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. Author manuscript; available in PMC: 2016 Sep 23.
Published in final edited form as: Cancer Epidemiol Biomarkers Prev. 2010 Jan 19;19(2):588–599. doi: 10.1158/1055-9965.EPI-09-0864

Single and Multivariate Associations of MSR1, ELAC2, and RNASEL with Prostate Cancer in an Ethnic Diverse Cohort of Men

Joke Beuten 1,2, Jonathan AL Gelfond 3, Jennifer L Franke 2, Stacey Shook 2, Teresa L Johnson-Pais 1, Ian M Thompson 4, Robin J Leach 1,2,4
PMCID: PMC5034730  NIHMSID: NIHMS814428  PMID: 20086112

Abstract

Three genes, namely, ELAC2 (HPC2 locus) on chromosome 17p11, 2′-5′-oligoisoadenlyate-synthetase-dependent ribonuclease L (RNASEL, HPC1 locus), and macrophage scavenger receptor 1 (MSR1) within a region of linkage on chromosome 8p, have been identified as hereditary tumor suppressor genes in prostate cancer. We genotyped 41 tagged single nucleotide polymorphisms (SNPs) covering the three genes in a case-control cohort, which included 1,436 Caucasians, 648 Hispanics, and 270 African Americans. SNPs within MSR1, ELAC2, and RNASEL were significantly associated with risk of prostate cancer albeit with differences among the three ethnic groups (P = 0.043–1.0 × 10−5). In Caucasians, variants within MSR1 and ELAC2 are most likely to confer prostate cancer risk, and rs11545302 (ELAC2) showed a main effect independent of other significant SNPs (P = 2.03 × 10−5). A major haplotype G-A-C-G-C-G combining five SNPs within MSR1 was further shown to increase prostate cancer risk significantly in this study group. Variants in RNASEL had the strongest effects on prostate cancer risk estimates in Hispanics and also showed an interaction effect of family history. In African Americans, single SNPs within MSR1 were significantly associated with prostate cancer risk. A major risk haplotype C-G-G-C-G of five SNPs within ELAC2 was found in this group. Combining high-risk genotypes of MSR1 and ELAC2 in Caucasians and of RNASEL and MSR1 in Hispanics showed synergistic effects and suggest that an interaction between both genes in each ethnicity is likely to confer prostate cancer risk. Our findings corroborate the involvement of ELAC2, MSR1, and RNASEL in the etiology of prostate cancer even in individuals without a family history.

Introduction

Prostate cancer is the most common non–skin cancer and the second leading cause of cancer death in men in the United States (1). The underlying etiology of prostate cancer remains poorly understood, with both genetic predisposition and environmental factors likely to play a role. Substantial evidence for a genetic component in the susceptibility to prostate cancer has been provided from a study on a large cohort of twins, for which the proportion of prostate cancer risk accounted for by inheritable factors was estimated to be 42% (2). Moreover, a recent study showed that both good and poor survival in prostate cancer aggregate in families, providing evidence on heritability in the prognosis of prostate cancer (3). Despite this strong evidence for a genetic component in prostate cancer, little progress has been made to identify a major gene or genes (4).

The majority of prostate cancer cases most likely involve more common, low- to moderate-penetrance alleles in genes that are components of pathways that influence prostate function, rather than mutations in high-penetrance susceptibility genes (5, 6). There is increased impetus for better understanding of the molecular processes involved in prostate carcinogenesis with the ultimate goal of discovering new biomarkers, which may be beneficial in the detection, prevention, and/or treatment of this disease.

As with breast and colon cancer, familial clustering of prostate cancer has been reported frequently (7–10). Familial prostate cancer represents families in which there are two first-degree or one first-degree and two or more second-degree relatives with prostate cancer. Familial prostate cancer is estimated to account for 10% to 20% of all cases of prostate cancer (5, 6). To date, several genome linkage analyses for prostate cancer predisposition loci have been reported (5, 6, 11), and three strong candidate genes that are involved in pathways critical to DNA damage response (ELAC2), apoptosis [2′-5′-oligoisoadenlyate-synthetase–dependent RNase L (RNASEL)], and innate immunity [macrophage scavenger receptor 1 (MSR1) and (RNASEL)] were identified in linkage-critical regions (12).

The ELAC2 gene (hereditary prostate cancer 2 locus, HPC2) at 17p11 encodes a tRNA 3′ processing endoribonuclease and was the first putative tumor suppressor gene identified for prostate cancer based on linkage analysis (13). An association between prostate cancer and two common missense variants, a serine to leucine change at amino acid 217 (Ser217Leu) and an alanine to threonine change at amino acid 541 (Ala541Thr), neither of which has been shown to alter the enzymatic activities of ELAC2 (14), has been reported in cases from families with hereditary prostate cancer (13). A meta-analysis by Camp and Tavtigian (2002) and a study by Noonan-Wheeler et al. (2006) and Stanford et al. (2003) of both variants suggested that the Thr541 allele, either alone or in combination with the Leu217 allele, confers risk for prostate cancer, in particular within sporadic cancer cases (15–17). However, subsequent studies could not unambiguously confirm a possible role of ELAC2 in the susceptibility to both sporadic and hereditary prostate cancer (6, 18).

The RNASEL gene, within the hereditary prostate cancer 1 (HPC1) locus on 1q25, mediates antiviral and proapoptotic activities of the INF-inducible 2–5A system, and is likely to host responses to infections, which may play a role in susceptibility to prostate cancer (19). Previous studies have indicated that the nonsense mutation Glu265X and the initiation codon mutation Met1Ile in the RNASEL gene segregate in prostate cancer families that were linked to the HPC1 locus (20). A truncating mutation (E265X) and an initiation-codon mutation (M1I) segregating with the disease were found in two HPC1-linked families. Functional studies showed that both mutations were associated with a reduction in RNASEL activity (20). The two most commonly studied variants within RNASEL are the nonsynonymous variants Arg462Gln and Asp541Glu, with the first showing a reduction in enzymatic activity (21). Both variants have been found to be significantly associated with prostate cancer risk albeit with between-study variability in outcome (21–23). On the other hand, several reports show a lack of association for both single nucleotide polymorphisms (SNPs) and prostate cancer, and thus the question remains as to the role of this gene in prostate cancer susceptibility.

