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. 2017 Sep 5;8(43):75141–75150. doi: 10.18632/oncotarget.20646

Association of a common genetic variant in RNASEL and prostate cancer susceptibility

Li Zuo 1,*, Ke-Wei Ren 5,*, Yu Bai 1,*, Li-Feng Zhang 1,*, Jian-Gang Zou 1,*, Xi-Hu Qin 2, Yuan-Yuan Mi 3, Atsushi Okada 4, Takahiro Yasui 4
PMCID: PMC5650407  PMID: 29088852

Abstract

The RNASEL gene (2’, 5’-oligoisoadenylate synthetase-dependent) encodes a ribonuclease that plays a significant role in the apoptotic and antiviral activities of interferons. Various studies have used polymorphisms in the RNASEL gene to evaluate prostate cancer risk but studies that show an association between RNASEL Arg462Gln (1385G>A, R462Q, rs486907) polymorphism and prostate cancer risk are somewhat inconclusive. To assess the impact of RNASEL Arg462Gln polymorphism on prostate cancer risk, we conducted a meta-analysis of all available studies including 11,522 patients and 10,976 control subjects. The overall results indicated no positive association between the variant and prostate cancer risk. However, in a subgroup analysis by ethnicity, obvious associations were observed in Hispanic Caucasians for allelic contrast (OR = 1.18, 95% CI = 1.00 - 1.39, Pheterogeneity = 0.010), homozygote comparison (OR = 1.50, 95% CI = 1.02 – 2.20, Pheterogeneity = 0.001), and the recessive genetic model (OR = 1.44, 95% CI = 1.01 - 2.05, Pheterogeneity = 0.002) ; and in African descendants for homozygote comparison (OR = 2.59, 95% CI = 1.29 – 5.19, Pheterogeneity = 0.194) and the recessive genetic model (OR = 2.61, 95% CI = 1.30 – 5.23, Pheterogeneity = 0.195). In conclusion, the RNASEL Arg462Gln polymorphism may contribute to the risk of developing prostate cancer in African descendants and Hispanic Caucasians. Further larger and well-designed studies are warranted to evaluate this association in detail.

Keywords: RNASEL, polymorphism, prostate cancer, meta-analysis

INTRODUCTION

Prostate cancer (PCa) is one of the most common types of neoplasm in the Western world. In United States, prostate cancer is the most prevalent cancer, with 217,730 new cases predicted to occur in 2010 [1]. The etiology of PCa is still poorly understood, but exposure to hormones, infectious agents, or dietary carcinogens may contribute to inflammation of the prostate [2, 3]. Intraprostatic inflammation may affect the tissue microenvironment, promoting genetic damage, and driving cellular proliferation, which may lead to prostate carcinogenesis [4, 5]. Prior studies have suggested that family history is the most reproducible and significant risk factor. Men with a brother or father diagnosed with PCa were twice as likely to develop this cancer as men with no relatives affected [6].

Ribonuclease L (RNASEL), which is considered to be a tumor-suppressor gene, plays a significant role in the pathogenesis of prostate cancer through inflammation and infection. RNASEL is on chromosome 1q24-25 and encodes Ribonuclease L, a significant enzyme of the interferon-induced antiviral 2-5A system [7]. Mutation of RNASEL can lead to dysfunction of Ribonuclease L in regulating single-stranded RNA cleavage, cellular viral defense, and tumor suppressor activities, such as stress-mediated apoptosis and regulation of protein synthesis [8–9].

Extensive epidemiological studies had been conducted to explore the association between RNASEL polymorphism and prostate cancer risk. A G-to-A transversion at nucleotide position 1385 (rs486907), which results in a glutamine instead of arginine at amino acid position 462 (R462Q), is one of the most widely investigated polymorphisms in RNASEL. Nevertheless, the association between the RNASEL R462Q polymorphism and prostate cancer risk is controversial because of conflicting case–control studies. Therefore, in this meta-analysis from all eligible studies published to date [10–32], we used enhance statistical power to understand the effect of this variant.

