	0.516–1.599	0.739	
  PRS_Q3	0.822	0.480–1.407	0.4745	0.853	0.489–1.490	0.5764	
  PRS_Q4	0.895	0.519–1.543	0.6893	0.817	0.458–1.457	0.4931	
Cancer death	
 PRS	
  PRS_Q1	–	–	–	–	–	–	
  PRS_Q2	0.914	0.502–1.665	0.7692	0.795	0.415–1.522	0.4885	
  PRS_Q3	0.738	0.394–1.382	0.3429	0.716	0.374–1.369	0.3122	
  PRS_Q4	0.585	0.291–1.176	0.1321	0.473	0.223–1.001	0.0504	
aHRs were estimated using univariable and multivariable Cox proportional hazards model, and adjusted for age at baseline, Gleason grade, clinical stage, PRS and cause of death.

In a subgroup analysis, we focused on 508 patients with localized prostate cancer who underwent radical prostatectomy (Supplementary Fig. 2.) and compared the demographic characteristics between PRS groups (Table 6). A median follow-up duration of 9.18 years (IQR 5.34–12.39) in Q1, 7.90 years (IQR 5.61–12.02) in Q2, 8.85 years (IQR 5.78–12.41) in Q3 and 8.85 years (IQR 6.24–11.56) in Q4, respectively. There was no significant difference in diagnosis age among the four quartile groups (p = 0.12). Among these patients, 156 patients experienced biochemical failure.Table 6 Characteristics of patients with localized prostate cancer received radical prostatectomy (N = 508).

Variables	Quartile of polygenic risk score (PGS000662)	P value	
Q1 (n = 125)	Q2 (n = 134)	Q3 (n = 120)	Q4 (n = 129)	
n	%	n	%	n	%	n	%	
Demography (mean/SD)a	
Diagnosis age	67.3	7.9	66.3	6.4	66.5	6.8	65.2	6.2	0.12	
Operation age	67.7	7.8	66.9	6.6	66.9	6.9	65.6	6.2	0.11	
Biochemical failure (n/%)b	
NO	82	65.6	104	77.61	76	63.33	90	69.77	0.07	
YES	43	34.4	30	22.39	44	36.67	39	30.23		
Follow-up period (Median/IQR)c(years)	9.18	5.34–12.39	7.90	5.61–12.02	8.85	5.78–12.41	8.85	6.24–11.56	0.83	
aContinuous variables were expressed as mean ± standard deviation (SD) and were analyzed using ANOVA follow a normal data distribution.

bCategorical variables were expressed as numbers (percent) and were analyzed using the Chi-square test.

cUsing Kruskal–Wallis test.

Compared with participants in Q1, participants in the highest quartile (Q4) had higher risk of incident prostate cancer (p < 0.0001) (Fig. 2).Fig. 2 Cumulative incidence rate of prostate cancer in the entire cohort by PRS group.

Figure 3 displays the Kaplan–Meier survival curve for overall mortality of the 24,778 male participants (Fig. 3A) and the 903 patients diagnosed with prostate cancer (Fig. 3B), respectively. Both Kaplan–Meier survival curves showed no significant differences in all-cause death among the four groups (p = 0.39 and p = 0.55, respectively). Figure 4 displays the Kaplan–Meier survival curve for the 508 patients who received radical prostatectomy, and no significant differences were observed in terms of biochemical failure among the four groups (Median biochemical failure time: Q1 to Q4: 41.4, 47.0, 47.9 and 51.0 months, respectively, p = 0.27).Fig. 3 The overall mortality (Kaplan–Meier survival curve) for the 24,778 male participants (A) and 903 patients (B) diagnosed with prostate cancer, respectively. The median overall survival is not reached within the follow-up period.

Fig. 4 The Kaplan–Meier survival curve was used to analyze the biochemical failure outcomes of 508 patients who underwent radical prostatectomy. No statistically significant differences were observed among the four groups by quartiles of polygenic risk score. The median times to biochemical failure for patients in quartiles 1–4 were 41.4, 47.0, 47.9, and 51.0 months, respectively (p = 0.27).

Furthermore, we conducted a survival analysis of 170 patients diagnosed with stage four prostate cancer who underwent hormone therapy (Table 7). A median follow-up duration of 8.28 years (IQR 4.90–10.91) in Q1, 7.93 years (IQR 5.19–10.99) in Q2, 8.67 years (IQR 4.22–11.52) in Q3 and 6.94 years (IQR 4.13–10.69) in Q4, respectively. There was no significant for the diagnosis age among the four quartile groups (p = 0.7). Out of this group, 45 individuals passed away during the follow-up period. Figure 5 showed the Kaplan Meier survival curve for the 170 patients with stage four prostate cancer, and no significant differences were observed in terms of all-cause death among the four groups (Median overall survival time: Q1–Q4: 46.5, 46.4, 40.3 and 44.2 months, respectively, p = 0.99).Table 7 Characteristics of patients with stage 4 prostate cancer use hormone therapy (N = 170).