Macrophage scavenger receptors (MSR) are trimeric membrane glycoproteins that mediate the binding, internalization, and processing of a wide range of negatively charged macromolecules, including a variety of bacteria (24). The macrophage scavenger receptor 1 (MSR1) gene, located at 8p22, has been reported as a strong candidate for prostate cancer susceptibility. Besides the positive linkage findings in hereditary prostate cancer (25), the p22 band of chromosome 8 is also found to be frequently deleted in prostate tumors (26–29). Mutations in MSR1 have been shown to be associated with prostate cancer risk in both hereditary and sporadic cases in European and African American men (30). Association studies of variants within MSR1 with prostate cancer risk show both positive and negative results (31).

In summary, numerous studies provide strong support, both functional and epidemiologic, that ELAC2, MSR1, and/or RNASEL confer risk for prostate cancer, yet other studies have suggested that their role may be small. Understanding the role of these three putative prostate cancer susceptibility genes needs more thorough evaluation and replication. In most studies only a small proportion of the estimated number of genetic variants was analyzed, and the contributions of variants in regulatory, noncoding regions of genes, rather than in exons, were often omitted. Moreover, except for our previous study on the association of two SNPs within RNASEL (23), Hispanics have not been extensively analyzed, and no association results for ELAC2 or MSR1 in Hispanics are currently available. We therefore carried out an association analysis with haplotype-tagged SNPs covering the whole genes in a sample consisting of three ethnic/racial groups. We determined the effects of single SNPs and also considered possible interactions. This is the first study to explore these three genes extensively.

Materials and Methods

Subjects

Study subjects included men in the San Antonio Center for Biomarkers of Risk of Prostate Cancer (SABOR) cohort. SABOR is funded by the National Cancer Institute and has been prospectively enrolling healthy male volunteers from 2001. On each annual visit, a digital rectal examination was done and serum prostate specific antigen level was determined. From this cohort, 226 incident cases (131 non-Hispanic Caucasians, referred to as Caucasians in the text; 59 Hispanic Caucasians, referred to as Hispanics; and 36 African Americans) were available. We also included 646 cases with a known history of prostate cancer that are enrolled within the same time period in a parallel study of prevalent prostate cancer using the same recruiting strategies. Institutional Review Board approval was obtained, as was informed consent from subjects in both studies. Cases had biopsy-confirmed prostate cancer and controls consisted of male volunteers ≥45 y old who had normal digital rectal examinations and prostate specific antigen levels <2.5 ng/mL on all study visits. Race/ethnicity was self-reported on a questionnaire completed at the time of enrollment. A total of 1,436 Caucasians (596 cases, 840 controls), 648 Hispanics (194 cases, 454 controls), and 270 African Americans (82 cases, 188 controls) were included in this analysis. The clinical characteristics of subjects are summarized in Table 1. Study age among controls was the age at last follow-up, and age among cases was the age at prostate cancer diagnosis; controls were younger than prostate cancer cases, with mean age (SD) of 61.3 (9.2) y and 65.5 (8.5) y, respectively (P < 0.0001). Because of this difference and the fact that prostate cancer risk increases with age, all the odds ratios (OR) were adjusted for age. First-degree relatives include father, full brother(s) and child, and for second-degree relative we considered both maternal and paternal grandfathers and uncles.

Table 1.

Clinical data of the study group

Subgroup Cases (n = 872)
Controls (n = 1,482)
No. (%) No. (%)
Ethnic background
 Caucasian 596 (68.4) 840 (56.7)
 Hispanic 194 (22.2) 454 (30.6)
 African American 82 (9.4) 188 (12.7)
Age, in y
 46–50 35 (4.0) 199 (13.4)
 51–60 212 (24.3) 536 (36.1)
 61–70 378 (43.4) 485 (32.7)
 >70 247 (28.3) 262 (17.8)
 Mean ± SD 65.5 ± 8.5 61.3 ± 9.2 P < 0.0001
Family history* 95 398
 1st-degree relative 74 300
  Brother 37 79
  Father 52 247
 2nd-degree relative 97 151
Age onset sporadic 66.3 ± 8.4 P < 0.0001
Age onset familial 63.9 ± 8.4
Disease aggressiveness (Gleason)
 Total <7 332
 Sporadic <7 215
 Familial <7 117
 Total ≥7 247
 Sporadic ≥7 163
 Familial ≥7 84
Prostate specific antigen (ng/mL)
 ≤4.0 180 1482
 4.1–10.0 36 0
 10.1–20.0 3 0
 >20.0 4 0
 Mean ± SD 3.16 ± 4.72 0.86 ± 0.44 P < 0.0001
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*

Family history data are from the SABOR cohort only.

