Genetically predicted benign prostate hyperplasia causally affects prostate cancer: a two-sample Mendelian randomization
Original Article

Genetically predicted benign prostate hyperplasia causally affects prostate cancer: a two-sample Mendelian randomization

Haijun Huang1#, Zhiquan Hu1#, Zhi Chen2, Yucong Zhang2, Chunguang Yang1

1Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; 2Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

Contributions: (I) Conception and design: C Yang, Y Zhang; (II) Administrative support: C Yang, Y Zhang; (III) Provision of study materials or patients: Z Hu; (IV) Collection and assembly of data: H Huang; (V) Data analysis and interpretation: H Huang, Z Hu, Z Chen; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

#The authors contributed equally to this work.

Correspondence to: Yucong Zhang, MD. Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Road, Wuhan 430030, China. Email: 406780532@qq.com; Chunguang Yang, MD. Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Road, Wuhan 430030, China. Email: cgyang-hust@hotmail.com.

Background: Benign prostate hyperplasia (BPH) and prostate cancer (PCa) share several similarities, including androgen dependency and parallel increases in prevalence with age. Although PCa lags by 15–20 years, no causal association has been identified between BPH and PCa. To investigate the potential causal links between BPH and PCa, this study was performed in a two-sample Mendelian randomization (MR) design.

Methods: We retrieved single-nucleotide polymorphisms (SNPs) associated with BPH from genome-wide association studies (GWAS), which were obtained from the Integrative Epidemiology Unit database, and conducted a two-sample MR analysis to explore the causal relationship between BPH and PCa. The exposure dataset included 13,118 BPH cases and 72,799 controls, while the outcome dataset comprised 9,132 PCa cases and 173,493 controls, all of European ancestry. Four SNPs were selected as instrumental variables (IVs) after stringent filtering for linkage disequilibrium and potential confounding factors. The causal effect was estimated using the inverse-variance-weighted (IVW) method, supplemented by sensitivity analyses to assess heterogeneity and pleiotropy.

Results: The IVW analysis revealed that genetically predicted BPH was causally associated with a 1.02-fold increased risk of PCa [95% confidence interval (CI): 1.0076–1.0286, P<0.001]. Sensitivity analyses, including MR-Egger regression and leave-one-out analysis, confirmed the robustness of these findings, with no significant heterogeneity or pleiotropy detected.

Conclusions: This study provides genetic evidence supporting a causal relationship between BPH and an increased risk of PCa. These findings suggest that BPH may contribute to the development of PCa, potentially guiding future clinical practices in screening, diagnosis, and treatment strategies for BPH patients to mitigate PCa risk. Further validation in diverse populations and clinical studies is warranted to confirm these findings.

Keywords: Benign prostate hyperplasia (BPH); prostate cancer (PCa); causal effects; Mendelian randomization (MR); instrumental variable (IV)

Submitted Nov 24, 2024. Accepted for publication Feb 23, 2025. Published online Mar 26, 2025.

doi: 10.21037/tau-2024-673

Highlight box

Key findings

• According to the result of random effect inverse-variance-weighting analysis, genetically predicted benign prostate hyperplasia (BPH) was causally related to a 1.02-fold risk (95% confidence interval: 1.0076–1.0286, P<0.001) of prostate cancer (PCa).

What is known and what is new?

• Although PCa lags by 15–20 years, BPH and PCa show a parallel elevation in prevalence with age. In addition, BPH and PCa share some risk factors, such as genetic variation, metabolic disorder, and chronic inflammation.

• Based on genetic data, this study provides causal evidence that genetically predicted BPH increases the risk of PCa.

What is the implication, and what should change now?

• Our current study identified a causal relationship genetically predicted between BPH and PCa, which may improve the accuracy of prognostication and expedite surgical treatment for relevant BPH patients to remove the potentially carcinogenic prostate tissue and prevent aggressive cancer development in the future.