Variables	Quartile of polygenic risk score (PGS000662)	P value	
Q1 (n = 40)	Q2 (n = 44)	Q3 (n = 40)	Q4 (n = 46)	
n	%	n	%	n	%	n	%	
Demography (mean/SD)a	
Diagnosis age	71.3	10.5	69.5	8.2	71.8	7.8	70.7	9.5	0.70	
Hormone therapy age	71.6	10.4	69.6	8.3	71.8	7.7	70.8	9.5	0.69	
Death (n/%)b	0.90	
NO	29	72.5	31	70.5	31	77.5	34	73.9		
YES	11	27.5	13	29.6	9	22.5	12	26.1		
Follow-up period (Median/IQR)c(years)	8.28	4.90–10.91	7.93	5.19–10.99	8.67	4.22–11.52	6.94	4.13–10.69	0.64	
aContinuous variables were expressed as mean ± standard deviation (SD) and were analyzed using ANOVA follow a normal data distribution.

bCategorical variables were expressed as numbers (percent) and were analyzed using the Chi-square test.

cUsing Kruskal–Wallis test.

Fig. 5 The Kaplan–Meier survival curve was used to analyze the overall survival outcomes of 170 patients with stage four prostate cancer. No statistically significant differences were observed among the four groups in terms of all-cause death by quartiles of polygenic risk score. The median overall survival times for patients in quartiles 1–4 were 46.5, 46.4, 40.3, and 44.2 months, respectively (p = 0.99).

Discussion

Our study aimed to utilize the PRS method to investigate the association between disease associated variants identified in GWAS and prostate cancer susceptibility and prognosis in Han Chinese individuals from TCVGH-TPMI cohort. We observed that individuals in the top quartile of PRS had a significantly higher risk of prostate cancer compared to those in the bottom quartile. Furthermore, the higher PRS was associated with early onset of prostate cancer. However, we did not find any correlation between PRS and tumor aggressiveness as determined by tumor stage, PSA levels and Gleason score. Additionally, PRS was not predictive of treatment outcomes in patients with localized prostate cancer who received radical prostatectomy or in patients with stage four prostate cancer who were treated with hormone therapy.

The PRS, PGS000662, used in our study consisted of 269 SNPs and was developed by Conti et al. in a trans-ancestry study that included GWAS data from 107,247 cases and 127,006 controls. This PRS comprised 269 single nucleotide polymorphisms (SNPs) and was developed using data from 12 large cohorts of cases and controls, encompassing individuals from various ancestries, including European, African American or Afro-Caribbean, African unspecified, East Asian, and Hispanic or Latin American backgrounds5. Conti et al. identified 86 new genetic risk variants independently associated with prostate cancer risk, in addition to the 269 known risk variants. Their PRS has been extensively validated in various cohort studies, consistently demonstrating its predictive value across different populations. In their original research, they found that PRS 90–100% compared to 40–60% increased risk of prostate cancer (OR = 4.47, 95% CI = 3.52–5.68) in EAST Asia. Furthermore, the risk was notably elevated among individuals with a PRS of 99–100% (OR = 9.41, 95% CI = 5.6–15.82).

Conti’s PRS has been utilized in several cohort study with consistent results. For instance, Plym et al.22 found that men with higher PRS scores, particularly when combined with a family history of prostate or breast cancer, exhibited significantly increased risks of prostate cancer and prostate cancer-specific death. Furthermore, Plym et al. utilized Conti's PRS to examine different ancestries and reported increased prostate cancer risk in European (PRS 90–100% vs. 40–60%, OR = 3.89, 95%CI = 3.24–4.68) and African ancestry populations but no Asian population (PRS 90–100% vs. 40–60%, OR = 3.81, 95%CI = 1.48–10.19). Additionally, Plym et al. found that men in the top quartile of PRS, along with a family history of prostate or breast cancer, had the highest risk of prostate cancer (HR = 6.95, 95% CI = 5.57–8.66) and prostate cancer-specific death (HR = 4.84, 95% CI = 2.59–9.03) compared to men in the bottom quartile with no family history23. Furthermore, they reported that by the age of 85, the cumulative incidence of prostate cancer was 7.1% in the bottom decile and 54.1% in the top decile for European American men22. In addition to Conti’s 269 PRS, Chen et al.24 identified nine novel susceptibility loci for prostate cancer in African men, which were strongly associated with prostate cancer risk and aggressive disease in African ancestry populations (PRS 90–100% vs. 40–60%, OR = 3.19, 95% CI = 3.00–3.40). Our study is the first and largest GWAS cohort to examine Conti’s 269 PRS in Han Chinese individuals (PRS Q4 vs. Q1, HR 4.770, 95% CI = 3.999–5.689) and found similar predictive value as observed in European and African ancestries.