SNP Selection and Genotyping

DNA was isolated from whole blood cells using a QIAamp DNA Blood Maxi Kit (Qiagen). Forty-one SNPs spanning the three genes were selected using Haploview. We first selected SNPs from available databases, National Center for Biotechnology Information5 and SNPper,6 using the following criteria: (a) within each gene, SNPs with a minor allele frequency (MAF) >0.05 that leads to an amino acid substitution and/or are in other coding regions of the gene and thus potentially functionally important were selected, and (b) SNPs for which an association with prostate cancer has previously been shown as reported in the literature were chosen. After this initial selection, we identified tagging SNPs within each gene using Haploview with the following criteria: (a) a MAF >0.05 to gain more statistical power; (b) an r2 threshold of 0.8 and a log of odds threshold for multimarker testing of 3.0; (c) a minimum distance between tags of 60 basepairs; (d) we included our preselected SNPs (see above), (e) for each gene the search for SNPs extended to a 10 kilobase region surrounding the gene, and (f) we used the 2- and 3-marker haplotype tagging option.7 The selection was based on the information on the European population as provided by HapMap.8 The SNPs are described in Table 2. Genotyping of 39 SNPs was done with the Goldengate assay of the VeraCode technology using the BeadXpress Reader System according to the manufacturer’s protocol (Illumina). Two SNPs within RNASEL (rs627928/Asp541Glu and rs486907/Arg462Gln) were genotyped as previously described (23). To ensure reliability of the results, duplicate samples were included in the analysis as quality controls.

Table 2.

Genes, SNP selection, their location, minor allele frequencies in cases and controls of each ethnic/race group

Gene SNP Position Function SNP Minor Allele Caucasians
Hispanics
African Americans
MAF case MAF control P* MAF case MAF control P* MAF case MAF control P*
RNASEL rs17568993 chr1:180804117 A 0.126 0.135 0.503 0.08 0.061 0.226 0.073 0.117 0.129
rs12757998 chr1:180805101 A 0.284 0.309 0.149 0.198 0.198 0.999 0.177 0.139 0.270
rs635261 chr1:180805664 C 0.383 0.37 0.484 0.402 0.406 0.901 0.287 0.262 0.569
rs10911099 chr1:180806772 G 0.115 0.116 0.934 0.085 0.062 0.154 0.049 0.028 0.232
rs1048260 chr1:180809474 3′UTR G 0.296 0.287 0.628 0.304 0.295 0.752 0.213 0.194 0.621
rs11072 chr1:180809954 3′UTR G 0.302 0.297 0.768 0.31 0.304 0.827 0.216 0.201 0.692
rs1048254 chr1:180810289 3′UTR C 0.298 0.293 0.756 0.265 0.266 0.987 0.213 0.207 0.865
rs533259 chr1:180815642 A 0.069 0.064 0.619 0.028 0.043 0.211 0.213 0.238 0.548
rs627928 chr1:180817960 Glu541Asp G 0.543 0.553 0.678 0.513 0.467 0.244 0.329 0.314 0.767
rs516134 chr1:180820316 G 0.03 0.032 0.703 0.008 0.018 0.174 0.152 0.198 0.223
rs486907 chr1:180821180 Gln462Arg A 0.351 0.337 0.528 0.313 0.244 0.052 0.236 0.119 0.004
rs3738579 chr1:180822659 5′UTR G 0.343 0.338 0.759 0.265 0.216 0.061 0.134 0.133 0.965
rs682585 chr1:180826133 A 0.382 0.39 0.673 0.446 0.486 0.196 0.146 0.108 0.220
MSR1 rs918 chr8:16011449 3′UTR A 0.055 0.057 0.792 0.098 0.107 0.637 0.189 0.148 0.247
rs1904577 chr8:16016055 G 0.126 0.128 0.843 0.273 0.302 0.315 0.396 0.444 0.310
rs11780669 chr8:16018641 G 0.083 0.095 0.247 0.039 0.028 0.325 0.012 0.028 0.273
rs12114368 chr8:16025334 A 0.035 0.036 0.832 0.169 0.208 0.117 0.08 0.071 0.713
rs12681382 chr8:16029033 G 0.029 0.031 0.714 0.102 0.129 0.178 0.018 0.019 0.986
rs2127565 chr8:16030930 G 0.119 0.131 0.355 0.29 0.312 0.445 0.644 0.614 0.528
rs4333601 chr8:16042345 3′UTR C 0.234 0.239 0.745 0.418 0.443 0.412 0.433 0.54 0.025
rs12718376 chr8:16042516 3′UTR A 0.107 0.153 0.002 0.301 0.235 0.092 0.46 0.524 0.430
rs17484273 chr8:16044606 A 0.315 0.335 0.255 0.284 0.299 0.584 0.207 0.207 0.989
rs17484315 chr8:16055103 C 0.043 0.047 0.606 0.008 0.009 0.843 0.012 0 0.046
rs3747531 chr8:16057019 Ala275Pro G 0.047 0.058 0.216 0.142 0.179 0.104 0.073 0.075 0.957
rs351572 chr8:16065839 G 0.438 0.404 0.067 0.265 0.239 0.327 0.341 0.256 0.049
rs754331 chr8:16067989 A 0.465 0.484 0.310 0.363 0.361 0.926 0.256 0.247 0.825
rs13251251 chr8:16073863 A 0.064 0.06 0.655 0.031 0.031 0.977 0 0.006 0.313
rs614794 chr8:16085228 G 0.12 0.119 0.894 0.365 0.398 0.277 0.366 0.358 0.865†
rs3789015 chr8:16087084 G 0.039 0.045 0.461 0.137 0.177 0.079 0.079 0.08 0.970
rs6530946 chr8:16099299 G 0.14 0.158 0.335 0.392 0.442 0.132 0.594 0 0
ELAC2 rs2072262 chr17:12833668 G 0.129 0.116 0.315 0.104 0.111 0.694 0.134 0.167 0.349
rs2072261 chr17:12833814 A 0.237 0.239 0.871 0.227 0.25 0.381 0.119 0.108 0.724
rs2523 chr17:12836540 3′UTR G 0.349 0.345 0.805 0.381 0.402 0.497 0.476 0.515 0.406
rs1044564 chr17:12836709 3′UTR G 0.35 0.348 0.917 0.381 0.401 0.514 0.481 0.534 0.275
rs17552022 chr17:12839020 Thr631Thr G 0.122 0.091 0.018 0.077 0.059 0.291 0.013 0.01 0.826
rs11545302 chr17:12840688 Thr520Thr G 0.268 0.189 9.1 × 10−6 0.259 0.263 0.888 0.2 0.068 2.0 × 10−4
rs11658321 chr17:12855209 A 0.35 0.341 0.604 0.41 0.408 0.957 0.665 0.698 0.459
rs2051974 chr17:12862370 A 0.233 0.242 0.601 0.22 0.198 0.382 0.427 0.466 0.411
rs8077923 chr17:12864712 C 0.14 0.158 0.194 0.179 0.18 0.964 0.201 0.176 0.496
rs7218504 chr17:12868379 C 0.309 0.315 0.737 0.302 0.27 0.253 0.39 0.41 0.676
rs12943765 chr17:12868955 G 0.059 0.048 0.201 0.062 0.039 0.087 0.055 0.049 0.795
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NOTE: Significant P values are in bold.

