Validation of the novel susceptibility loci for prostate cancer in a Chinese population

  • Authors:
    • Yishuo Wu
    • Haitao Chen
    • Ying Ji
    • Rong Na
    • Zengnan Mo
    • Dingwei Ye
    • Meilin Wang
    • Jun Qi
    • Xiaoling Lin
    • Qiang Ding
    • Jianfeng Xu
    • S. Lilly Zheng
    • Yinghao Sun
    • Wei Meng
  • View Affiliations

  • Published online on: December 13, 2017     https://doi.org/10.3892/ol.2017.7602
  • Pages:2567-2573
Metrics: HTML 0 views | PDF 0 views     Cited By (CrossRef): 0 citations

Abstract

The present study evaluated 23 newly identified susceptibility loci for prostate cancer (PCa) in a Chinese population and assessed whether any validated loci were associated with the genetic risk score (GRS) of PCa in a Chinese population. A total of 1,417 patients with PCa and 1,008 controls were recruited in the present study. The association of each single nucleotide polymorphism (SNP) with PCa risk and PCa aggressiveness was analyzed. The predictive ability of two GRSs based on 30 SNPs (GRS30) and the 9 most significant SNPs (GRS9) in the Chinese population were also compared. Among the 19 SNPs evaluated, 1 SNP (rs7153648 at 14q23) was associated with PCa risk [odds ratio (OR)=1.206, P<0.05)] and 1 SNP (rs636291 at 1p23) was associated with PCa aggressiveness (OR=1.123, P<0.05). GRS30 and GRS9 were significantly increased in patients with PCa compared with that among non‑PCa controls. The areas under receiver operating characteristic curves of GRS9 and GRS 30 were similar (0.792 for GRS9 vs. 0.7994 for GRS30, P=0.138). To conclude, among the 19 SNPs evaluated, only 1 SNP was associated with PCa risk in the Chinese population. SNPs that were weakly associated with PCa were unlikely to improve the predictive ability of existing GRS in the Chinese population.

Introduction

Prostate cancer (PCa) is the second most common cancer and one of the leading causes of mortality among males worldwide by 2012 (1). The incidence of PCa in China is considered reduced compared with that in Western countries; however, it has been progressively increasing over the past 30 years (2).

Genetic susceptibility to PCa has been well established and almost 100 common risk loci have been identified by genome wide association studies (GWAS) among European, African-American, Japanese and Chinese populations (3,4). However, only 10 of these loci were initially identified from GWAS in Japanese and Chinese populations. Among previous evaluation and validation studies, a part of the loci was revealed to be associated with PCa risk in Chinese population (5). Since these risk-associated single nucleotide polymorphisms (SNPs) exhibited a cumulative effect on PCa risk, the genetic risk scores (GRS) derived from PCa risk-associated SNPs were able to evaluate an individual's risk of PCa. The GRS based on the Chinese population is established and demonstrated to be a significant predictor of biopsy outcome in previous studies (59).

With an increasing sample size used in GWAS through combined data, a meta-analysis of a multi-ethnic population, which included 87,040 individuals, identified 23 new susceptibility loci for PCa (including 15 in European, 7 in multiethnic and 1 in the early onset analysis) (10). These PCa risk-associated alleles exhibited decreased effects with odds ratios (ORs) ranging between 1.06 and 1.14 (10). However, since the Chinese population was not included in the study, the effects of these 23 novel risk variants in individuals of Chinese descent remains unknown.

The objective of the present study was to evaluate the 23 newly identified susceptibility loci for PCa in a Chinese population and assess whether any validated loci contributed to the GRS in predicting the risk of PCa in a Chinese population.

Materials and methods

Population

The baseline characteristics of the present study subjects were summarized (Table I). A total of 2,425 subjects including 1,417 patients with PCa and 1,008 controls were recruited in the present study. All patients were part of the China PCa consortium from the southeast of China (1113) recruited during January 2010 and December 2011, from which data were obtained. All cases were pathologically diagnosed with primary PCa and all the controls were recruited from the community or selected from subjects who had undergone routine physical examination in local hospitals. Written informed consent was obtained from subjects for their participation in the present study and a blood sample was taken from each subject at the time of recruitment for DNA extraction. The present study was reviewed and approved by the Institutional Review Board of every participating institution.

Table I.

Characteristics of study population.

Table I.

Characteristics of study population.

