==== Front Cancer Manag ResCancer Manag ResCancer Management and ResearchCancer Management and Research1179-1322Dove Medical Press 10.2147/CMAR.S171305cmar-10-2377Original ResearchLack of association between the risk of prostate cancer and vitamin D receptor Bsm I polymorphism: a meta-analysis of 27 published studies Kang Shaosan 1*Zhao Yansheng 2*Wang Lei 1Liu Jian 1Chen Xi 1Liu Xiaofeng 3Shi Zhijie 4Gao Weixing 1Cao Fenghong 1 1 Department of Urology, North China University of Science and Technology Affiliated Hospital, Tangshan 063000, People’s Republic of China, kangshaosan@163.com 2 Department of Imaging, Kailuan General Hospital, Tangshan 063000, People’s Republic of China 3 Department of Surgery, Laoting Traditional Chinese Medicine Hospital, Tangshan 063600, People’s Republic of China 4 Department of Urology, Tangshan Gongren Hospital, Tangshan 063000, People’s Republic of ChinaCorrespondence: Shaosan Kang, Department of Urology, North China University of Science and Technology Affiliated Hospital, Tangshan, People’s Republic of China, Email kangshaosan@163.com* These authors contributed equally to this work 2018 01 8 2018 10 2377 2387 © 2018 Kang et al. This work is published and licensed by Dove Medical Press Limited2018The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.Background The association between vitamin D receptor gene Bsm I (rs1544410) polymorphism and prostate cancer (PCa) risk has been investigated by numerous previous studies, which yielded inconsistent results. We conducted this meta-analysis to derive a relatively precise description of this association. Methods All studies published up to December 2017 were identified via a systematic search of PubMed, Embase, and China National Knowledge Infrastructure databases. Pooled odds ratios (ORs) with their 95% confidence intervals (CIs) were estimated to describe the strength of the relationship between Bsm I and PCa risk. Results In this meta-analysis, 27 studies with 9,993 cases and 9,345 controls were included. The pooled results revealed that Bsm I polymorphism was not associated with PCa risk in the overall analysis. Moreover, no significant relationship was found in the subgroup analyses by ethnicities, genotyping methods, Hardy–Weinberg equilibrium status, and Gleason score. In the stratified analysis by the source of controls and clinical stages, controls of benign prostatic hyperplasia (BPH) seemed to be in the particular groups in which the association of PCa risk with Bsm I polymorphism was significant (Bb vs. bb: OR=0.643, 95% CI=0.436–0.949, p=0.026; BB/Bb vs. bb: OR=0.627, 95% CI=0.411–0.954, p=0.029; B vs. b: OR=0.715, 95% CI=0.530–0.965, p=0.029). Conclusion Our results suggest that Bsm I polymorphism is weakly associated with PCa risk, and hence, it cannot be considered as a predictor of the occurrence and development of PCa in clinical practice. Future studies with a larger number of samples are needed to verify our results. Keywords Bsm Iprostate cancervitamin D receptorpolymorphismsmeta-analysis ==== Body Introduction According to a recent report published in the CA: A Cancer Journal for Clinicians in January, 164,690 new prostate cancer (PCa) cases and 29,430 PCa-related deaths were estimated in Americans in 2015.1 PCa has risen to the first place among new cancer cases, and become the second leading cause of cancer-related deaths in males.1 To make matters worse, the global prevalence rate of PCa is rising rapidly. It is forecasted that by 2030, the number of newly diagnosed PCa cases and deaths will rise up to more than 1.8 million and 0.5 million, respectively.2 Existing evidence suggests that PCa risk might increase due to multiple factors, including aging, genetic factors, pathological changes, diet, hormonal level, as well as ethnicity and environment.3 However, the