*

Assumes Hardy-Weinberg equilibrium.

†

SNP not in Hardy-Weinberg equilibrium (P < 0.01).

Statistics

Haploview version 4 beta 15 was used to check for Hardy-Weinberg equilibrium for each SNP and to measure linkage disequilibrium (LD) between the SNPs in the controls and cases of each race/ethnicity (32).9

The allele frequency for each SNP was determined in each ethnic group, and the frequencies among the case-control groups were compared using the χ2 test. Association analyses were stratified by ethnicity and done using R statistical software version 2.9.1. The OR and its 95% confidence interval (95% CI) were estimated by unconditional logistic regression as a measure of the associations between genotypes and prostate cancer risk. We tested for additive, dominant, and recessive associations. The model with the strongest association was chosen for presentation (i.e., model with smallest P value with ≥5 individuals). To correct for multiple testing, we used the method of Storey and Tibshirani (2003) based on the concept of false discovery rate (33). This estimation of the false discovery rate showed that for P < 0.05, the probability that the association is expected to be a true positive in our sample group is >70% (i.e., the false discovery rate is <30%). To estimate the independent effect of a significant SNP while adjusting for other SNPs, we used a generalized linear model function from the R statistical package so that all SNPs are entered into a single multivariable logistic regression model. SNPs in this model were taken to have additive effects.

Relative risk (RR) ratios for family history, including first- (father and full brother) and second-degree (grandfather and uncle) relatives affected with cancer, were calculated in the samples from the SABOR cohort only using Fisher’s exact test. To test whether family history (first degree and second degree) modulated the effects of genotypes on prostate cancer risk, a likelihood ratio test on the interaction term between family history and genotype was done. The magnitude of any effect modification was described using parameters obtained in logistic regression stratified by family history.

The cumulative effect of combined genotypes on prostate cancer risk was estimated by counting the number of genotypes associated with prostate cancer, on the basis of the best-fitting genetic inheritance from single-SNP analysis. ORs and their 95% CIs were calculated for men carrying any combination of one, two, or more alleles associated with prostate cancer as compared with men carrying none of the risk genotypes using unconditional logistic regression analysis. We also fit models that estimated the cumulative effect of family history on prostate cancer risk in addition to the risk alleles determined above in an unconditional logistic regression. We selected SNPs that were not in LD with each other (D′ < 0.8). If several SNPs presented higher LD values, we choose a SNP in a coding region above an intronic SNP, and also selected the most significant SNP.

Logistic regression was used to calculate the ORs of the haplotypes, using the method implemented in the haplo. ccs package (34) where the OR of each major haplotype was computed relative to a reference group consisting of all other haplotypes, including rare haplotypes. Only major haplotypes (estimated frequency >5%) are considered in this report. Three genetic models (additive, dominant, and recessive) were tested. For all statistical analyses, age was used as covariate. Individuals with missing data for a particular analysis were removed from the analysis. Because of the small sample sizes for prostate cancer men with first- or second-degree relatives, we restricted the analysis in this report to the family history data, which include both subgroups. All statistical tests were two-sided and significance was set at P < 0.05.

Results

All SNPs were in Hardy-Weiberg equilibrium (P > 0.01) in the controls of each ethnic/racial group, except for SNP rs614794, which showed a deviation from Hardy-Weiberg equilibrium in African American controls. This SNP was omitted for further statistical analysis in this study group. Table 2 shows the MAF of the SNPs estimated in all three ethnicities. Significant case/control differences of allele frequencies at a level <0.05 were observed for three and five polymorphisms in Caucasians and African Americans, respectively.

Five SNPs (three in MSR1 and two in ELAC2) were significantly associated with prostate cancer risk in Caucasians (P values 0.043–0.0001). The strongest association, considering both the level of significance and the magnitude of OR, was seen for rs12718376 in MSR1 and rs11545302 in ELAC2 (OR, 0.32; 95% CI, 0.12–0.90; P = 0.031, and OR, 2.19; 95% CI, 1.25–3.82; P = 0.006, respectively; Table 3). SNP rs12718376 is located within the 3′ untranslated region of MSR1 and the two SNPs in ELAC2, rs17552022 and rs11545302, are located within exonic regions of the gene but do not result in amino acid changes. In Hispanics three SNPs within RNASEL and one SNP within MSR1 were found to be significantly associated with prostate cancer risk (P values 0.03–0.003). In addition to the two previously reported nonsynonymous SNPs, rs627928 (Asp541Glu) and rs486907 (Arg462Gln; ref. 23), rs682585 was also found to be significant in this ethnic group. In African Americans, rs4333601 and rs351572, both located within MSR1, showed a significant association with prostate cancer (P values 0.039–0.024). Significance for SNP rs351572 (MSR1) was found in both Caucasians and African Americans. All SNPs that were significantly associated with prostate cancer remained significant after adjusting for multiple comparisons. After conditioning on other significant SNPs not in LD with each other, rs11545302 (ELAC2) showed a main effect independent of other significant SNPs in Caucasians (P = 2.03 × 10−5), whereas no significant independent associations were found for Hispanics or African Americans.