VariablesPCa cases (n=1,417)Controls (n=1,008)
Age, yearsa,b71.3±8.162.1±10.0
PSA, ng/mla,c
  0–3.9954 (4.0)965 (95.9)
  4–9.99187 (14.0)32 (3.2)
  10–19.99305 (22.8)6 (0.6)
  ≥20791 (59.2)3 (0.3)
  Missing80 (5.6)2 (0.2)
Gleason scorec
  ≤7809 (60.1)N/A
  ≥8537 (39.9)N/A
  Missing71 (5)N/A

a At the time of diagnosis for cases or at recruitment for controls.

b Data are presented as the mean ± standard deviation.

c Data are presented as n (%). PSA, prostate-specific antigen; N/A, not applicable; PCa, prostate cancer.

Genotyping and quality control

DNA samples were genotyped in the Center for Cancer Genomics at Wake Forest University (Winston-Salem, NC, USA) using the Illumina HumanOmniExpress BeadChips (Illumina, Inc., San Diego, CA, USA), which included 731,458 SNPs. For PCa risk-associated SNPs that were not included in the GWAS array, imputation was performed using IMPUTE 2.2.2 based on the combined data of the 1,000 Genomes project and HapMap3 data (14). A posterior probability of >0.9 was applied to call imputed genotypes. Imputed SNPs were excluded if they exhibited: i) A call rate <95%; ii) a minor allele frequency <0.05; or iii) P<1×10−3 in a Hardy-Weinberg equilibrium test in controls, as previously described (13).

Assessment of genetic risk

A GRS was calculated for each subject based on genotypes of the SNPs and weighted by their ORs and risk allele frequency, as described previously (15). GRS was calculated as

GRS=∏i=1nORigiWi

where gi is the genotype of SNP i for an individual (0, homozygous of non-risk allele; 1, heterozygous; 2 homozygous of risk allele). ORi is the OR of SNP i estimated from external study (16), Wi is the average population risk of SNP i, calculated as Wi = fi2ORi2 + 2fi(1-fi) ORi + (1-fi)2, where fi is the risk allele frequency of SNP i based on the 1,000 Genome Project of the CHB (Han Chinese in Bejing, China) population (17). Therefore, a GRS value of 1.0 represents a population average risk.

Statistical analysis

A logistic regression model was used to analyze the association of each SNP with PCa risk, assuming an additive genetic model, which was implemented in PLINK version 1.07 (18). ORs and 95% confident intervals (CIs) were estimated from logistic regression analysis with adjustment for age and the highest eigen value. Student's t-tests were used to analyze the differences in means of normally distributed variables between 2 groups. For variables that were not normally distributed, 2 tests were performed: i) A nonparametric method using the Wilcoxon rank sum test and ii) Student's t-tests for different means between 2 groups following log-transformation. Differences in binary variables were investigated using χ2 tests. Area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of GRS in discriminating between 2 groups of subjects. The difference between two AUCs was determined using Delong's test (19). P<0.05 was considered to indicate a statistically significant difference. All statistical analyses were performed using SPSS 19.0 (SPSS; IBM Corporation, Armonk, NY, USA).

Results

SNPs and PCa risk

The present study evaluated 19 newly reported SNPs. Among the 19 SNPs, only 1 (rs7153648 at 14q23) was associated with PCa risk in the China PCa cohort (OR=1.206, P<0.05). The direction of the effect was consistent with the previous multiethnic meta-analysis (10). The other 18 SNPs that had previously demonstrated genome-wide significance in European ancestry meta-analysis and multiethnic meta-analysis (Table II) either were not associated with PCa risk or did not demonstrate the same magnitude of effect in the Chinese population investigated in the present study.

Table II.

Association results for 19 novel risk variants for PCa in Chinese males.

Table II.

Association results for 19 novel risk variants for PCa in Chinese males.