RESULTS

Study characteristics

A total of 22 articles (including 26 case–control studies) met all the inclusion criteria and were included (Figure 1). The genotype distribution of the control population was consistent with Hardy-Weinberg equilibrium (HWE) in 19 of the publications. Characteristics of the eligible studies are summarized in Table 1. In general, 11,522 prostate cancer patients and 10,976 control subjects with the RNASEL Arg462Gln polymorphism were evaluated. In the ethnic subgroups, 17 case–control studies were performed with European descendants, three with Asian descendants, and four with African descendants. We checked the Minor Allele Frequency (MAF) reported for the five main worldwide populations in the 1000 Genomes Browser: East Asian, 0.2421; European, 0.3708; African, 0.0666; American, 0.2233; and South Asian, 0.3016. The MAF in our analysis was 0.3034 and 0.2900 in the case and control group, respectively (Figure 2). Hospital-based controls were carried out in 15 of the studies. TaqMan real-time polymerase chain reaction (PCR), the classical genotyping method, was utilized in 10 comparisons. Five studies used the GoldenGate platform or Sequenom MassARRAY platform genotyping method. Six publications had genotype frequency information for familial and sporadic prostate cancer cases.

Figure 1. Flowchart illustrating the search strategy used to identify association studies for RNASEL Arg462Gln polymorphism and prostate cancer risk.

Figure 1

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Table 1. Study characteristics of RNASEL Arg462Gln (1385G>A) polymorphism included in this meta-analysis.

First author Year Country Ethnicity Source of Genotype method Sample size of case Sample size of control
control GG GA AA Total MAF HWE GG GA AA Total MAF HWE
Babaei 2015 Iran Asian HB PCR 20 15 5 40 0.313 0.421 44 32 4 80 0.250 0.551
Alvarez-Cubero 2015 Spain Hispanic HB Goldengate assay 80 120 37 237 0.409 0.468 61 114 41 216 0.454 0.342
Winchester 2015 USA Non-Hispanic PB Goldengate assay 352 407 105 864 0.357 0.445 330 372 129 831 0.379 0.157
San Francisco 2014 Chile Hispanic PB Taqman 43 31 9 83 0.295 0.351 28 14 4 46 0.239 0.267
Reza 2012 Iran Asian HB Taqman 64 73 44 181 0.445 0.014 14 4 1 19 0.158 0.364
Sakuma 2011 USA Caucasian HB Real-time PCR 43 55 12 110 0.359 0.366 11 21 8 40 0.463 0.723
Beuten 2010 USA Hispanic HB Goldengate assay 75 64 17 156 0.314 0.550 126 91 7 224 0.234 0.048
Meyer 2010 USA Caucasian PB Sequenom MassARRAY 529 547 159 1235 0.350 0.346 505 546 159 1210 0.357 0.551
Martinez-Fierro 2010 Mexico Mixed HB Taqman 9 2 0 11 0.091 0.041 8 2 1 11 0.182 0.197
Agalliu 2010 USA Non-Hispanic PB Pyrosequencing 467 414 84 965 0.302 0.566 572 556 109 1237 0.313 0.110
Wang 2009 USA Caucasian PB Taqman 100 121 27 248 0.353 0.282 88 132 33 253 0.391 0.130
Fischer 2008 Germany Non-Hispanic HB Real time PCR 51 29 7 87 0.247 0.331 42 24 4 70 0.229 0.816
Robbins 2008 USA African HB Sequenom MassARRAY 183 55 5 243 0.134 0.718 225 66 5 296 0.128 0.950
Shea 2008 USA African PB PCR 187 41 2 230 0.098 0.881 362 88 2 452 0.102 0.168
Shook 2007 USA African HB Taqman 45 13 10 68 0.243 <0.001 111 31 3 145 0.128 0.633
Shook 2007 USA Hispanic HB Taqman 72 62 16 150 0.313 0.629 136 96 7 239 0.230 0.039
Shook 2007 USA Non-Hispanic HB Taqman 187 183 60 430 0.352 0.162 221 225 57 503 0.337 0.981
Cybulski 2007 Poland Non-Hispanic PB PCR-RFLP 245 376 116 737 0.412 0.153 177 252 82 511 0.407 0.625
Daugherty 2007 USA Non-Hispanic HB TaqMan 463 505 148 1116 0.359 0.578 554 602 188 1344 0.364 0.235
Daugherty 2007 USA African HB TaqMan 73 23 2 98 0.138 0.905 277 98 5 380 0.142 0.261
Maier 2005 Germany Non-Hispanic PB PCR 133 171 59 363 0.398 0.746 73 97 37 207 0.413 0.629
Nam 2005 Canada Mixed PB Mass spectrometry 477 409 110 996 0.316 0.117 521 459 112 1092 0.313 0.464
Wiklund 2004 Sweden Non-Hispanic PB TaqMan 597 778 247 1622 0.392 0.804 297 384 115 796 0.386 0.611
Nakazato 2003 Japan Asian HB PCR 69 32 0 101 0.158 0.059 71 26 8 105 0.200 0.020
Rokman 2002 Finland Non-Hispanic HB PCR 88 106 39 233 0.395 0.464 69 84 23 176 0.369 0.745
Wang 2002 USA Caucasian PB PCR 389 427 102 918 0.344 0.347 193 233 67 493 0.372 0.802
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HWE: Hardy-Weinberg equilibrium of controls, HB: Hospital-based; PB: Population-based; MAF: Minor Allele Frequency.