pathophysiological mechanism of PCa remains largely unclear. In a laboratory investigation, prostate cell division and growth was reported to be affected by vitamin D.4 Thus, low plasma levels of vitamin D were hypothesized to be one of the important contributors to PCa.4 The clinical trial also found that pre-diagnostic serum levels of vitamin D >85 nmol/L may improve survival in men with PCa.5 The action of vitamin D is mediated by vitamin D receptor (VDR).6 1,25-Dihydroxy vitamin D3 (1,25(OH)2D3), which is one of the active forms of vitamin D, would combine with VDR to form a heterodimer complex. Subsequently, the complex binds to vitamin D response element inducing reduced transcriptional levels of many genes which then stimulates tumor cell growth and differentiation.7,8 In recent years, the association between PCa risk and some single-nucleotide polymorphisms of VDR gene has become the focus of research attention.9 We also conducted a meta-analysis on Taq I and Fok I polymorphisms and their relationships with PCa risk.7 Bsm I polymorphism (rs1544410) is one of the most frequently researched variants. It is a restriction site located in intron 8 of VDR gene, which does not affect the amino acid sequence during VDR protein expression.10 However, mutations in the intron region might be able to lower the stability of mRNA and affect the mRNA levels. Numerous research has revealed that Bsm I mutation might play a significant role in the development or progression of PCa.11–14 However, some other studies do not support this association.15–18 These results are inconsistent and worth further exploration. In addition, previous meta-analyses10,19–22 seemed to be out of date due to availability of new data.3,9,14,23,24 Therefore, we performed a new meta-analysis with the aim of obtaining more accurate and updated results. Methods Literature retrieval strategy PubMed, Embase, and China National Knowledge Infrastructure (CNKI) electronic databases were searched for eligible studies published till December 2017. The terms “VDR/vitamin D receptor”, “prostate cancer/tumor/carcinoma”, and “polymorphism/mutation/variant” were used for searching titles or abstracts. Full search expressions were “vitamin D receptor [Title/Abstract] AND ((polymorphism [Title/Abstract] OR mutation [Title/Abstract]) OR variant [Title/Abstract]) AND prostate cancer [Title/Abstract]” for PubMed, “‘vitamin d receptor’:ab,ti AND ‘polymorphism’:ab,ti AND ‘prostate cancer’:ab,ti” for Embase, and “vitamin D receptor AND polymorphism AND prostate cancer” in Chinese for CNKI. In addition, we read the original or review reports carefully and searched manually for more eligible literature based on their references. Study selection Candidate studies were evaluated by two authors independently (Lei Wang and Jian Liu) for the following inclusion criteria: (1) studies in nonfamilial case–control or nested case–control design conducted on human beings; (2) studies that assessed the relationship between Bsm I polymorphism and risk or progression of PCa; (3) studies in which the distribution frequency of genotype and allelic profile of participants could be acquired or calculated; (4) studies in which no significant difference was reported between cases and controls in the aspect of baseline characters; (5) studies that scored more than 5 points on the Newcastle–Ottawa Scale (NOS). Data extraction Two investigators (Lei Wang and Jian Liu) collected the following information independently: first author’s name, publication year, population information, genotyping methods, the number of participants, genotype and allelic profile, as well as the source of controls. Cases and controls were classified into different subgroups by ethnicity, source of controls, and genotyping method, respectively. The subjects were also divided into group with