Introduction

Background

Prostate cancer (PCa) is the most prevalent cancers among male patients worldwide, and the second leading cause of cancer mortality in men (1). Although over 80% of PCa patients have a history of benign prostate hyperplasia (BPH) (2), no causal association has been identified between BPH and PCa. Whether BPH can be considered a precursor of PCa remains controversial (3). A study even considered BPH as a potential protective factor against PCa (4). There are several similarities between BPH and PCa. For example, both are androgen-dependent diseases and can respond to antiandrogen treatment regimens. Although PCa lags by 15–20 years, BPH and PCa show a parallel elevation in prevalence with age. In addition, BPH and PCa share some risk factors, such as genetic variation, metabolic disorder (5), and chronic inflammation (6). Relatively, BPH may increase the diagnosis chance of incidental PCa, as BPH patients may ask for treatment to relieve their symptoms (7).

Rationale and knowledge gap

Mendelian randomization (MR) is a design that uses single-nucleotide polymorphisms (SNPs) as genetic instrumental variables (IVs) to investigate the causal association between exposures (i.e., BPH) and outcomes (i.e., PCa). Because SNPs are allocated randomly during conception, they are mostly unrelated to confounders. In addition, due to the random allocation of effect alleles in the MR design, a natural randomized controlled trial can be mimicked by using MR. An MR design can help avoid reversed causality because genetic variants used to proxy the effect of the exposure cannot be modified by the onset and progression of the outcome (8). Therefore, MR analysis may help investigate and understand the relationship between BPH and PCa.

Objective

In the current MR study, data from the Integrative Epidemiology Unit (IEU) open genome-wide association study (GWAS) database were used to investigate the causal effect of BPH on PCa. Our results may help clinical screening, diagnosis and treatment in the future. We present this article in accordance with the STROBE-MR reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2024-673/rc).

Methods

Two-sample MR

In this study, we adopted two-sample MR, in which the exposure dataset and outcome dataset were obtained from independent samples drawn from population with similar ancestry (9). MR analysis is conducted based on three assumptions. First, the IVs must be strongly correlated with exposure. Second, IVs should not be associated with confounding factors that may be related to exposure or outcome. Third, IVs should not be directly correlated with outcome, that is, IVs can only affect outcome through the exposure factors (10). Here, exposure refers to BPH, and the outcome refers to PCa. This hypothesis is shown in Figure S1. We tested the causal association between BPH and PCa via the effect of SNPs on PCa through BPH.

Exposure data and outcome data sources

Three GWAS datasets for BPH were obtained from the IEU database (https://gwas.mrcieu.ac.uk/). The latest dataset with the largest SNP number was chosen for subsequent analysis (dataset id: finn-b-N14_PROSTHYPERPLA). The original study of this dataset enrolled 85,917 individuals of European ancestry including 13,118 cases and 72,799 non-cases, with a total of 16,378,414 SNPs detected.

Four GWAS datasets for PCa were also obtained from the IEU database. The latest dataset was chosen for subsequent analysis (the dataset id: ieu-b-4809), a dataset from a study conducted by the UK Biobank Consortium. This study enrolled 182,625 individuals of European ancestry including 9,132 PCa cases and 173,493 non-cases, with a total of 12,097,504 SNPs detected (11). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Ethical review and approval were waived for this study as all the data were publicly accessible. Informed consent was obtained from all individuals in the original GWAS.

Genetic instrument selection

The threshold of the P value was set at 5×10−8 to identify genetic variants with significant genome-wide association with exposure (12). To avoid linkage disequilibrium (13), SNPs (R2<0.001 with any other associated SNP within a 10-kb window) were clumped (14). Ten SNPs were finally obtained from the exposure dataset. The SNPs were subsequently searched on the website (http://www.phenoscanner.medschl.cam.ac.uk/) to identify SNPs which were associated with confounding factors correlated with both exposure and outcome. Consequently, we excluded one SNP associated with other cancers, one SNP associated with self-reported enlargement of the prostate, and two SNPs associated with prostate specific antigen elevation. In addition, we excluded one SNP with an effective allele frequency less than 0.01. The statistical association of the remaining genetic variants with PCa was then obtained from the outcome dataset. According to the data from this dataset, a variant showing a potential association with PCa was excluded (P<5×10−8). Finally, 4 SNPs were selected as IVs in the subsequent analysis, including rs6738440, rs10503728, rs10740998, and rs17101982.