To the best of our knowledge, the first PRS for prostate cancer was conducted in 2008, utilizing five SNPs among the Swedish population. Incorporation of this PRS and family history accounted for 46% of prostate cancer cases with an odds ratio of 9.4625. Subsequently, numerous studies investigating genetic factors and prostate cancer susceptibility have been published. Nordstrom et al. also reported the potential application of PRS in predicting prostate cancer risk in individuals with PSA levels between 1 and 3 ng/ml26. Another cohort study evaluating pathogenic variants in 14 prostate cancer susceptibility genes and 72 validated prostate cancer-associated SNPs further confirmed the predictive value and potential clinical utility of PRS for risk assessment in addition to family history27. In conclusion, Siltari et al. summarized a total of 16 PRS prostate cancer studies involving 9 to 448 SNPs, and found that the ability of PRS to identify men with prostate cancer was modest [pooled AUC 0.63, 95% CI 0.62–0.64], and could be improved when combined with clinical variables (AUC 0.74, 95% CI 0.68–0.81)28.

In Ho et al., the PGS000662 demonstrated excellent performance in predicting prostate cancer for Han Chinese, with an area under the receiver operating characteristic curve (AUC) of 0.729. Similarly, in this study, the predictive value of PGS000662 for prostate cancer was 0.685 (95% CI = 0.427–0.732) (Supplementary Fig. 3). In comparison to the study by Ho et al., our study includes a larger number of male participants (24,778 vs. 9,610) and prostate cancer cases (903 vs. 308). However, we did not observe any significant correlation between the polygenic risk score (PRS) and disease aggressiveness or treatment prognosis. This finding is consistent with previous studies suggesting that while PRS may be associated with disease incidence, it does not necessarily provide predictive value for aggressive disease or cancer-related mortality beyond prostate-specific antigen (PSA) level30,31. Possible reasons for this lack of correlation include confounding factors such as cancer stage, early cancer screening practices, or treatment availability. Although we specifically focused on localized prostate cancer cases that received radical prostatectomy and stage 4 prostate cancer cases, our sample size for these analyses was limited (508 and 170, respectively). Further large-scale analyses would be beneficial for more robust conclusions.

The incidence of prostate cancer is notably lower in Asian countries compared to Western populations32. The discrepancy may be attributed to the lack of systemic PSA screening; however, genetic ethnicities could potentially account for the significant differences in prostate cancer incidence. Zhu et al. conducted a Chinese cohort study comprising 176 cases and 548 controls, which identified 24 prostate cancer-associated SNPs and revealed that the genetic score was able to predict prostate cancer risk in the overall population as well as in individuals aged 60–70 years33. Another Chinese cohort study conducted by Na et al. compared seven prostate cancer risk-associated SNPs implicated in East Asians with seventy-six prostate cancer risk-associated SNPs implicated in at least one racial group. They found that the former set of SNPs demonstrated better predictive performance for prostate cancer (AUC 0.602 vs. 0.573, respectively)34. Notably, although most PRS studies have been developed in Western countries, they have shown similar predictive performance in Asian populations9.

Furthermore, our study has revealed that in addition to supporting the association of PRS with prostate incidence, PRS is also significantly associated with disease development at a younger age. This finding aligns with previous research indicating that the relative risk of PRS for prostate cancer varies depending on age. For instance, Schaid et al. conducted refined analyses on a validated PRS for prostate cancer and reported significantly higher relative risks for younger men (relative risk of 2.56, age 30–55 years) compared with older men (relative risk of 1.86, aged 70–88 years)35. Similarly, Varma et al. demonstrated that machine learning algorithms incorporating PRS information and basic patient data could provide risk assessment in men younger than 55 years, for whom screening is not standard practice36. Moreover, Seibert et al. reported that PRS based on 54 SNPs was a highly significant predictor of age at diagnosis of aggressive prostate cancer37. Consistent with these findings, our study also observed that the diagnosis age in the top quartile of PRS was lower than that in the bottom quartile of PRS in our population (Table 2), and PRS had similar impacts across different age groups (Table 4).

Although there was a positive correlation between PRS and prostate incidence in our study population, we did not find any significant correlation between PRS and disease aggressiveness or treatment prognosis. This finding is consistent with a population-based study conducted by Klein et al. in Sweden, which shown that PRS was associated with incident of prostate cancer and prostate cancer death, but it did not provide additional utility beyond PSA30. Furthermore, the study by Schaffer et al., which used PRS with 269 SNPs by Conti in a cohort of 655 men who underwent prostate biopsy, found that although PRS 269 improved the prediction of all prostate cancer cases, it did not improve the risk prediction of aggressive disease31.