Gleason score <7 and group with Gleason score ≥7 by pathological grade, and localized group and aggressive group by clinical stages, respectively. Any controversial content was discussed and evaluated by a third reviewer (Yansheng Zhao) to reach an agreement on all the items. Statistical analyses The heterogeneity was evaluated by using χ2-test based on Cochran’s Q-test and I2 statistics. If I2>50% and p<0.05, the heterogeneity between studies was significant and the random-effects model was used to combine the values from single studies;25 otherwise, in the absence of heterogeneity, the fixed-effects model was chosen. The pooled odds ratios (ORs), together with 95% confidence intervals (CIs), were calculated to assess the strength of the relationship. The statistical significance of ORs was determined with Z-test. Five genetic comparison models were calculated in our analysis, including homozygote model (BB vs. bb), heterozygous model (Bb vs. bb), dominant model (BB vs. Bb/bb), recessive model (BB/Bb vs. bb), and allele genetic model (B vs. b allele). Begg’s funnel plot and Egger’s linear regression were used to evaluate the potential publication bias. Sensitivity analysis was performed to evaluate the stability of pooled results. Moreover, the Hardy–Weinberg equilibrium (HWE) status of controls was recalculated with the goodness-of-fit χ2-test; p<0.05 indicated that the genotype frequency of controls was not consistent with HWE. For each outcome, we also conducted subgroup analyses by ethnicity, the source of controls, genotyping method, and clinical stages. p-values were two-sided, and p<0.05 was considered statistically significant. All analyses were done using the STATA package version 12.0 (Stata Corp, College Station, TX, USA). Results Characteristics of studies A total of 87 studies were identified to be potentially related to the topic through our search strategy. Following our inclusion criteria, 27 studies3,9,11–17,23,24,26–41 published between the years 1998 and 2017 were finally included to evaluate the association (Figure 1). As shown in Table 1, out of the 27 studies, 25 explored the relationship of PCa risk with Bsm I, and nine were about the association between PCa progression and Bsm I. The number of participants in the case group and control group varied from 28 to 1,034, and 30 to 1,566, respectively. For all studies, except five, the genotype distribution frequency of Bsm I polymorphism in the control groups conformed to the HWE. All the studies scored more than 5 on the NOS, and were considered to be of high quality (Table 1). Heterogeneity Obvious heterogeneity between the studies was found in overall analysis for some genetic comparison models (Bb vs. bb: p=0.000, I2=59.2%; BB/Bb vs. bb: p=0.000, I2=65.9%; and B vs. b: p=0.000, I2=65.8%) (Tables 2–6). Thus, the random-effects model was chosen for data analysis in these comparison models. Meanwhile, in the recessive model, no heterogeneity was detected (BB vs. Bb/bb: p=0.285, I2=12.5%), and the fixed-effects model was used. Similar results were found in the subgroup analyses. Pooled results in terms of PCa risk with Bsm I polymorphism The results of the overall analysis obtained by pooling all the 25 studies are shown in Table 2 and Figure 2. These results indicate that Bsm I mutation does not increase the risk of PCa under different comparison models (BB vs. bb: OR=0.977, 95% CI=0.889–1.074, p=0.634; Bb vs. bb: OR=0.940, 95% CI=0.825–1.072, p=0.357; BB/Bb vs. bb: OR=0.951, 95% CI=0.832–1.087, p=0.462; BB vs. Bb/bb: OR=1.002, 95% CI=0.923–1.087, p=0.963; B vs. b: OR=0.969, 95% CI=0.883–1.065, p=0.516) (Table 2). In the subgroup analyses conducted for a more detailed evaluation of the relationship, the results did not reveal any association by different ethnicities (Table 3), different genotyping methods (Table 4), or different HWE statuses of control groups (results not shown). As shown in Figure 3 and Table 5, in the stratified analysis by the source of control groups, the PCa risk was significantly increased in patients with bb genotype or b genotype specifically in the subgroup of benign prostatic hyperplasia (BPH) controls (Bb vs. bb: OR=0.689, 95% CI=0.534–0.890, p=0.004; BB/Bb vs. bb: OR=0.627, 95% CI=0.411–0.954, p=0.029; B vs. b: OR=0.715, 95% CI=0.530–0.965, p=0.029). However, the results for the other two control groups revealed no significant association (Table 5). Pooled results in terms of Bsm I polymorphism with PCa progression Stratified analyses, according to the clinical stages and Gleason score of patients, were also performed. As shown in Table 6, the pooled results for the patients with Gleason score <7 and Gleason score ≥7 did not reveal any relationship between the Bsm I variant and PCa risk in various genetic models compared to controls. Similarly, the subgroup of PCa cases with localized stage and aggressive stage showed no association. In the inter-patient comparisons by different clinical stages and Gleason score statuses, a weak influence of Bsm I polymorphism on PCa progression was detected in patients with Gleason score ≥7 compared to the group with Gleason score <7 (BB/Bb vs. bb: OR=1.176, 95% CI=1.008–1.373, p=0.04). However, no effect of Bsm I polymorphism on the clinical stages was detected (Figure 4 and Table 6). Publication bias and sensitivity analysis The funnel plots for publication bias analysis did not show any significant asymmetry in the overall analysis (Figure 5). Moreover, Begg’s and Egger’s tests also revealed no publication bias in overall analysis as well as subgroup analyses (Tables 2–6). Sensitivity analysis for the positive results suggested that no obvious change in the pooled results was detected by omitting each individual study for the subgroup analysis of BPH controls, while the results were unstable in the comparison of PCa cases in terms of Gleason scores (Figure 6). Discussion Polymorphisms of VDR gene and their relationships with PCa susceptibility have drawn a lot of attention in recent years. Bsm I polymorphism is one of the “star biomarkers”. Even though Bsm I polymorphism is located in the noncoding regions of VDR gene, it is frequently considered to be associated with PCa risk by numerous studies.9,11,12,39 Meanwhile, some studies support the opposite conclusion.15,16,30,34 Five meta-analyses conducted by Yin et al,19 Zhang et al,20 Guo et al,21 Xu et al,22 and Liu et al,10 including 14, 19, 19, 15, and 6 primary studies, respectively, also yielded conflicting results. Moreover, some new data were reported.3,9,14,23,24 Therefore, a new meta-analysis is necessary to clarify this issue. In the present study, data of 27 independent studies including 9,993 cases and 9,345 controls, which is higher compared to the previous meta-analyses, were pooled. Therefore, our updated results will be more convincing and stringent. According to our results, no association between PCa risk and Bsm I polymorphism was detected in the overall population, which was similar to the results reported by Guo et al,21 Liu et al,10 and Xu et al,22 but different from the other two meta-analyses.19,20 As we mentioned above, the results of previous meta-analyses might be suspect due to outdated data or inclusion of incomplete studies. Ethnicity might be an important biological factor for the genetic difference.42 The genotype frequency distribution of Bsm I was found to be different between Asians, Caucasians and Africans, but in each subgroup by ethnicity, no association was found. In addition, subgroup analyses by the genotyping method and HWE status both revealed no influence of Bsm I on PCa risk, suggesting that these two variables would