The F statistic of each IV was calculated to judge the existence of weak IV bias, which indicates a weak association between selected genetic variants and exposure. The possibility of weak IV bias was considered to be small if the F statistic >10 (15).

F statistic can be calculated by

F=R2×(n−k−1)/[k×(1 − R2)]

R2 can be calculated by

R2=2×EAF×(1−EAF)×beta2

(EAF, effect allele frequency; beta, effect size on the exposure; n, sample size; k, number of instruments).

When F statistic was less than 10, indicated a weak IV bias. While all the F statistics in this study were above 10.

The selection of IVs is summarized in Figure 1. Information on the selected IVs in BPH and PCa is provided in Table 1.

Figure 1 Schematic diagram for the Mendelian randomization analysis investigating effects of benign prostate hyperplasia on prostate cancer. EAF, effect allele frequency; GWAS, genome-wide association study; MR, Mendelian randomization; PSA, prostate specific antigen; SNPs, single nucleotide polymorphisms.

Table 1

Associations of single nucleotide polymorphisms with benign prostate hyperplasia and prostate cancer

SNP EA NEA EAF Nearby gene Hyperplasia of prostate Prostate cancer
Beta SE P Beta SE P
rs10503728 C A 0.3670 SLC25A37 −0.1045 0.0166 2.89×10−10 −0.0014 0.0007 0.049
rs10740998 C T 0.8228 DNAJC1 −0.1364 0.0209 6.45×10−11 −0.0035 0.0008 2.71×10−5
rs17101982 G C 0.0872 FGFR2 −0.1754 0.0286 8.68×10−10 −0.0052 0.0016 0.001
rs6738440 G A 0.2441 BCL11A −0.1185 0.0186 1.91×10−10 −0.0007 0.0008 0.37

A, adenine; C, cytosine; EA, effect allele; EAF, effect allele frequency; G, guanine; NEA, noneffect allele; SE, standard error; SNP, single nucleotide polymorphism; T, thymine.

Statistical analysis

The two-sample MR analysis was conducted in R (version 4.0.2) by the TwoSampleMR package (16), which could be downloaded from the website (https://mrcieu.github.io/TwoSampleMR/index.htm). Each Wald ratio of IVs was obtained after dividing the beta coefficient for the effect of the SNP on the outcome by the beta coefficient for the effect of the SNP on the exposure (17). The Wald ratio refers to the beta coefficient for the effect of the exposure on the outcome (18). The inverse-variance-weighted (IVW) method, which meta-analyses individual Wald ratios of IVs, was selected as one of the principal two-sample MR analyses to estimate causal effects. Besides, there were some other methods for testing the causal effects, including MR-Egger regression, simple mode method, weighted median method and weighted mode method (18). Notably, the IVW method held the prior position among all the statistical methods to test the causal effects, when no potential pleiotropy was detected. In IVW, when the P value <0.05, the causal association of BPH with PCa was considered to be significant.

In order to test the uncertainty affecting the robustness of the results and the validity of the model composed of the selected IVs, we performed the sensitivity analysis (19), which consists of the analysis of heterogeneity, pleiotropy and leave-one-out analysis. Heterogeneity was detected by the IVW method and MR-Egger regression. The heterogeneities were assessed by the Cochran Q test (20). A P value <0.05 was considered significant heterogeneity. The potential pleiotropic effects of the SNPs, which were used as IVs, were assessed by MR-Egger regression (21). The intercept term in MR-Egger regression could indicate whether directional horizontal pleiotropy is driving the results of an MR analysis (22). A P value <0.05 indicated a significant horizontal pleiotropy, which suggested that those IVs could affect the outcome directly or through the potential confounding factors (23). In addition, we performed a leave-one-out analysis (24), where the IVW method was performed again with each SNP left out in turn, to identify if a single SNP was driving the causal association, in other words, to assess whether a single SNP significantly affected the result.