The study by Ou et al. conducted in South Korea utilized a cohort design and demonstrated that a PRS incorporating sixteen SNPs was able to predict biochemical failure following radical prostatectomy. The 10-year biochemical-free survival rate was reported to be 46.3% in the high PRS group compared to 81.8% in the low PRS group38. However, another multi-center cohort study from Taiwan presented the opposite conclusions. Wang et al. found that PRS was associated with early onset age of prostate cancer in patients undergoing radical prostatectomy, but it did not predict disease recurrence39. Our own results were consistent with this finding, as we did not observe a predictive association between PRS and biochemical failure following radical prostatectomy. Furthermore, we also reported for the first time that PRS was not predictive of overall survival in stage four prostate cancer patients treated with hormone therapy. In contrast, a prospective cohort study conducted in the UK biobank setting, which considered mortality as an endpoint, found that PRS was superior in predicting incidence and mortality compared to family history and rare pathogenic mutation40. Despite the promising potential of PRS in predicting prostate cancer incidence and susceptibility, there is currently no consensus on its ability to predict prognosis or treatment outcomes. One possible explanation for this inconsistency is the significant impact of life-prolonging treatments such as androgen receptor targeted agents and chemotherapy on prostate cancer outcomes, which may reduce the contribution of genetic factors. Additionally, the widespread implementation of early screening programs for prostate cancer has resulted in the detection of cancers at earlier stages, thereby further reducing the cancer specific mortality rates in the population.

The value of germline genetic testing for prostate cancer screening strategies has been called into question, as it has not been shown to be superior to other validated biomarkers for prostate cancer prediction, such as PSA or Prostate Health Index31. In the BARCODE1 study, which enrolled 5000 men to access their genetic risk of prostate cancer, only 18 out of 25 participants in the top 10% of the PRS distribution underwent Magnetic Resonance Imaging and biopsy, and only seven cases diagnosed with prostate cancer (38.9%). Furthermore, all the identified cancers were low-risk and were managed with active surveillance41. As current prostate cancer risk SNPs were primarily designed to predict disease incidence, it may be necessary to expand the polygenic models to include rare coding variants that influence disease aggressiveness and outcome, such as DNA repair genes, in order to improve the prediction of lethal prostate cancer42,43.

This study has several limitations that should be acknowledged. Firstly, the sample size of the study was relatively small, and the follow-up period was not long enough, which may have limited the generalizability of our findings in terms of disease treatment and prognosis. Enrolled participants consisted of patients who visited our institute, thereby limiting the applicability of the findings to the general population. Furthermore, due to limitations in data collection, we were unable to obtain medical records from sources outside of the hospital where the study was conducted. Additionally, the family history records of prostate cancer patients included in this study were incomplete. This limitation could potentially be overcome by leveraging national-level data. Moreover, we did not include the treatment effects of novel hormone therapy and chemotherapy, which have been proven to be life-prolonging agents for metastatic prostate cancer, and their omission may confound our results. Additionally, the PRS utilized in our study was obtained from cross-ancestry GWAS, which may have potentially diminished its predictive efficacy5. A better approach would be to develop a GWAS-based PRS system specifically designed for the Taiwanese population. This can be accomplished by utilizing national-level data from the TPMI study cohort, which is conducted by Academia Sinica in collaboration with 13 medical center-level hospitals across Taiwan. By leveraging this extensive dataset, we can enhance the accuracy of disease incidence and prognosis prediction for the Taiwanese ethnic group. Finally, to develop PRS system not only for cancer incidence but also disease prognosis or drug selection would be helpful for clinician in their decision making and treatment approaches.

Conclusions

In this cohort study conducted in a hospital setting, we observed that individuals in the top quartile of PRS were more susceptible to prostate cancer and tended to be diagnosed at a younger age. However, we did not find any significant associations between PRS and disease stage, PSA level, Gleason grade, or D'Amico risk classification. Furthermore, PRS was not a reliable predictor of biochemical failure in localized prostate cancer patients treated with radical prostatectomy, nor overall death in stage four prostate cancer patients treated with hormone therapy. To further validate our findings, PRS scoring derived from a cohort with a longer follow-up, such as the TPMI cohort, would be preferable.

Supplementary Information

Supplementary Table 1.

Supplementary Figure 2.

Supplementary Figure 3.

Supplementary Figure 4.

Supplementary Information 5.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71544-7.

Acknowledgements

We thank all of the participants and investigators from the Taiwan Precision Medicine Initiative, which was funded by Academia Sinica (40-05-GMM; AS-GC-110-MD02; 236e-1100202), and National Development Fund, Executive Yuan [NSTC 111-3114-Y-001-001]. The authors sincerely appreciate the assistance of the Center for Translational Medicine of Taichung Veterans General Hospital.

Author contributions

Study design and protocol development: SCH, THH, JRL, ICC; manuscript writing and editing: SCH, ICC; statistical analysis: ICC, CYW; data collection and patient management: SCH, LWC, SSW, JRL; supervision or mentorship: SCH, LWC, THH, CYW, SSW, JRL and ICC. All authors reviewed the final manuscript.