Risk allele frequency

Origin of GWASSNP IDChromosome positionaRegionGeneAllelesRisk allelePCA casesControlsOdds ratioP-value
Europeanrs6362911p35IntronPEX14A/GG0.2540.2411.120.148
Europeanrs175996291q21IntronGOLPH3LG/AG0.1030.0981.030.780
Multi-ethnicrs17751481q32IntergenicSLC41A1C/TC0.5080.4861.130.109
Europeanrs92877192p25IntergenicNOL10C/TC0.3720.3501.140.075
Europeanrs100094094q13IntergenicCOX18T/CC0.4840.4681.070.310
Europeanrs47132666p24IntronNEDD9C/TC0.1870.1771.110.263
Europeanrs1154571356p22IntronTRIM31A/GA0.1440.1611.030.903
Europeanrs1153069676p21IntergenicHLA-DRB6G/CC0.1350.1261.120.570
Multi-ethnicrs94431896q14IntronMYO6G/AA0.6330.6321.030.728
Europeanrs562325067p12IntronTNS3A/GA0.3830.3831.020.764
Europeanrs176944939p21IntronCDKN2B-AS1G/CG0.0300.0291.130.575
Europeanrs7693403410q11Intron41706T/CT1.0000.9991.000.999
Europeanrs1121477511q23IntronHTR3BG/AG0.7980.7960.970.756
Multi-ethnicrs715364814q23IntergenicSIX1C/GC0.1780.1531.210.045
Europeanrs801467114q24IntergenicTTC9G/AG0.3000.2980.990.904
Multi-ethnicrs1205144316q22IntronPHLPP2A/GA0.7620.7580.910.243
Multi-ethnicrs1248032820q13IntronADNPT/CT0.9240.9190.940.616
Multi-ethnicrs104144921q22IntergenicTMPRSS2G/AG0.1640.1611.050.674
Multi-ethnicrs223877622q11IntronTBX1G/AG0.5150.5151.070.376

a Genome Build 37. PCa, prostate cancer; SNP, single nucleotide polymorphism; GWAS, genome wide association study.

SNPs and PCa aggressiveness

The association between the 19 SNPs and PCa aggressiveness was also investigated (cases with a Gleason score ≥7; Table III). The results did not demonstrate a significant association between rs7153648 and PCa aggressiveness, whereas rs636291 at 1p23 was significantly associated with PCa aggressiveness (OR=1.123, P<0.05).

Table III.

Association results for 19 novel risk variants for aggressive PCa in Chinese males.

Table III.

Association results for 19 novel risk variants for aggressive PCa in Chinese males.

Risk allele frequency

Origin of GWASSNP IDChromosome positionaRegionGeneAllelesRisk allelePCa casesControlsOdds ratioP-value
Europeanrs6362911p35IntronPEX14A/GG0.2620.2391.160.049
Europeanrs175996291q21IntronGOLPH3LG/AG0.1020.1001.000.984
Multi-ethnicrs17751481q32IntergenicSLC41A1C/TC0.5080.4901.110.148
Europeanrs92877192p25IntergenicNOL10C/TC0.3730.3561.070.303
Europeanrs100094094q13IntergenicCOX18T/CC0.4770.4750.980.746
Europeanrs47132666p24IntronNEDD9C/TT0.8200.8180.990.862
Europeanrs1154571356p22IntronTRIM31A/GA0.1390.1320.950.790
Europeanrs1153069676p21IntergenicHLA-DRB6G/CC0.1420.1241.210.308
Multi-ethnicrs94431896q14IntronMYO6G/AG0.3780.3621.100.141
Europeanrs562325067p12IntronTNS3A/GA0.3880.3791.070.374
Europeanrs176944939p21IntronCDKN2B-AS1G/CG0.0310.0291.120.565
Europeanrs7693403410q11Intron41706T/CT1.0001.0001.000.999
Europeanrs1121477511q23IntronHTR3BG/AG0.8000.7960.970.661
Multi-ethnicrs715364814q23IntergenicSIX1C/GC0.1710.1611.050.571
Europeanrs801467114q24IntergenicTTC9G/AG0.3050.2941.060.407
Multi-ethnicrs1205144316q22IntronPHLPP2A/GA0.7670.7600.920.286
Multi-ethnicrs1248032820q13IntronADNPT/CT0.9290.9190.890.352
Multi-ethnicrs104144921q22IntergenicTMPRSS2G/AA0.8400.8340.960.647
Multi-ethnicrs223877622q11IntronTBX1G/AG0.5170.5121.010.875

a Genome Build 37. PCa, prostate cancer; SNP, single nucleotide polymorphism; GWAS, genome wide association study.

SNPs, GRS and PCa

GRS was calculated using rs7153648 and 29 previously implicated SNPs (10). The mean GRS based on the 30 SNPs (GRS30) was significantly increased in patients with PCa compared with that among non-PCa individuals (1.439 vs. 0.961, P=7.44×10−41; Table IV). As reported in a previous study, it would be more efficient and reliable to calculate GRS using race-specific disease-associated SNPs that demonstrated genome-wide significance (20). Therefore, in the present study, GRS was also calculated based on the 9 strongest SNPs previously reported in individuals of Asian descent (GRS9; Table V) (16). The mean GRS based on 9 SNPs was 1.26 in patients with PCa and 0.99 in non-PCa controls (P=3.71×10−28).