Figure 2. A-allele frequencies for the RNASEL Arg462Gln polymorphism in the controls stratified by ethnicity.

Figure 2

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Vertical line, A-allele frequency; Horizontal line, ethnicity type.

Quantitative synthesis

When all the eligible studies were pooled into the analysis (Table 2), no positive association was observed for allelic contrast (fixed-effects OR = 0.99, 95% CI = 0.95 - 1.03, Pheterogeneity = 0.004, P = 0.758, I2 = 47.9), homozygote comparison (fixed-effects OR = 1.00, 95% CI = 0.91 - 1.09, Pheterogeneity = 0.001, P = 0.968, I2 = 54.2), heterozygote comparison (fixed-effects OR = 1.01, 95% CI = 0.92 - 1.10, Pheterogeneity = 0.029, P = 0.861, I2 = 37.6), the dominant genetic model (fixed-effects OR = 0.99, 95% CI = 0.93 - 1.04, Pheterogeneity = 0.361, P = 0.653, I2 = 7.0), and the recessive genetic model(fixed-effects OR = 1.00, 95% CI = 0.92 – 1.09, Pheterogeneity = 0.002, P = 0.960, I2 = 50.3). However, in the subgroup analysis by ethnicity, obvious associations between the RNASEL Arg462Gln polymorphism and prostate cancer risk were observed in African descendants for homozygote comparison (fixed-effects OR = 2.59, 95% CI = 1.29 – 5.19, Pheterogeneity = 0.194, P = 0.008, I2 = 36.3), and the recessive genetic model (fixed-effects OR = 2.61, 95% CI = 1.30 – 5.23, Pheterogeneity = 0.195, P = 0.007, I2 = 36.1); and for Hispanic Caucasians for the recessive genetic model (fixed-effects OR = 1.44, 95% CI = 1.01 - 2.05, Pheterogeneity = 0.002, P = 0.046, I2 = 79.9), homozygote comparison (fixed-effects OR = 1.50, 95% CI = 1.02 – 2.20, Pheterogeneity = 0.001, P = 0.039, I2 = 82.3), and allelic contrast (fixed-effects OR = 1.18, 95% CI = 1.00 - 1.39, Pheterogeneity = 0.010, P = 0.050, I2 = 73.5). No association was observed in Asian descendants for allelic contrast (fixed-effects OR = 1.30, 95% CI = 0.93 – 1.83, Pheterogeneity = 0.004, P = 0.126, I2 = 82.2), homozygote comparison (fixed-effects OR = 1.49, 95% CI = 0.70 – 3.17, Pheterogeneity = 0.013, P = 0.303, I2 = 76.9), and the recessive genetic model (fixed-effects OR = 1.23, 95% CI = 0.57 – 2.63, Pheterogeneity = 0.019, P = 0.600, I2 = 74.8); non-Hispanic Caucasians for allelic contrast (fixed-effects OR = 0.99, 95% CI = 0.94 – 1.04, Pheterogeneity = 0.856, P = 0.641, I2 = 0) homozygote comparison (fixed-effects OR = 0.98, 95% CI = 0.87 – 1.10, Pheterogeneity = 0.631, P = 0.701, I2 = 0), and the recessive genetic model (fixed-effects OR = 0.98, 95% CI = 0.88 – 1.09, Pheterogeneity = 0.487, P = 0.680, I2 = 0); and mixed descendants for allelic contrast (fixed-effects OR = 1.01, 95% CI = 0.89 – 1.15, Pheterogeneity = 0.381, P = 0.886, I2 = 0), homozygote comparison (fixed-effects OR = 1.06, 