Funding

This study was funded by Taichung Veterans General Hospital, Taiwan [grant numbers TCVGH-TCVGH-1127304B, TCVGH-1135003B and TCVGH-1137302B].

Data availability

All data used in this study are available in this article. However, the individual-level PRS and prostate cancer information data are not currently available within the paper. However, we are committed to facilitating access to the data for interested researchers. To request access to the underlying data, please contact the corresponding author. We will provide further information regarding the availability and any necessary procedures for obtaining access, taking into account any ethical, legal, or privacy considerations associated with the data. We appreciate your understanding and patience in this matter.

Competing interests

The authors declare no competing interests.

Ethics statement

The studies involving human participants were reviewed and approved by certification at Taichung Veteran General Hospital, Taiwan, with Certification of approval with IRB: CE23119A. The patients/participants provided their written informed consent to participate in this study.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Siegel RL Miller KD Fuchs HE Jemal A Cancer statistics, 2022 CA Cancer J. Clin. 2022 72 7 33 10.3322/caac.21708 35020204
Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer statistics, 2022. CA Cancer J. Clin. 72, 7–33. 10.3322/caac.21708 (2022).35020204 10.3322/caac.21708
2. Cancer registry annual report, 2019 Taiwan. Health Promotion Administration Ministry Of Health And WelfarE (December 2021).
3. Hemminki K Familial risk and familial survival in prostate cancer World J. Urol. 2012 30 143 148 10.1007/s00345-011-0801-1 22116601
Hemminki, K. Familial risk and familial survival in prostate cancer. World J. Urol. 30, 143–148. 10.1007/s00345-011-0801-1 (2012).22116601 10.1007/s00345-011-0801-1
4. Jansson KF Concordance of tumor differentiation among brothers with prostate cancer Eur. Urol. 2012 62 656 661 10.1016/j.eururo.2012.02.032 22386193
Jansson, K. F. et al. Concordance of tumor differentiation among brothers with prostate cancer. Eur. Urol. 62, 656–661. 10.1016/j.eururo.2012.02.032 (2012).22386193 10.1016/j.eururo.2012.02.032
5. Conti DV Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction Nat. Genet. 2021 53 65 75 10.1038/s41588-020-00748-0 33398198
Conti, D. V. et al. Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction. Nat. Genet. 53, 65–75. 10.1038/s41588-020-00748-0 (2021).33398198 10.1038/s41588-020-00748-0
6. Chung CC Magalhaes WC Gonzalez-Bosquet J Chanock SJ Genome-wide association studies in cancer–current and future directions Carcinogenesis 2010 31 111 120 10.1093/carcin/bgp273 19906782
Chung, C. C., Magalhaes, W. C., Gonzalez-Bosquet, J. & Chanock, S. J. Genome-wide association studies in cancer–current and future directions. Carcinogenesis 31, 111–120. 10.1093/carcin/bgp273 (2010).19906782 10.1093/carcin/bgp273
7. Torkamani A Wineinger NE Topol EJ The personal and clinical utility of polygenic risk scores Nat. Rev. Genet. 2018 19 581 590 10.1038/s41576-018-0018-x 29789686
Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 581–590. 10.1038/s41576-018-0018-x (2018).29789686 10.1038/s41576-018-0018-x
8. Schumacher FR Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci Nat. Genet. 2018 50 928 936 10.1038/s41588-018-0142-8 29892016
Schumacher, F. R. et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat. Genet. 50, 928–936. 10.1038/s41588-018-0142-8 (2018).29892016 10.1038/s41588-018-0142-8
9. Oh JJ Hong SK Polygenic risk score in prostate cancer Curr. Opin. Urol. 2022 32 466 471 10.1097/mou.0000000000001029 35855560
Oh, J. J. & Hong, S. K. Polygenic risk score in prostate cancer. Curr. Opin. Urol. 32, 466–471. 10.1097/mou.0000000000001029 (2022).35855560 10.1097/mou.0000000000001029
10. Song SH Byun SS Polygenic risk score for genetic evaluation of prostate cancer risk in Asian populations: A narrative review Investig. Clin. Urol. 2021 62 256 266 10.4111/icu.20210124 33943048
Song, S. H. & Byun, S. S. Polygenic risk score for genetic evaluation of prostate cancer risk in Asian populations: A narrative review. Investig. Clin. Urol. 62, 256–266. 10.4111/icu.20210124 (2021).33943048 10.4111/icu.20210124