Table IV.

Genetic score and prostate biopsy outcomes.

Table IV.

Genetic score and prostate biopsy outcomes.

Parameter9 SNPs30 SNPs
Genetic scorea
  PCa1.26±0.721.44±1.18
  Non-PCa0.99±0.530.96±0.73
  P-value 3.71×10−28 7.44×10−41
Association with PCab
  Genetic score ≤1.011
  Genetic score >1.02.47 (2.05–2.97)2.25 (1.96–2.58)
  P-value 6.90×10−22 2.97×10−31
Discrimination of PCa
  AUC0.7920.799
  P-value0.138
  (AUC comparison)

a Data presented as the mean ± standard deviation.

b Data presented as odds ratio (95% confidence interval). PCa, prostate cancer; AUC, areas under receiver operating characteristic curves; SNP, single nucleotide polymorphism.

Table V.

Most significant SNPs previously reported in Asian individualsa.

Table V.

Most significant SNPs previously reported in Asian individualsa.

Reported SNPs in meta-analysis

SNPLocus PositionbNearby geneAllelecORP-value Frequency in 1,000 Genome-CHB
rs126539465p15.331895829IRX4G/A0.78 1.94×10−12TT0.103TC0.474CC0.422
rs3393316q22.2 1.17×108RFX6C/T0.78 4.27×10−11CC0.103CT0.485TT0.412
rs15122688p21.223526463NKX3-1C/T0.76 1.40×10−13TT0.113TC0.526CC0.361
rs169019798q24.21 1.28×108POU5F1BC/A0.72 1.00×10−16AA0.052AC0.392CC0.557
rs69832678q24.21 1.28×108POU5F1BG/T1.23 8.36×10−9GG0.113GT0.526TT0.361
rs14472958q24.21 1.28×108LOC727677C/A0.65 3.92×10−23AA0.010AC0.237CC0.753
rs1099399410q1151549496MSMBC/T0.83 3.58×10−8TT0.165TC0.505CC0.330
rs1279144711p15.47556577PPFIBP2C/T1.23 3.59×10−8GG0.010GA0.155AA0.835
rs5826236914q23.264693912ESR2C/T0.78 6.05×10−10TT0.031TC0.216CC0.753

a According to reference 16.

b On the basis of the National Center for Biotechnology Information (NCBI) database, build 37.

c Effect/non-effect allele. OR, odds ratio; SNP, single nucleotide polymorphism.

Following adjustment for age (Table IV), GRS9 and GRS30 remained significantly associated with PCa (all P<0.01). The OR of the GRS30 for the prediction of PCa risk was 2.25 (95% CI, 1.976–2.598; P=2.97×10−31), decreased compared with that of GRS9 (OR=2.468; 95% CI, 2.053–2.967; P=6.9×10−22), although no significant differences were identified. When comparing the predictive ability of the GRS9 and GRS30, the AUCs were similar (0.792 for GRS9 vs. 0.7994 for GRS30, P=0.138).

Discussion

Genetic susceptibility is a major risk factor for PCa and is estimated to account for 42% of variation in the disease (21). In the past few years, GWAS and meta-analysis of combined data have identified 99 genomic variants associated with PCa in multiple populations of European, African-American, Japanese, Latino and Chinese ancestry (10). In the present study, 23 novel susceptibility loci detected in European ancestry or multi-ethnic analysis were investigated and their association in a Chinese population was evaluated.

Of the 19 SNPs evaluated in the present study, only 1 was identified to be associated with PCa. The estimate of risk of this SNP in the Chinese population was similar to that in European and multi-ethnic populations (10). Despite reaching genome-wide significance in European or multi-ethnic populations, the other 18 loci were not identified to be significant in the population of the present study. The discrepancy may be explained in multiple ways. First, since the effects of 18 SNPs (not including rs636291 at 1p36) were relatively low, with ORs ranging between 1.06 and 1.13, the present study may not possess the power to identify the small effects of these SNPs. This was also one of the reasons why rs7153648 did not reach a significant level following Bonferroni correction (P=0.05/19). Second, the risk allele frequencies in European and Chinese ancestry differed between SNPs evaluated (Table II); this difference may also influence the detection of significant effects of these SNPs in populations of Chinese ancestry. Finally, besides the different genetic backgrounds between European ancestry (or other populations) and Chinese ancestry, environmental factors, dietary-habit and other non-genetic factors may also affect the penetrance of these alleles, which may result in the difference of risk profiles.