Table 3.

Significant results from individual SNP effects on prostate cancer in Caucasians, Hispanics, and African Americans after correction for multiple testing

Gene SNP Genotype Controls (n) Cases (n) OR* (95% CI) P
Caucasians
MSR1 rs12718376 GG 484 329 1.00
AA 18 5 0.32 (0.12–0.90) 0.031
AG 170 78 0.68 (0.50–0.92) 0.014
AA/AG vs GG 188 83 0.64 (0.48–0.86) 0.004
MSR1 rs17484273 GG 371 263 1.00
AA 95 44 0.66 (0.45–0.98) 0.041
AG 372 285 1.06 (0.85–1.33) 0.590
AA vs AG/GG 95 44 0.64 (0.44–0.94) 0.022
MSR1 rs351572 AA 306 175 1.00
GG 145 102 1.26 (0.91–1.73) 0.159
AG 388 315 1.40 (1.10–1.78) 0.007
GG/AG vs AA 533 417 1.36 (1.08–1.71) 0.009
ELAC2 rs17552022 AA 443 427 1.00
GG 5 9 1.92 (0.63–5.83) 0.251
AG 87 117 1.38 (1.01–1.88) 0.043
GG/AG vs AA 92 126 1.41 (1.04–1.91) 0.027
ELAC2 rs11545302† AA 356 311 1.00
GG 21 41 2.19 (1.25–3.82) 0.006
AG 161 229 1.67 (1.29–2.16) 1.0 × 10−4
GG/AG vs AA 182 270 1.73 (1.36–2.22) 1.0 × 10−5
Hispanics
RNASEL rs627928 TT 59 41 1.00
GG 48 45 1.49 (0.83–2.69) 0.186
GT 120 70 0.80 (0.48–1.33) 0.390
GG vs GT/TT 48 45 1.72 (1.05–2.81) 0.030
RNASEL rs486907 GG 126 75 1.00
AA 7 17 4.18 (1.61–10.85) 0.003
AG 91 64 1.16 (0.75–1.81) 0.508
AA vs AG/GG 7 17 3.92 (1.54–9.96) 0.004
RNASEL rs682585 GG 96 64 1.00
AA 85 43 0.72 (0.43–1.20) 0.207
AG 210 87 0.52 (0.34–0.80) 0.003
AA/AG vs GG 295 130 0.58 0.38–0.86 0.007
MSR1 rs12114368 GG 246 139 1.00
AA 20 12 0.97 (0.44–2.14) 0.940
AG 121 41 0.58 (0.38–0.90) 0.015
AA/AG vs GG 141 53 0.64 (0.43–0.95) 0.029
African Americans
MSR1 rs4333601 CC 46 14 1.00
AA 33 25 2.57 (1.13–5.83) 0.024
AC 83 43 1.74 (0.84–3.58) 0.134
A # vs C # 162 82 1.59 (1.06–2.39) 0.024
MSR1 rs351572 AA 89 33 1.00
GG 10 7 2.32 (0.78–6.84) 0.128
AG 63 42 1.84 (1.03–3.28) 0.039
GG/AG vs AA 73 49 1.90 (1.09–3.32) 0.025
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NOTE: Significant P values are in bold.

*

Age adjusted.

†

Main effect independent from other significant SNPs.

Of the 226 incident cases, 95 (42%) had a positive family history. A positive family history of prostate cancer, including both first- and second-degree relatives, showed a significant increase in relative risk (RR, 1.79; 95% CI, 1.40–2.27; P < 0.0001). For a man suffering from prostate cancer with a first-degree relative affected with prostate cancer the RR is 1.84 (95% CI, 1.41–2.37; P < 0.0001), which is slightly higher compared with the risk of having a second-degree relative affected with prostate cancer (RR, 1.71; 95% CI, 1.20–2.38; P = 0.005). When more than one first-degree relative is affected with prostate cancer, the risk increases 2-fold (RR, 1.99; 95% CI, 1.13–3.40; P = 0.03). We have to mention that these results are based on a small number of samples within the cohort and thus need to be interpreted with caution (Table 1). Logistic regression including both age and family history as covariates, however, did not change the risk estimates for cancer as compared with an age-only adjusted analysis. Adding family history as interaction term in the logistic regression showed that several SNPs within RNASEL had a significant interaction effect in Hispanics (Table 4). A stratified analysis by family history further indicated that significant associations were only found in the group with family history, which corroborates the findings of the interaction model. In addition, an increase in effect size was observed for all SNPs except rs627928. No major effects with family history were found in the Caucasians or African Americans.

Table 4.

Risk estimates of variants in RNASEL for cancer by interaction effects of family history (left) and family history stratification (right) in Hispanics