11. Huynh-Le MP Polygenic hazard score is associated with prostate cancer in multi-ethnic populations Nat. Commun. 2021 12 1236 10.1038/s41467-021-21287-0 33623038
Huynh-Le, M. P. et al. Polygenic hazard score is associated with prostate cancer in multi-ethnic populations. Nat. Commun. 12, 1236. 10.1038/s41467-021-21287-0 (2021).33623038 10.1038/s41467-021-21287-0
12. Song SH Prediction of clinically significant prostate cancer using polygenic risk models in Asians Investig. Clin. Urol. 2022 63 42 52 10.4111/icu.20210305 34983122
Song, S. H. et al. Prediction of clinically significant prostate cancer using polygenic risk models in Asians. Investig. Clin. Urol. 63, 42–52. 10.4111/icu.20210305 (2022).34983122 10.4111/icu.20210305
13. Akamatsu S Clinical utility of germline genetic testing in Japanese men undergoing prostate biopsy JNCI Cancer Spectr. 2022 6 pkac001 10.1093/jncics/pkac001 35118230
Akamatsu, S. et al. Clinical utility of germline genetic testing in Japanese men undergoing prostate biopsy. JNCI Cancer Spectr. 6, pkac001. 10.1093/jncics/pkac001 (2022).35118230 10.1093/jncics/pkac001
14. Vickers AJ Sud A Bernstein J Houlston R Polygenic risk scores to stratify cancer screening should predict mortality not incidence NPJ Precis. Oncol. 2022 6 32 10.1038/s41698-022-00280-w 35637246
Vickers, A. J., Sud, A., Bernstein, J. & Houlston, R. Polygenic risk scores to stratify cancer screening should predict mortality not incidence. NPJ Precis. Oncol. 6, 32. 10.1038/s41698-022-00280-w (2022).35637246 10.1038/s41698-022-00280-w
15. Fan CT Lin JC Lee CH Taiwan Biobank: A project aiming to aid Taiwan's transition into a biomedical island Pharmacogenomics 2008 9 235 246 10.2217/14622416.9.2.235 18370851
Fan, C. T., Lin, J. C. & Lee, C. H. Taiwan Biobank: A project aiming to aid Taiwan’s transition into a biomedical island. Pharmacogenomics 9, 235–246. 10.2217/14622416.9.2.235 (2008).18370851 10.2217/14622416.9.2.235
16. Wei CY Genetic profiles of 103,106 individuals in the Taiwan Biobank provide insights into the health and history of Han Chinese NPJ Genom. Med. 2021 6 10 10.1038/s41525-021-00178-9 33574314
Wei, C. Y. et al. Genetic profiles of 103,106 individuals in the Taiwan Biobank provide insights into the health and history of Han Chinese. NPJ Genom. Med. 6, 10. 10.1038/s41525-021-00178-9 (2021).33574314 10.1038/s41525-021-00178-9
17. Das S Next-generation genotype imputation service and methods Nat. Genet. 2016 48 1284 1287 10.1038/ng.3656 27571263
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287. 10.1038/ng.3656 (2016).27571263 10.1038/ng.3656
18. Auton A A global reference for human genetic variation Nature 2015 526 68 74 10.1038/nature15393 26432245
Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74. 10.1038/nature15393 (2015).26432245 10.1038/nature15393
19. Marees AT A tutorial on conducting genome-wide association studies: Quality control and statistical analysis Int. J. Methods Psychiatr. Res. 2018 27 e1608 10.1002/mpr.1608 29484742
Marees, A. T. et al. A tutorial on conducting genome-wide association studies: Quality control and statistical analysis. Int. J. Methods Psychiatr. Res. 27, e1608. 10.1002/mpr.1608 (2018).29484742 10.1002/mpr.1608
20. Lambert SA The polygenic score catalog as an open database for reproducibility and systematic evaluation Nat Genet 2021 53 420 425 10.1038/s41588-021-00783-5 33692568
Lambert, S. A. et al. The polygenic score catalog as an open database for reproducibility and systematic evaluation. Nat Genet 53, 420–425. 10.1038/s41588-021-00783-5 (2021).33692568 10.1038/s41588-021-00783-5
21. D'Amico AV Cancer-specific mortality after surgery or radiation for patients with clinically localized prostate cancer managed during the prostate-specific antigen era J. Clin. Oncol. 2003 21 2163 2172 10.1200/jco.2003.01.075 12775742
D’Amico, A. V. et al. Cancer-specific mortality after surgery or radiation for patients with clinically localized prostate cancer managed during the prostate-specific antigen era. J. Clin. Oncol. 21, 2163–2172. 10.1200/jco.2003.01.075 (2003).12775742 10.1200/jco.2003.01.075
22. Plym A Evaluation of a multiethnic polygenic risk score model for prostate cancer J. Natl. Cancer Inst. 2022 114 771 774 10.1093/jnci/djab058 33792693
Plym, A. et al. Evaluation of a multiethnic polygenic risk score model for prostate cancer. J. Natl. Cancer Inst. 114, 771–774. 10.1093/jnci/djab058 (2022).33792693 10.1093/jnci/djab058