95% CI = 0.79 – 1.42, Pheterogeneity = 0.453, P = 0.692, I2 = 0), and the recessive genetic model (fixed-effects OR = 1.07, 95% CI = 0.81 – 1.42, Pheterogeneity = 0.452, P = 0.610, I2 = 0) (Figure 3). Furthermore, a significant association was also observed under the recessive genetic model (random-effects OR = 1.45, 95% CI = 1.01 – 2.08, Pheterogeneity < 0.001, P = 0.046, I2 = 64.8) between the RNASEL polymorphism and hospital-based controls (Figure 4). Interestingly, in a stratified analysis by the type of prostate cancer, a positive association was observed in familial prostate cancer for allelic contrast (fixed-effects OR = 0.89, 95% CI = 0.79 – 0.99, Pheterogeneity = 0.209, P = 0.028, I2 = 31.8) and homozygote comparison (fixed-effects OR = 0.77, 95% CI = 0.61 – 0.98, Pheterogeneity = 0.136, P = 0.037, I2 = 42.9), but not in sporadic prostate cancer for allelic contrast (fixed-effects OR = 1.02, 95% CI = 0.94 – 1.10, Pheterogeneity = 0.774, P = 0.671, I2 = 0) and homozygote comparison (fixed-effects OR = 1.06, 95% CI = 0.89 – 1.27, Pheterogeneity = 0.640, P = 0.503, I2 = 0).

Table 2. Stratified analyses of the RNASEL Arg462Gln polymorphism on prostate cancer risk.

Variables Na Cases/ A-allele vs. G-allele AA vs. GG AA vs. GA+GG
Controls OR(95%CI) P Pheterb I2 OR(95%CI) P Pheterb I2 OR(95%CI) P Pheterb I2
Total 26 11522/10976 0.99(0.95-1.03 0.758 0.004 47.9 1.00(0.91-1.09 0.968 0.001 54.2 1.00(0.92-1.09 0.960 0.002 50.3
Ethnicity
Asian 3 322/204 1.30(0.93-1.83 0.126 0.004 82.2 1.49(0.70-3.17 0.303 0.013 76.9 1.23(0.57-2.63 0.600 0.019 74.8
African 4 639/1273 1.12(0.91-1.37 0.281 0.056 60.3 2.59(1.29-5.19 0.008 0.194 36.3 2.61(1.30-5.23 0.007 0.195 36.1
Caucasian 4 2511/1996 0.92(0.84-1.00 0.058 0.371 4.4 0.84(0.70-1.02 0.081 0.319 14.6 0.88(0.74-1.06 0.173 0.462 0
Hispanic Caucasians 4 626/725 1.18(1.00-1.35 0.050 0.010 73.5 1.50(1.02-2.20 0.039 0.001 82.3 1.44(1.01-2.05 0.046 0.002 79.9
Non-Hispanic Caucasians 9 6417/5675 0.99(0.94-1.04 0.641 0.856 0 0.98(0.87-1.10 0.701 0.631 0 0.98(0.88-1.09 0.680 0.487 0
Mixed 2 1007/1103 1.01(0.89-1.15 0.886 0.381 0 1.06(0.79-1.42 0.692 0.453 0 1.07(0.81-1.42 0.610 0.452 0
Source of control
Hospital-based 15 3261/3848 1.06(0.98-1.14 0.120 0.001 63.1 1.47(0.99-2.20 0.059 <0.001c 67.9 1.45(1.01-2.08 0.046 <0.001c 64.8
Population-based 11 8261/7128 0.97(0.92-1.01 0.169 0.802 0 0.94(0.84-1.04 0.235 0.709 0 0.95(0.86-1.05 0.296 0.739 0
Type of prostate cancer
Sporadic Pca 6 2838/2934 1.02(0.94-1.10 0.671 0.774 0 1.06(0.89-1.27 0.503 0.640 0 1.07(0.91-1.26 0.441 0.679 0
Familial Pca 5 1313/1967 0.89(0.79-0.99 0.028 0.209 31.8 0.77(0.61-0.98 0.037 0.145 31.8 0.81(0.65-1.02 0.070 0.210 31.7
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a Number of comparisons