not change the negative result of the overall analysis either. An interesting finding was that according to the results of the subgroup analysis by different sources of controls, Bsm I mutation increased the risk of PCa in BPH controls in the heterozygote model, recessive model, and allele model. Moreover, this result was proved to be robust by sensitivity analysis, and the heterogeneity was found to be acceptable as well. Based on this result, for individuals with BPH, the bb genotype or b might increase the risk of PCa, however, this result was suspicious and difficult to explain. Age was reported to be a risk factor for the relationship between Bsm I mutation and PCa risk.26,35 We intended to perform a subgroup meta-analysis by age, but the age classification in the included studies was too ambiguous to be pooled. Similar to overall analysis, subgroup analyses by clinical stage and Gleason score revealed no relationship between PCa risk and Bsm I. Moreover, we conducted inter-patient analysis to assess the relationship of Bsm I polymorphism with PCa progression by comparing cases with aggressive stage and Gleason score ≥7 to cases with localized stage and Gleason score <7, respectively. Almost all the results were negative, except for the comparison between cases with Gleason score ≥7 and <7 in the recessive model. However, the only positive result was not stable in sensitivity analysis. Therefore, we have ignored the weak relationship. Regrettably, we failed to perform a subgroup analysis by vitamin D intake, because only two primary studies have a detailed description of the effect of plasma vitamin D levels on the association between Bsm I and PCa risk. Ma et al reported that in patients with low levels of 25-D, which is one of the vitamin D metabolites, the PCa risk would be significantly increased by carrying bb genotype.33 Meanwhile, in the group with high levels of 25-D, the relationship was not significant. Similar results were reported by Ahn et al in 2009.18 These studies suggest that plasma levels of 25-D might influence our pooled result, and a stratified analysis by vitamin D intake or 25-D levels is warranted in the future. Significant heterogeneity between studies was detected in both overall analysis and subgroup analyses under multiple comparison models. We noted that the BB genotype in the Asian group was quite rare but very commonly detected in Caucasians and Africans. It may contribute to this heterogeneity. However, no obvious publication bias was found and the sensitivity analysis supported the stability of our results. Overall, the present analysis was credible and statistically valid for the studied population. However, some limitations of our meta-analysis should be acknowledged. First of all, some reports with a small number of cases and controls were included in our analysis, which increases the statistical power but introduces potential bias and heterogeneity as well. Second, our pooled outcomes were based on the initial results of the included studies, which were not adjusted by patient characteristics and other potential factors, such as age, gender, smoking, alcohol, sunshine, vitamin D intake, and so on. Therefore, a more precise analysis is required, in which the results should be adjusted by some related parameters. Besides, heterogeneity was obviously detected in some pooled results, which cannot be eliminated by subgroup analyses. In conclusion, the present pooled analysis might be the largest one so far to evaluate the relationship between PCa susceptibility and Bsm I polymorphism of VDR gene. No increased risk of PCa was detected to be associated with Bsm I mutant in the overall analysis, and similarly in different subgroup analyses by race, genotyping methods, HWE status of controls, and clinical stage and Gleason score of cases. The