23. Plym A Family history of prostate and breast cancer integrated with a polygenic risk score identifies men at highest risk of dying from prostate cancer before age 75 years Clin. Cancer Res. 2022 28 4926 4933 10.1158/1078-0432.Ccr-22-1723 36103261
Plym, A. et al. Family history of prostate and breast cancer integrated with a polygenic risk score identifies men at highest risk of dying from prostate cancer before age 75 years. Clin. Cancer Res. 28, 4926–4933. 10.1158/1078-0432.Ccr-22-1723 (2022).36103261 10.1158/1078-0432.Ccr-22-1723
24. Chen F Evidence of novel susceptibility variants for prostate cancer and a multiancestry polygenic risk score associated with aggressive disease in men of african ancestry Eur. Urol. 2023 10.1016/j.eururo.2023.01.022 38135561
Chen, F. et al. Evidence of novel susceptibility variants for prostate cancer and a multiancestry polygenic risk score associated with aggressive disease in men of african ancestry. Eur. Urol.10.1016/j.eururo.2023.01.022 (2023).38135561 10.1016/j.eururo.2023.01.022
25. Zheng SL Cumulative association of five genetic variants with prostate cancer N. Engl. J. Med. 2008 358 910 919 10.1056/NEJMoa075819 18199855
Zheng, S. L. et al. Cumulative association of five genetic variants with prostate cancer. N. Engl. J. Med. 358, 910–919. 10.1056/NEJMoa075819 (2008).18199855 10.1056/NEJMoa075819
26. Nordström T Aly M Eklund M Egevad L Grönberg H A genetic score can identify men at high risk for prostate cancer among men with prostate-specific antigen of 1–3 ng/ml Eur. Urol. 2014 65 1184 1190 10.1016/j.eururo.2013.07.005 23891454
Nordström, T., Aly, M., Eklund, M., Egevad, L. & Grönberg, H. A genetic score can identify men at high risk for prostate cancer among men with prostate-specific antigen of 1–3 ng/ml. Eur. Urol. 65, 1184–1190. 10.1016/j.eururo.2013.07.005 (2014).23891454 10.1016/j.eururo.2013.07.005
27. Black MH Validation of a prostate cancer polygenic risk score Prostate 2020 80 1314 1321 10.1002/pros.24058 33258481
Black, M. H. et al. Validation of a prostate cancer polygenic risk score. Prostate 80, 1314–1321. 10.1002/pros.24058 (2020).33258481 10.1002/pros.24058
28. Siltari A How well do polygenic risk scores identify men at high risk for prostate cancer? Systematic review and meta-analysis Clin. Genitourin. Cancer 2023 21 316 e311 316.e311 10.1016/j.clgc.2022.09.006
Siltari, A. et al. How well do polygenic risk scores identify men at high risk for prostate cancer? Systematic review and meta-analysis. Clin. Genitourin. Cancer 21(316), e311-316.e311. 10.1016/j.clgc.2022.09.006 (2023).10.1016/j.clgc.2022.09.006
29. Ho PJ Polygenic risk scores for the prediction of common cancers in East Asians: A population-based prospective cohort study Elife 2023 12 e82608 10.7554/eLife.82608 36971353
Ho, P. J. et al. Polygenic risk scores for the prediction of common cancers in East Asians: A population-based prospective cohort study. Elife 12, e82608. 10.7554/eLife.82608 (2023).36971353 10.7554/eLife.82608
30. Klein RJ Prostate cancer polygenic risk score and prediction of lethal prostate cancer NPJ Precis. Oncol. 2022 6 25 10.1038/s41698-022-00266-8 35396534
Klein, R. J. et al. Prostate cancer polygenic risk score and prediction of lethal prostate cancer. NPJ Precis. Oncol. 6, 25. 10.1038/s41698-022-00266-8 (2022).35396534 10.1038/s41698-022-00266-8
31. Schaffer KR A polygenic risk score for prostate cancer risk prediction JAMA Intern. Med. 2023 183 386 388 10.1001/jamainternmed.2022.6795 36877498
Schaffer, K. R. et al. A polygenic risk score for prostate cancer risk prediction. JAMA Intern. Med. 183, 386–388. 10.1001/jamainternmed.2022.6795 (2023).36877498 10.1001/jamainternmed.2022.6795
32. Ha Chung B Horie S Chiong E The incidence, mortality, and risk factors of prostate cancer in Asian men Prostate. Int. 2019 7 1 8 10.1016/j.prnil.2018.11.001 30937291
Ha Chung, B., Horie, S. & Chiong, E. The incidence, mortality, and risk factors of prostate cancer in Asian men. Prostate. Int. 7, 1–8. 10.1016/j.prnil.2018.11.001 (2019).30937291 10.1016/j.prnil.2018.11.001
33. Zhu Y Influence of age on predictiveness of genetic risk score for prostate cancer in a Chinese hospital-based biopsy cohort Oncotarget 2015 6 22978 22984 10.18632/oncotarget.3938 26011940