SNP Genotype Interaction model
Genotype Stratified: no family history
Stratified: family history
Controls/Cases (n) OR (95% CI) P Controls/Cases (n) OR (95% CI) P Controls/Cases (n) OR (95% CI) P
rs12757998 GG 258/126 Ref 213/88 Ref 45/38 Ref
AG 114/59 1.41 (0.88–2.25) 0.149   77/47 1.41 (0.88–2.25) 0.148 37/12 0.33 (0.14–0.77) 0.011
AA/AG vs GG 135/68 1.30 (0.83–2.04) 0.246   93/52 1.30 (0.83–2.03) 0.246 42/16 0.41 (0.19–0.88) 0.023
AG*GxE 37/12 0.24 (0.09–0.62) 0.008
AA/AG vs GG *GxE 135/68 0.32 (0.13–0.76) 0.010
rs635261 GG 140/69 Ref 107/55 Ref 33/14 Ref
CC 66/31 0.77 (0.41–1.45) 0.421   61/21 0.77 (0.41–1.44) 0.416 5/10 6.22 (1.67–23.2) 0.006
CC vs CG/GG 66/31 0.83 (0.47–1.48) 0.534   61/21 0.83 (0.47–1.47) 0.527 5/10 4.58 (1.40–15.0) 0.012
CC*GxE 5/10 7.81 (1.86–32.9) 0.016
CC vs CG/GG*GxE 66/31 5.39 (1.46–19.9) 0.009
rs1048260 CC 191/94 Ref 148/75 Ref 43/19 Ref
CG 172/82 0.77 (0.49–1.20) 0.251 134/54 0.77 (0.49–1.20) 0.252 38/28 2.07 (0.94–4.54) 0.069
GG/CG vs CC 202/100 0.76 (0.50–1.17) 0.210 158/65 0.76 (0.50–1.17) 0.212 44/35 2.18 (1.03–4.63) 0.042
CG*GxE 38/28 2.65 (1.09–6.46) 0.049
GG/CG vs CC *GxE 202/100 2.82 (1.20–6.62) 0.016
rs11072 AA 183/90 Ref 142/72 Ref 41/18 Ref
AG 173/85 0.82 (0.53–1.29) 0.394 135/56 0.82 (0.53–1.29) 0.393 38/29 2.03 (0.92–4.46) 0.078
GG/AG vs AA 204/102 0.79 (0.51–1.21) 0.273 160/66 0.79 (0.51–1.21) 0.275 44/36 2.13 (1.00–4.54) 0.049
AG*GxE 38/29 2.43 (0.99–5.94) 0.059
GG/AG vs AA *GxE 204/102 2.66 (1.13–6.28) 0.024
rs1048254 AA 208/103 Ref 159/82 Ref 49/21 Ref
AC 161/79 0.80 (0.51–1.25) 0.326 126/51 0.80 (0.51–1.25) 0.325 35/28 1.93 (0.90–4.11) 0.090
CC/AC vs AA 185/91 0.76 (0.50–1.17) 0.215 147/58 0.76 (0.50–1.17) 0.215 38/33 2.14 (1.03–4.46) 0.042
AC*GxE 35/28 2.40 (1.00–5.76) 0.022
CC/AC vs AA *GxE 185/91 2.80 (1.20–6.52) 0.016
rs627928* TT 59/41 Ref GG 40/28 Ref 19/16 Ref
GT 120/69 1.05 (0.57–1.96) 0.866 GT 90/58 0.83 (0.45–1.53) 0.559 30/11 0.40 (0.15–1.08) 0.071
GG vs GT/TT 48/45 1.21 (0.68–2.15) 0.519 TT vs GT/GG 40/25 0.90 (0.50–1.62) 0.715 8/17 4.40 (1.63–11.9) 0.003
GT*GxE 30/11 0.37 (0.12–1.20) 0.013
GG vs GT/TT *GxE 48/45 3.85 (1.23–12.0) 0.018
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NOTE: Significant P values are in bold.

Abbreviation: GxE, Interaction gene-environment.

*

SNP significant in single-SNP analysis.

Age-adjusted multivariate logistic regression of combinations of risk alleles for SNPs not in LD with each other compared with no risk alleles as reference, showed a cumulative effect for SNPs rs351572 and rs11545302 in Caucasians (OR, 2.31; 95% CI, 1.64–3.26; Ptrend = 1.73 × 10−6). In Hispanics, the combination of three risk alleles for SNPs rs486907, rs682585, and rs12114368 showed a significant association with prostate cancer (Ptrend = 0.015) and a 3.31-fold increase in risk (95% CI, 1.26–8.71; Table 5). No cumulative effect of both significant SNPs in African Americans was observed. For the analysis, we selected significant SNPs not in LD with each other and chose the most significant and/or functional SNP. Of note, however, is that for Caucasians, the three-SNP combination, including rs12718376, rs351572, and rs11545302 with rs12718376 and rs351572 being in LD, showed an even stronger cumulative effect with a >4-fold increase in OR (OR, 4.05; 95% CI, 2.09–7.87; P = 3.66 × 20−5; data not shown). After adding the presence of family history and checking for risk estimates of prostate cancer in men carrying a combination of multiple risk alleles and also having a family history, the OR slightly increased in Caucasians (from 2.31 to 2.47) and slightly decreased in Hispanics (from 3.31 to 3.15; Table 5).

Table 5.

Cumulative effects of risk variants

Markers Number of risk genotypes Controls Cases OR (95% CI)* P
Caucasians
rs351572, rs11545302 0 136 99 Ref –
1 275 272 1.36 (0.99–1.86) 0.055
2 127 208 2.27 (1.61–3.21) 3.29 × 10−6
Trend 2.31 (1.64–3.26) 1.73 × 10−6
 Add family history Trend 2.47 (1.75–3.49) 2.88 × 10−7
Hispanics
rs486907, rs682585, rs12114368 0 39 25 Ref –
1 90 89 1.71 (0.93–3.16) 0.085
2 32 31 1.75 (0.84–3.67) 0.137
3 2 10   8.5 (1.63–44.26) 0.011
Trend 3.31 (1.26–8.71) 0.015
 Add family history Trend 3.15 (1.38–7.20) 0.007
Open in a new tab

NOTE: Significant P values are in bold.

*

Age adjusted.

Haplotype analysis of SNPs not in LD within each of the three genes showed a major haplotype (39%) G-A-C-G-C-G for the SNPs rs918-rs1904577-rs2127565-rs12718376-rs3747531-rs351572 within MSR1 that significantly increased the risk for prostate cancer in Caucasians under the dominant model (OR, 1.58; 95% CI, 1.23–2.04; P = 4.02 × 10−4; Table 5). In African Americans, the major haplotype C-G-G-C-G (6%) for SNPs rs2072262-rs2523-rs11545302-rs8077923-rs7218504 within ELAC2 is significantly associated with disease risk with an OR of 3.65 (95% CI, 1.38–9.68; P = 0.009) under the additive model (Table 6).