association between PCa progression and Bsm I was also negative. Individuals with BPH, carrying bb genotype and b, seemed to have an increased risk of PCa. More large-scale and well-designed studies are needed in future to demonstrate the weak influence of Bsm I mutant on PCa risk and progression. Author contributions All authors contributed toward data analysis, drafting and revising the paper and agree to be accountable for all aspects of the work. Disclosure The authors report no conflicts of interest in this work. Figure 1 Flowchart showing the process of selection of the final 27 studies. Abbreviation: PCa, prostate cancer. Figure 2 Forest plots to estimate the association of VDR Bsm I polymorphism with PCa in the overall analysis. (A) Homozygote model (BB vs. bb). (B) Recessive model (BB vs. Bb/bb). Abbreviations: PCa, prostate cancer; OR, odds ratio; CI, confidence interval. Figure 3 Forest plots to estimate the association of VDR Bsm I polymorphism with PCa in the subgroup of BPH controls. (A) Heterozygote model (Bb vs. bb). (B) Allelic frequency model (B vs. b). Abbreviations: PCa, prostate cancer; BPH, benign prostatic hyperplasia; OR, odds ratio; CI, confidence interval. Figure 4 Forest plot to estimate the association of VDR Bsm I polymorphism with cases with Gleason score >7 and cases with Gleason score <7 in the dominant model (BB/Bb vs. bb). Abbreviations: OR, odds ratio; CI, confidence interval. Figure 5 Begg’s funnel plots to examine publication bias for reported comparisons of VDR gene Bsm I polymorphism for the homozygote model in the (A) overall analysis and (B) the subgroup analysis of BPH controls. Abbreviations: BPH, benign prostatic hyperplasia; OR, odds ratio. Figure 6 Sensitivity analysis of the (A) comparison of PCa cases with BPH controls (Bb vs. bb) and (B) comparison of PCa cases with Gleason score >7 vs. <7 (BB/Bb vs. bb). Abbreviations: PCa, prostate cancer; BPH, benign prostatic hyperplasia. Table 1 Characteristics and quality assessment of the studies included in this meta-analysis Author Year Country Ethnicity Genotyping method Source of controls Sample size (cases/controls) HWE NOS Bai et al11* 2009 People’s Republic of China Asian PCR-RFLP HB 122/130 Y 6 Chaimuangraj et al15 2006 Thailand Asian PCR-RFLP HB/BPH 28/30/44 N/N 5 Chen et al26 2001 People’s Republic of China Asian PCR-RFLP HB 95/103 Y 5 Cheteri et al27* 2004 USA Caucasian PCR-RFLP PB 543/510 N 6 Chokkalingam et al16* 2001 People’s Republic of China Asian PCR-RFLP PB 161/297 N 6 Cicek et al28* 2006 USA Mixed PCR-RFLP PB 493/479 Y 7 El Ezzi et al24 2014 Lebanon Asian PCR-RFLP BPH 50/68 N 5 El Ezzi et al23 2017 Lebanon Asian PCR-RFLP PB 50/79 Y 6 Habuchi et al12 2000 Japan Asian PCR-RFLP PB/BPH 222/326/209 Y/Y 8 Hayes et al17 2005 Australia Caucasian PCR-RFLP PB 812/713 Y 8 Holick et al29 2007 USA Caucasian SNPlex PB 590/541 Y 8 Holt et al30 2009 USA Mixed SNPlex PB 795/767 Y 8 Huang et al13* 2004 People’s Republic of China Asian PCR-RFLP PB 160/205 N 6 Jingwi et al9 2015 USA African TaqMan HB 278/71 Y 7 Li et al31 2007 USA Caucasian PCR-RFLP PB 1034/1566 Y 8 Liu et al32 2003 People’s Republic of China Asian HPLC PB 103/106 Y 7 Ma et al33 1998 USA Caucasian PCR-RFLP PB 372/591 Y 7 Mikhak et al34 2007 USA Caucasian TaqMan PB 646/669 Y 7 Nam et al35 2003 Canada Mixed PCR-RFLP HB/BPH 483/548/256 N/Y 7 Nunes et al14* 2016 Brazil Caucasian PCR-RFLP PB/BPH 132/169/41 Y/Y 7 Oakley-Girvan et al36 2004 USA Mixed PCR-RFLP PB 345/292 Y 7 Oh et al3 2014 South Korea Asian SNPlex BPH 272/173 Y 6 Onen et al37 2008 Turkey Caucasian PCR-RFLP PB 133/157 Y 7 Suzuki et al38* 2003 Japan Asian PCR-RFLP HB 81/105 Y 6 Szendroi et al39 2011 Hungary Caucasian PCR-RFLP PB 204/102 Y 7 Chen et al40* 2009 UK Caucasian TaqMan HB Gleason score <7/≥7 1104/449 Localized/Advanced 1356/197 Y 7 Williams et al41* 2004 