Zhu, Y. et al. Influence of age on predictiveness of genetic risk score for prostate cancer in a Chinese hospital-based biopsy cohort. Oncotarget 6, 22978–22984. 10.18632/oncotarget.3938 (2015).26011940 10.18632/oncotarget.3938
34. Na R Race-specific genetic risk score is more accurate than nonrace-specific genetic risk score for predicting prostate cancer and high-grade diseases Asian J. Androl. 2016 18 525 529 10.4103/1008-682x.179857 27140652
Na, R. et al. Race-specific genetic risk score is more accurate than nonrace-specific genetic risk score for predicting prostate cancer and high-grade diseases. Asian J. Androl. 18, 525–529. 10.4103/1008-682x.179857 (2016).27140652 10.4103/1008-682x.179857
35. Schaid DJ Sinnwell JP Batzler A McDonnell SK Polygenic risk for prostate cancer: Decreasing relative risk with age but little impact on absolute risk Am. J. Hum. Genet. 2022 109 900 908 10.1016/j.ajhg.2022.03.008 35353984
Schaid, D. J., Sinnwell, J. P., Batzler, A. & McDonnell, S. K. Polygenic risk for prostate cancer: Decreasing relative risk with age but little impact on absolute risk. Am. J. Hum. Genet. 109, 900–908. 10.1016/j.ajhg.2022.03.008 (2022).35353984 10.1016/j.ajhg.2022.03.008
36. Varma A Early prediction of prostate cancer risk in younger men using polygenic risk scores and electronic health records Cancer Med. 2023 12 379 386 10.1002/cam4.4934 35751453
Varma, A. et al. Early prediction of prostate cancer risk in younger men using polygenic risk scores and electronic health records. Cancer Med. 12, 379–386. 10.1002/cam4.4934 (2023).35751453 10.1002/cam4.4934
37. Seibert TM Polygenic hazard score to guide screening for aggressive prostate cancer: Development and validation in large scale cohorts Bmj 2018 360 j5757 10.1136/bmj.j5757 29321194
Seibert, T. M. et al. Polygenic hazard score to guide screening for aggressive prostate cancer: Development and validation in large scale cohorts. Bmj 360, j5757. 10.1136/bmj.j5757 (2018).29321194 10.1136/bmj.j5757
38. Oh JJ Genetic risk score to predict biochemical recurrence after radical prostatectomy in prostate cancer: Prospective cohort study Oncotarget 2017 8 75979 75988 10.18632/oncotarget.18275 29100285
Oh, J. J. et al. Genetic risk score to predict biochemical recurrence after radical prostatectomy in prostate cancer: Prospective cohort study. Oncotarget 8, 75979–75988. 10.18632/oncotarget.18275 (2017).29100285 10.18632/oncotarget.18275
39. Wang SH Association between the polygenic liabilities for prostate cancer and breast cancer with biochemical recurrence after radical prostatectomy for localized prostate cancer Am. J. Cancer Res. 2021 11 2331 2342 34094689
Wang, S. H. et al. Association between the polygenic liabilities for prostate cancer and breast cancer with biochemical recurrence after radical prostatectomy for localized prostate cancer. Am. J. Cancer Res. 11, 2331–2342 (2021).34094689
40. Shi Z Performance of three inherited risk measures for predicting prostate cancer incidence and mortality: A population-based prospective analysis Eur. Urol. 2021 79 419 426 10.1016/j.eururo.2020.11.014 33257031
Shi, Z. et al. Performance of three inherited risk measures for predicting prostate cancer incidence and mortality: A population-based prospective analysis. Eur. Urol. 79, 419–426. 10.1016/j.eururo.2020.11.014 (2021).33257031 10.1016/j.eururo.2020.11.014
41. Benafif S The BARCODE1 Pilot: a feasibility study of using germline single nucleotide polymorphisms to target prostate cancer screening BJU Int. 2022 129 325 336 10.1111/bju.15535 34214236
Benafif, S. et al. The BARCODE1 Pilot: a feasibility study of using germline single nucleotide polymorphisms to target prostate cancer screening. BJU Int. 129, 325–336. 10.1111/bju.15535 (2022).34214236 10.1111/bju.15535
42. Li W Genome-wide scan identifies role for AOX1 in prostate cancer survival Eur. Urol. 2018 74 710 719 10.1016/j.eururo.2018.06.021 30289108
Li, W. et al. Genome-wide scan identifies role for AOX1 in prostate cancer survival. Eur. Urol. 74, 710–719. 10.1016/j.eururo.2018.06.021 (2018).30289108 10.1016/j.eururo.2018.06.021
43. Pritchard CC Inherited DNA-repair gene mutations in men with metastatic prostate cancer N. Engl. J. Med. 2016 375 443 453 10.1056/NEJMoa1603144 27433846
Pritchard, C. C. et al. Inherited DNA-repair gene mutations in men with metastatic prostate cancer. N. Engl. J. Med. 375, 443–453. 10.1056/NEJMoa1603144 (2016).27433846 10.1056/NEJMoa1603144