Table 6.

Association of common haplotypes with prostate cancer risk in Caucasian and African American men

SNP combination Freq No. of haplotypes OR* (95% CI) P
Caucasians† Cases Controls
MSR1: rs918-rs1904577-rs2127565-rs12718376-rs3747531-rs351572
G-A-C-G-C-G 39% 259 392 1.58 (1.23–2.04) 4.02 × 10−4
G-A-C-G-C-A 31% 208 346 1.10 (0.86–1.40) 0.443
G-A-C-A-C-A 8% 44 118 0.63 (0.44–0.90) 0.010
African Americans‡
ELAC2: rs2072262-rs2523-rs11545302-rs8077923-rs7218504
C-A-A-A-C 30% 35 58 0.76 (0.48–1.20) 0.235
C-G-A-A-G 24% 29 51 0.64 (0.38–1.07) 0.090
C-A-A-A-G 17% 28 30 1.23 (0.73–2.08) 0.435
C-G-G-C-G 6% 15 6 3.65 (1.38–9.68) 0.009
G-G-A-A-G 5% 6 13 0.59 (0.21–1.65) 0.314
Open in a new tab

NOTE: Significant results after Bonferroni correction are in bold (P < 0.017 and P < 0.01 in Caucasians and African Americans, respectively).

Only common haplotypes (>5%) are shown.

*

OR is age adjusted.

†

Dominant model.

‡

Additive model.

Discussion

Substantial evidence exists indicating that the etiology of prostate cancer involves the interplay among genetic, environmental, and dietary factors. Whereas several of the risk factors are merely the result of individual choices and thus modifiable (e.g., diet, exposure to UV radiation, tobacco use), some major risk factors for prostate cancer are determined and unchangeable, including age, ethnicity, and family history. Finding which and to what extent such factors confer increased risk of prostate cancer has been a burden and major challenge for researchers over the last several years.

We studied three candidate susceptibility genes, ELAC2 on chromosome 17p11/HPC2 region, RNASEL within the HPC1 region, and MSR1 within a region of linkage on chromosome 8p, that have been previously suggested to play a role in hereditary prostate cancer. Forty-one tagged SNPs covering each of the three genes were genotyped in a case-control cohort consisting of 1,436 Caucasians (596 cases, 840 controls), 648 Hispanics (194 cases, 454 controls), and 270 African Americans (82 cases, 188 controls). Single-SNP analysis showed that SNPs within MSR1 were significantly associated in all three ethnicities (P = 0.04–0.004), with rs351572 being in common between Caucasians and African Americans. None of the significant SNPs within MSR1 found in this study have been reported previously. Of interest is that SNP rs433601, significant in African Americans and located in the 3′untranslated region of the gene, has an allele-specific alteration of an exon splicer enhancer binding site according to PupaSuite. Moreover, a major G-A-C-G-C-G haplotype for the SNP combination rs918-rs1904577-rs2127565-rs12718376-rs3747531-rs351572 showed a significant increase in prostate cancer risk in Caucasians. The nonsynonymous SNP rs3747531, included in this haplotype, results in an alanine to proline change, which, according to Polyphen, has a damaging effect.10 However, no evidence has been shown for possible phenotypic effects of the allelic variation for this SNP. The majority of previous studies did not find associations of variants within MSR1 and prostate cancer (35–37), although Hsing et al. (2007) reported on significant associations between MSR1 variants and prostate cancer in Chinese (38). The lack of positive association findings for MSR1 variants could be explained by the fact that only a few SNPs were investigated per study, in particular coding SNPs, underestimating the importance of noncoding intronic SNPs. Alternatively, (geographic) differences in population structures, and/or insufficient power to detect single SNP associations for some studies due to small sample sizes could explain between-study differences in association results. This study shows that, in addition to coding SNPs, noncoding intronic SNPs within MSR1 play a role in determining susceptibility to prostate cancer and are part of high-risk haplotypes. Moreover, a potential role of MSR1 in the susceptibility to prostate cancer is suggested in the three ethnicities studied, albeit with subgroup differences in significance of SNPs likely due to population-specific allele frequencies and LD structure. Studies in animals have shown that mutations in MSR1 increase the likelihood of bacterial infections. Therefore, our findings support a previous hypothesis that infection and prostate cancer could be linked (39).

This study also found two synonymous SNPS (rs11545302/Thr520Thr in exon 17 and rs17552022/Thr631Thr in exon 20) within ELAC2 that showed significant risk effects on prostate cancer in Caucasians. SNP rs11545302 further showed an independent effect from other significant SNPs in this group (P = 0.0001). Although two recent genome-wide association studies have found several regions to be implicated in prostate cancer risk in Europeans, no significance was found for rs11545302 (40, 41). Both studies used the HumanHap300 and HumanHap240 panels from Illumina for the analysis which does not contain rs11545302. Nonetheless, previous studies report negative findings for association of single SNPs within ELAC2 in Caucasians, which is in contrast with our findings (18, 42). On the other hand, positive associations were also found in Japanese men (43, 44) and African Americans (45), with the latter consistent with our results. Although not significant for the single-SNP analysis, a major C-G-G-C-G haplotype for SNPs rs2072262-rs2523-rs11545302-rs8077923-rs7218504 showed a significant increase in risk for prostate cancer in African Americans. This risk haplotype contains a SNP (rs2523) that is located within a microRNA binding site (miR-648) at the 3′ untranslated region of the ELAC2 gene (information retrieved from PupaSuite). Currently there are no reports that describe possible functions of this microRNA. Moreover, the function of ELAC2 is unknown but the gene is believed to play a role in cell cycle progression. Consequently, it remains to be determined to what extent variants within ELAC2 confer increased risk of prostate cancer.