When evaluating the association between the 19 SNPs and aggressive PCa (Gleason score, ≥7), the results demonstrated that rs636291 at 1p36 reached a significant level (P<0.05). This SNP reached genome-wide significance in early onset disease in European ancestry (10); however, a similar analysis could not be performed in the present study due to the lack of cases (only 34 patients with PCa were diagnosed <55 years of age). Nevertheless, this result may indicate that this risk variant was associated with more advanced PCa and should be further validated in an independent study.

In the comparison of the two GRS-based risk models, the results revealed that the performance was approximately the same between the two models. This may be attributed to the fact that certain risk variants were not strongly associated with PCa and others conferred a decreased effect to the risk of PCa in Chinese population compared with that in European whites. In a previous study, the plateau effect of PCa risk-associated SNPs was evaluated in predicting PCa in a Chinese population and it was identified that the predictive performance increased when the top 13 highest impact PCa risk-associated SNPs were included in the GRS (9). The results were similar in the present study; therefore, this may indicate that further SNPs weakly associated with PCa may not improve the predictive performance of GRS for PCa. Therefore, GRS only including the strongest SNPs may be appropriate while balancing the predictive performance and economic benefit.

In the present study, the variant rs7153648 at 14q23 that we demonstrated to be associated with PCa is located in the intergenic region of SIX homeobox 1. The regional information of the confirmed SNP (rs7153648) was presented (Fig. 1). In the LocusZoom plots of this loci, multiple SNPs located upstream of rs7153648 demonstrated marked association (P<0.01) but a weak correlation (dark blue circles), which may suggest the presence of multiple potential independent association signals. Variant rs636291 at 1p36, which was associated with early-onset PCa in European ancestry and was identified to be associated with aggressive PCa in Chinese ancestry in the current study, is located in intron 2 of peroxisomal biogenesis factor 14 and is associated with a variant (rs616488) reported in a GWAS of breast cancer (22).

There were multiple limitations to the present study. First, only 19 SNPs, rather than 23 of the novel identified loci, were genotyped or imputed due to 4 SNPs not being included in the GWAS panel and failing to impute using the CHB population of the 1,000 Genome project. Among the 4 SNPs, rs80130819 at 12q13 was not polymorphous in the CHB population, while the remaining 3 were polymorphous in the CHB population. Second, due to the open nature of the China PCa cohort, the clinical characterization of the cases was not consistent between distinct hospitals (e.g., Gleason score diagnosis in the present study), which limited further analysis of clinical phenotypes.

To conclude, by evaluating 19 PCa risk-associated SNPs identified in a large meta-analysis of GWAS from a European and multiethnic population, the results of the present study identified 1 SNP that was associated with PCa risk and 1 that was associated with aggressive PCa in a Chinese population. However, the validated small-effect SNP and other SNPs that weakly associated with PCa are not likely to improve the predictive ability of existing GRS in Chinese populations.

Acknowledgements

The present study was supported by the National Natural Science Foundation of China (grant nos. 81202269 and 81402339), the Clinical Science and Technology Innovation Project of Shanghai Shen Kang Hospital Development Center (grant no. SHDC12015105) and Scientific Research Project supported by Huashan Hospital, Fudan University (grant no. 2016QD079).

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February 2018
Volume 15 Issue 2

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Wu, Y., Chen, H., Ji, Y., Na, R., Mo, Z., Ye, D. ... Meng, W. (2018). Validation of the novel susceptibility loci for prostate cancer in a Chinese population. Oncology Letters, 15, 2567-2573. https://doi.org/10.3892/ol.2017.7602
MLA
Wu, Y., Chen, H., Ji, Y., Na, R., Mo, Z., Ye, D., Wang, M., Qi, J., Lin, X., Ding, Q., Xu, J., Zheng, S. L., Sun, Y., Meng, W."Validation of the novel susceptibility loci for prostate cancer in a Chinese population". Oncology Letters 15.2 (2018): 2567-2573.
Chicago
Wu, Y., Chen, H., Ji, Y., Na, R., Mo, Z., Ye, D., Wang, M., Qi, J., Lin, X., Ding, Q., Xu, J., Zheng, S. L., Sun, Y., Meng, W."Validation of the novel susceptibility loci for prostate cancer in a Chinese population". Oncology Letters 15, no. 2 (2018): 2567-2573. https://doi.org/10.3892/ol.2017.7602