b P value of Q-test for heterogeneity test(Pheter).

c Random effects model was performed when Pheter <0.001; otherwise, fixed effects model was used.

Figure 3. Forest plot of prostate cancer risk associated with RNASEL Arg462Gln polymorphism (recessive genetic model) in the stratified analysis by ethnicity.

Figure 3

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The squares and horizontal lines correspond to the study-specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI. Separate details were summarized in Table 1.

Figure 4. Forest plot of prostate cancer risk associated with RNASEL Arg462Gln polymorphism (recessive genetic model of AA vs. GA + GG) by source of control.

Figure 4

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The squares and horizontal lines correspond to the study-specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the summary OR and 95% CI.

Publication bias

The Egger’s test and Begg’s funnel plot were carried out to assess the publication bias of the literature. No obvious evidence of publication bias was found (A-allele vs. G-allele, t = 1.72, P = 0.098; AA vs. GG, t = 1.77, P = 0.089; GA vs. GG, t = 1.75, P = 0.094; AA + GA vs. AA, t = 1.23, P = 0.231; AA vs. GA + GG, t = 1.77, P = 0.090)

DISCUSSION

Published studies have shown evidence that RNASEL is a constitutively expressed latent endo-ribonuclease that mediates proapoptotic and antiviral activities of the IFN-inducible 2-5A system [7–9]. Mutation carriers in the RNASEL gene have loss of heterozygosity and are deficient in functional RNase L activity [33]. However, previous reports showing association between RNASEL polymorphism and prostate cancer susceptibility are contradictory. The general goal of this pooled analysis is to quantitatively analyze previous studies to understand the true relationship between RNASEL polymorphism and prostate cancer. Here, previous case-control studies with information between the RNASEL polymorphism and different types of Caucasians (Hispanic and Non-Hispanic) were included. As a result, some new findings were observed in our meta-analysis.

Our results indicated that the RNASEL Arg462Gln polymorphism may be associated with increased prostate cancer in African descendants (under homozygote comparison and the recessive genetic model) and Hispanic Caucasians (under allelic contrast, homozygote comparison, and the recessive genetic model), but not in Asian descendants and Non-Hispanic Caucasians. Furthermore, in a stratified analysis by source of control, the RNASEL Arg462Gln variant was found to increase prostate cancer risk in hospital-based studies (under the recessive genetic model). Nevertheless, several caveats limit generalization of these results. First, detailed information, such as age, prognostic parameters, environmental factors, and life-style, were not considered. Second, because different types of prostate cancer influence susceptibility, we tried to assess the effect of this polymorphism to different types of prostate cancer but not all data was compatible. Third, positive findings may be published faster than that those with “negative” results, which may result in a time-lag bias [34]. In addition, more environmental interactions, such as smoking habits, dietary factors, hormone exposure, toxins, and infectious agent, need to be added to the meta-analysis in the future.