Three SNPs within RNASEL showed a significant association with prostate cancer risk in Hispanics. These findings conform with our previous results showing a significant increase in prostate cancer risk for two nonsynonymous SNPs, rs627928 (Asp541Glu) and rs486907 (Arg462Gln; ref. 23). The allelic variant at position 462 (Arg462Gln), which reduces RNASEL enzymatic activity 3-fold, is associated with an increase in risk of prostate cancer as found in Hispanics in this study and other previous studies (23, 39). Our findings further showed a significant association between rs682585, located just upstream of RNASEL, and prostate cancer risk in Hispanics. An association between prostate cancer and rs682585 has not been reported previously. A viral etiology for prostate cancer has been suggested from recent findings, including the observation that a novel retrovirus, the xenotropic murine leukemia-related virus, was frequently found in prostate tissue of men with the Arg462Gln allelic variant (39, 46, 47). It was further shown that RNASEL-deficient cells and animals are more susceptible to viral infections (48). Therefore, RNASEL was suggested to be implicated in the suppression of xenotropic murine leukemia-related virus infections of the prostate.

A positive family history is a well-established and important epidemiologic risk factor for prostate cancer, and our findings corroborate a previous meta-analysis on the increased family-history associated risk for prostate cancer. A risk ratio of 1.8 for first-degree relatives found in our sample was higher than the RR of 1.53 found by Roemeling et al. (2006) but lower than reported in the meta-analysis by Noe et al. (2008; RR range between 2.2 and 2.5; refs. 49, 50). This could be explained by the smaller number of participants in our study as compared with the meta-analysis and/or because we did not stratify the analysis by ethnicity. However, previous reports showed that the increased risk of prostate cancer in family members is similar among Caucasians, Hispanics, and African-Americans within the United States (51–53). On the other hand, the RR, being 1.7 for second-degree relatives, is similar to the meta-analysis by Noe et al. (2008; RR between 1.68 and 1.88; ref. 50). In general, the relative risk of prostate cancer increases markedly with increasing number of affected relatives suggesting a genetic component of prostate cancer. Incorporation of family history into our model did not dramatically change the results in Caucasians and African Americans. In Hispanics, however, several SNPs within RNASEL showed an interaction effect of family history.

Combining the two high-risk genotypes of MSR1 and ELAC2 in Caucasians and the three high-risk genotypes of RNASEL and MSR1 in Hispanics showed synergistic effects, and individuals with multiple-risk genotypes are at higher risk as compared with individuals with a single high-risk genotype. From these findings one could assume that an interaction between both genes in each ethnicity is likely to confer prostate cancer risk. Although a biological explanation awaits further experimental studies, the function of these genes in cellular defense against inflammation and oxidative stress is supportive of a possible interaction between these genes, which also corroborates previous suggestions that infection and prostate cancer could be linked.

A possible limitation of the study is that 13.4% of our control group was between 45 and 50 years old compared with 4% of our cases in this age range. This is a limitation because the average age of men diagnosed with prostate cancer is over the age of 60 years, and according to the American Cancer Society two thirds of prostate cancers are found in men over the age of 65. In our heavily screened population, however, we noted that 50% of the cancers had a diagnosis before the age of 65 years. Furthermore, for the analyses in this study we adjusted statistically for age difference. In addition, the presence of potential cases in the control group will merely result in an underestimation of the effect of significant associations. Another limitation of our study is that selection of the tagged SNPs was based on HapMap data of the European population. Due to the ethnic-specific LD patterns, these SNPs selected may not fully represent all tagged variants in Hispanics and/or African Americans. Furthermore, the power of this study is limited by the sample size (2,354 in total, with 1,435 Caucasians, 648 Hispanics, and 270 African Americans), the MAF, the baseline incidence of disease (~6%), and the unknown OR of a genetic risk factor. Assuming a type I error of 0.05, an OR of 1.5, and a MAF of 20%, we estimated the power of the study with the method of Slager and Schaid to be 99%, 75%, and 38% in Caucasians, Hispanics, and African Americans, respectively. Even with these weaknesses, however, our findings indicate that variants within ELAC2, RNASEL, and MSR1 play a significant role in the susceptibility to prostate cancer risk. We did not report on the risk effects of the investigated SNPs on Gleason grade (Gleason score ≥7 versus <7) or prognosis (defined as Gleason score of ≥7 or stage T3b or higher) due to the small number of cases with information on the trait of interest. However, a case only logistic regression analysis showed that in Caucasians variants in RNASEL and ELAC2 could be involved in Gleason grade and prognosis, respectively. A trend towards significance for SNPs within MSR1 was seen for Gleason grade and within both RNASEL and MSR1 for prognosis in Hispanics. There results have to be considered with caution due to the number of cases. The sample size of the African Americans was too small for data analysis.

In summary, this is the first association study to cover the three susceptibility genes for prostate cancer with haplotype-tagged SNPs. Our results show that variants in ELAC2, RNASEL, and MSR1 play a role in the development of prostate cancer albeit with ethnic-specific differences in risk estimates. Our findings suggest that interactions among these genes likely confer prostate cancer risk consistent with a polygenic model for cancer susceptibility. Moreover, a function of these genes in cellular response to inflammation corroborates the hypothesis of a link between infection and etiology of prostate cancer.

Acknowledgments

The participation of all study subjects in SABOR and in the prevalent prostate cancer studies at the University of Texas Health Science Center at San Antonio is gratefully acknowledged. The study could not have been accom-plished without the skilled assistance of the SABOR clinical staff. We utilized the Illumina genotyping system of the Institutional Genomic Resource Core for the majority of the genotyping.

Grant Support

U01 CA086402 from the Early Detection Research Network of the National Cancer Institute, from the American Cancer Society grant number TURSG-03-152-01-CCE, entitled “The Role of Genetic Variation in Prostate Cancer among Hispanics and Blacks”, from the Cancer Support Grant, P30 CA54174, and from the Department of Defense Grant W81XWH-05-1-0203.

Footnotes

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

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