USA Mixed TaqMan HB Gleason score <7/≥7 159/267 (Caucasian) and 102/208 (African) Y 7 Note: * These studies evaluated the association between Bsm I and PCa progression by different clinical stage or Gleason score. In the Sample size column the three numbers were case/HB/BPH as it has two control groups. Abbreviations: HWE, Hardy–Weinberg equilibrium; NOS, Newcastle–Ottawa Scale; HB, hospital-based; BPH, benign prostate hyperplasia; PB, population-based; N, non-HWE; Y, HWE. Table 2 Results of the association between Bsm I polymorphism and PCa risk in the whole population Comparison Studies Overall effect Heterogeneity Publication bias OR (95% CI) Z-score p-value I2 (%) p-value Begg’s test Egger’s test BB vs. bb 25 0.977 (0.889–1.074) 0.48 0.634 48.5 0.005 0.874 0.901 Bb vs. bb 25 0.940 (0.825–1.072) 0.92 0.357 59.2 0 0.126 0.013 BB/Bb vs. bb 25 0.951 (0.832–1.087) 0.74 0.462 65.9 0 0.229 0.042 BB vs. Bb/bb 25 1.002 (0.923–1.087) 0.05 0.963 12.3 0.293 0.853 0.824 B vs. b 25 0.969 (0.883–1.065) 0.65 0.516 65.8 0 0.913 0.229 Abbreviations: PCa, prostate cancer; OR, odds ratio; CI, confidence interval. Table 3 Results of the association between Bsm I polymorphism and PCa risk by different ethnicities Comparison Studies Overall effect Heterogeneity Publication bias OR (95% CI) Z-score p-value I2 (%) p-value Begg’s test Egger’s test Asian BB vs. bb 11 1.075 (0.625–1.850) 0.26 0.793 26.7 0.207 1 0.945 BB vs. bb 11 0.884 (0.592–1.320) 0.6 0.546 65.9 0.001 0.484 0.253 BB/Bb vs. bb 11 0.913 (0.612–1.362) 0.45 0.656 69.9 0 0.392 0.371 BB vs. Bb/bb 11 1.125 (0.756–1.675) 0.58 0.562 0.0 0.618 0.677 0.987 B vs. b 11 0.957 (0.686–1.334) 0.26 0.794 70.0 0 0.938 0.481 Caucasian BB vs. bb 11 0.975 (0.812–1.172) 0.26 0.791 58.2 0.008 0.815 0.875 Bb vs. bb 11 0.970 (0.840–1.120) 0.42 0.675 60.0 0.005 0.186 0.215 BB/Bb vs. bb 11 0.975 (0.839–1.134) 0.33 0.743 67.8 0.001 0.392 0.366 BB vs. Bb/bb 11 0.995 (0.904–1.094) 0.11 0.913 31.7 0.146 0.938 0.835 B vs. b 11 0.981 (0.887–1.085) 0.37 0.711 67.2 0.001 0.938 0.649 African BB vs. bb 3 1.131 (0.316–4.055) 0.19 0.85 83.1 0.003 0.117 0.137 Bb vs. bb 3 1.131 (0.544–2.349) 0.33 0.742 71.3 0.031 0.602 0.212 BB/Bb vs. bb 3 1.155 (0.509–2.622) 0.34 0.731 79.7 0.007 0.602 0.273 BB vs. Bb/bb 3 1.021 (0.670–1.555) 0.1 0.924 63.7 0.064 0.602 0.578 B vs. b 3 1.015 (0.592–1.738) 0.05 0.957 80.6 0.006 0.117 0.228 Abbreviations: PCa, prostate cancer; OR, odds ratio; CI, confidence interval. Table 4 Results of the association between Bsm I polymorphism and PCa risk by different genotyping methods Comparison Studies Overall effect Heterogeneity Publication bias OR (95% CI) Z-score p-value I2 (%) p-value Begg’s test Egger’s test PCR-RFLP BB vs. bb 16 0.979 (0.730–1.313) 0.14 0.877 60.7 0.001 0.528 0.742 BB vs. bb 16 0.976 (0.784–1.215) 0.22 0.826 68.1 0 0.528 0.758 BB/Bb vs. bb 16 0.987 (0.789–1.235) 0.11 0.91 73.7 0 0.510 0.513 BB vs. Bb/bb 16 0.987 (0.861–1.132) 0.19 0.853 35.7 0.077 0.510 0.513 B vs. b 16 0.995 (0.842–1.176) 0.06 0.951 74.4 0 0.510 0.569 TaqMan BB vs. bb 3 1.041 (0.867–1.250) 0.43 0.668 0.0 0.71 0.117 0.126 Bb vs. bb 3 0.957 (0.838–1.093) 0.65 0.518 0.0 0.948 0.117 0.016 BB/Bb vs. bb 3 0.972 (0.859–1.100) 0.45 0.656 0.0 0.84 0.117 0.48 BB vs. Bb/bb 3 1.031 (0.893–1.191) 0.42 0.677 0.0 0.689 0.117 0.48 B vs. b 3 0.998 (0.918–1.084) 0.06 0.954 0.0 0.696 0.117 0.316 SNPlex BB vs. bb 4 0.960 (0.786–1.171) 0.41 0.685 13.1 0.327 0.497 0.501 Bb vs. bb 4 0.852 (0.671–1.082) 1.31 0.19 54.9 0.064 0.497 0.492 BB/Bb vs. bb 4 0.854 (0.677–1.078) 1.33 0.184 57.0 0.054 0.624 0.427 BB vs. Bb/bb 4 0.990 (0.857–1.143) 0.14 0.888 0.0 0.861 0.624 0.513 B vs. b 4 0.918 (0.796–1.060) 1.17 0.243 50.5 0.089 0.070 0.126 Abbreviations: PCa, prostate cancer; OR, odds ratio; CI, confidence interval. Table 5 Results of the association between Bsm I polymorphism and PCa risk