Other limitations of the meta-analysis need to be addressed. First, while it is possible that the Arg462Gln polymorphism contributes to cancer, the combined effects of multiple environmental or genetic components predominate in the development of carcinoma, and may mask the effect of the polymorphism [35]. Second, the present analysis was based on unadjusted estimates. A more precise analysis with individual data is needed to evaluate combinatorial effects of the polymorphism [36]. Despite these concerns, the current analysis has some advantages compared with the individual studies. First, a substantial number of cases and controls were pooled from different studies, which significantly enhance the statistical power of this analysis. Second, the quality of case-control studies enrolled in our analysis was satisfactory based on the selection criteria. Third, no obvious publication bias was observed, which indicates that the conclusions were relatively stable and the publication bias might not influence the conclusions of the present meta-analysis.

In conclusion, this meta-analysis showed evidence that the RNASEL Arg462Gln polymorphism may contribute to the risk for developing prostate cancer in African descendants and Hispanic Caucasians, but not for other descendants. Further well-designed and prospective studies, particularly focused on gene-environment interactions, are warranted. These future studies should lead to a comprehensive understanding of the association between the RNASEL Arg462Gln polymorphism and prostate cancer risk.

MATERIALS AND METHODS

Search strategy and identification of relevant studies

PubMed database searches were conducted using the following keywords: “ribonuclease L” or “RNASEL”, “prostate cancer”, and “polymorphism” (last search updated on March 01, 2017). References of the relevant articles and retrieved paper were also screened by hand search. Eligible studies had to meet all the following criteria: (a) used an unrelated case–control design; (b) contained information about available genotype frequency; (c) published in English; and (d) included the full-text article.

Data extraction and quality assessment

Data were collected on the genotype of rs486907 G/A (R462Q) according to prostate cancer. For each publication, the data extraction and methodological quality assessment were conducted by two of the investigators independently to ensure accuracy of the data. Disagreement was resolved by discussion between the two investigators. If they could not reach a consensus, the problem was discussed by all investigators to reach a consensus. The following parameters from each study were recorded: first author’s name, publication date, ethnicity, sources of cancer cases and controls, sample size in cases and controls, and the number of cases and controls with wild-type and variant allele, respectively.

Statistical analysis

Crude ORs with 95% CIs were utilized to evaluate the strength of association between the RNASEL polymorphism and prostate cancer based on genotype frequencies in cases and controls. Five genetic contrasts were used to assess the association: allelic contrast (A-allele vs. G-allele), homozygote comparison (AA vs. GG), heterozygote comparison (GA vs. GG), the dominant genetic model (AA + GA vs. GG), and the recessive genetic model (AA vs. GA + GG). Subgroup analysis was stratified by ethnicity, source of control (hospital-based and population-based), and smoking exposure. We utilized the random effects model and fixed effects model to calculate the pooled OR. Heterogeneity assumption was evaluated by a chi-square-based Q test. P value lower than 0.001 for the Q-test indicates lack of heterogeneity among studies, hence the pooled OR was utilized by the random effects model (DerSimoniane and Laird method [37] or by the fixed-effects model (the Mantel-Haenszel method [38]). HWE was checked by the Pearson chi-square test for goodness of fit. A Z-test was used to evaluate statistical significance of the summary OR, and P value of ≤ 0.05 was considered significant. We also utilized the I2 statistic to test heterogeneity, with I2 >75%, 25–75%, and < 25% to represent high, moderate, and low degree of inconsistency, respectively [39]. We determined significance of the intercept by t-test suggested by Egger (P < 0.01 was considered representative of significant publication bias) [40]. All statistical analyses were performed with STATA version 11.0 (Stata Corporation, College Station, TX), utilizing two-sided P values.

Footnotes

CONFLICTS OF INTEREST

The authors declare that they have no competing interests.

FUNDING

This study was supported by the foundation of High-Level Medical Talents Training Project (No.2016CZBJ035) and Changzhou 23rd Science and Technology Project (International Science and Technology Collaboration), Project No. CZ20160017.

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