by different sources of controls Comparison Studies Overall effect Heterogeneity Publication bias OR (95% CI) Z-score p-value I2 (%) p-value Begg’s test Egger’s test Population-based BB vs. bb 17 0.963 (0.809–1.147) 0.42 0.675 48.5 0.016 0.653 0.737 Bb vs. bb 17 0.910 (0.779–1.065) 1.18 0.24 69.5 0 0.510 0.599 BB/Bb vs. bb 17 0.920 (0.787–1.075) 1.05 0.294 73.0 0 0.742 0.656 BB vs. Bb/bb 17 1.004 (0.909–1.109) 0.08 0.937 14.0 0.293 0.928 0.961 B vs. b 17 0.950 (0.850–1.062) 0.9 0.368 71.9 0 1 0.968 Hospital-based BB vs. bb 6 0.951 (0.505–1.790) 0.15 0.877 43.1 0.118 0.573 0.973 BB vs. bb 6 1.016 (0.600–1.721) 0.06 0.953 70.6 0.005 0.573 0.782 BB/Bb vs. bb 6 1.026 (0.607–1.732) 0.1 0.924 75.0 0.001 0.851 0.858 BB vs. Bb/bb 6 1.015 (0.806–1.279) 0.13 0.879 30.9 0.204 0.573 0.969 B vs. b 6 1.035 (0.666–1.607) 0.15 0.879 78.1 0 0.851 0.906 BPH BB vs. bb 6 0.515 (0.239–1.108) 1.7 0.09 67.7 0.015 0.624 0.287 Bb vs. bb 6 0.689 (0.534–0.890) 2.86 0.004 42.8 0.12 0.573 0.325 BB/Bb vs. bb 6 0.627 (0.411–0.954) 2.18 0.029 56.7 0.042 0.573 0.27 BB vs. Bb/bb 6 0.842 (0.649–1.093) 1.29 0.197 28.6 0.231 1 0.385 B vs. b 6 0.715 (0.530–0.965) 2.19 0.029 59.1 0.032 0.573 0.379 Note: Bold values indicate statistical significance. Abbreviations: PCa, prostate cancer; OR, odds ratio; CI, confidence interval; BPH, benign prostatic hyperplasia. Table 6 Results of the association between Bsm I polymorphism and PCa risk by different tumor stages Comparison Studies Overall effect Heterogeneity Publication bias OR (95% CI) Z-score p-value I2 (%) p-value Begg’s test Egger’s test Gleason score <7 (cases vs. controls) BB vs. bb 6 1.095 (0.490–2.449) 0.22 0.824 74.8 0.001 0.851 0.4 Bb vs. bb 6 1.051 (0.848–1.304) 0.46 0.649 28.6 0.22 0.573 0.072 BB/Bb vs. bb 6 0.942 (0.612–1.450) 0.27 0.787 69.4 0.006 0.348 0.147 BB vs. Bb/bb 6 1.097 (0.572–2.106) 0.28 0.78 66.8 0.01 0.851 0.426 B vs. b 6 0.957 (0.635–1.443) 0.21 0.835 81.1 0 0.573 0.193 Gleason score ≥7 (cases vs. controls) BB vs. bb 6 0.873 (0.607–1.253) 0.74 0.46 0.0 0.429 0.573 0.899 Bb vs. bb 6 0.787 (0.609–1.017) 1.83 0.067 55.3 0.048 0.348 0.15 BB/Bb vs. bb 6 0.742 (0.470–1.171) 1.28 0.2 63.1 0.019 0.039 0.191 BB vs. Bb/bb 6 0.920 (0.662–1.279) 0.48 0.621 0.0 0.483 0.851 0.874 B vs. b 6 0.793 (0.540–1.163) 1.19 0.235 69.2 0.006 0.091 0.253 Localized (cases vs. controls) BB vs. bb 6 0.855 (0.632–1.158) 1.01 0.312 47.9 0.088 0.851 0.478 Bb vs. bb 6 0.793 (0.627–1.003) 1.93 0.053 0.0 0.78 0.188 0.19 BB/Bb vs. bb 6 0.818 (0.661–1.012) 1.85 0.065 0.0 0.504 0.348 0.216 BB vs. Bb/bb 6 0.923 (0.701–1.215) 0.57 0.567 45.7 0.101 0.851 0.443 B vs. b 6 0.874 (0.748–1.022) 1.69 0.092 42.8 0.12 0.188 0.22 Aggressive (cases vs. controls) BB vs. bb 6 0.709 (0.461–1.092) 1.56 0.118 0.0 0.637 0.573 0.339 Bb vs. bb 6 0.753 (0.559–1.014) 1.87 0.062 54.7 0.051 0.348 0.466 BB/Bb vs. bb 6 0.693 (0.416–1.155) 1.41 0.159 54.9 0.05 0.851 0.799 BB vs. Bb/bb 6 0.785 (0.530–1.164) 1.20 0.229 0.0 0.685 0.851 0.323 B vs. b 6 0.711 (0.459–1.101) 1.53 0.127 56.7 0.042 0.573 0.603 Gleason score ≥7 vs. <7 BB vs. bb 8 1.207 (0.962–1.514) 1.63 0.103 53.8 0.043 0.548 0.632 Bb vs. bb 8 1.166 (0.989–1.375) 1.82 0.068 18.0 0.288 0.266 0.684 BB/Bb vs. bb 8 1.176 (1.008–1.373) 2.06 0.040 39.3 0.117 0.536 0.763 BB vs. Bb/bb 8 1.131 (0.919–1.392) 1.16 0.246 51.1 0.056 1 0.833 B vs. b 8 1.163 (0.928–1.457) 1.31 0.191 59.9 0.015 0.536 0.901 Aggressive vs. localized BB vs. bb 7 0.946 (0.692–1.295) 0.34 0.731 22.0 0.268 0.133 0.054 Bb vs. bb 7 0.971 (0.765–1.231) 0.806 0.25 23.0 0.254 0.368 0.338 BB/Bb vs. bb 7 0.984 (0.790–1.226) 0.14 0.887 4.0 0.396 0.035 0.031 BB vs. Bb/bb 7 0.966 (0.727–1.284) 0.24 0.812 23.7 0.256 0.133 0.071 B vs. b 7 0.981 (0.839–1.147) 0.24 0.808 26.9 0.224 0.035 0.001 Note: Bold values indicate statistical significance. 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