Results

The results of MR estimated from each single SNP for assessing the causal effect of BPH on PCa are displayed in Figure 2A and Table S1. The results of MR estimated from the five methods for assessing the causal effect of BPH on PCa are shown in Table 2. The result from IVW indicated that genetically predicted BPH lead to a 1.02-fold risk of PCa [95% confidence interval (CI): 1.0076–1.0286, P<0.001, Table 2]. The scatter plot in Figure 2B indicated that the SNP effect on PCa was intensified when the SNP effect on BPH increased.

Figure 2 The causal effects of BPH on PCa. (A) The causal links between BPH and PCa obtained by the random effect inverse variance weighted method. (B) Scatter plot of the SNP effect on BPH and PCa. BPH, benign prostate hyperplasia; MR, Mendelian randomization; PCa, prostate cancer; SNP, single-nucleotide polymorphism.

Table 2

Association of genetically predicted benign prostate hyperplasia of prostate cancer

Method nSNPs Beta SE P OR OR_lci95 OR_uci95
MR Egger 4 0.0616 0.0270 0.15 1.0636 1.0088 1.1213
Weighted median 4 0.0158 0.0050 0.002 1.0160 1.0060 1.0261
Inverse-variance-weighted 4 0.0179 0.0053 <0.001 1.0181 1.0076 1.0286
Simple mode 4 0.0253 0.0090 0.07 1.0256 1.0076 1.0440
Weighted mode 4 0.0116 0.0077 0.23 1.0117 0.9966 1.0270

lci, lower confidence interval; MR, Mendelian randomization; OR, odds ratio; SE, standard error; SNP, single nucleotide polymorphism; uci, upper confidence interval.

According to the results of sensitivity analysis, significant heterogeneity (Table 3) and pleiotropy (Table 4) were not detected. The leave-one-out analysis did not identify influential SNPs in the BPH-PCa causal association. The results were robust when excluding any one of the SNPs (Figure 3).

Table 3

Results of heterogeneity tests

Method Q value Q_P value
MR Egger 2.8690 0.24
Inverse-variance-weighted 6.7352 0.08

MR, Mendelian randomization.

Table 4

Results of pleiotropy tests

Method Egger intercept P value
MR Egger −0.0055 0.24

MR, Mendelian randomization.

Figure 3 Results of leave-one-out sensitivity analysis. MR, Mendelian randomization.

Discussion

Key findings

In the current MR study, we found that genetically predicted BPH causally elevated the risk of PCa in a sample of 9,132 cases and 173,493 non-cases, which provided novel evidence to support the causal association between BPH and PCa. Our findings also support predominant observational clinical studies.

Strengths and limitations

There are some advantages and limitations in this study. First, this study demonstrated the causal relationship between BPH and PCa by a two-sample MR design, which avoided residual confounding and other potential biases. Second, the studied population was confined to individuals of European ancestry, which decreased the population bias, whereas this might also limit the generalizability of our findings to other populations. Further validation in the population of other ancestries is needed. Third, in our study, we identified 10 SNPs with significant genome-wide association with BPH in finn-b-N14_PROSTHYPERPLA. However, a study identified 15 SNPs with significant genome-wide association with BPH in UK Biobank dataset (2), in which the criteria for linkage disequilibrium was set as R2<0.2. While in our study, R2<0.001 with any other associated SNP within a 10-kb window was set as the criteria for linkage disequilibrium. The differences in the identification of SNPs with significant genome-wide association with BPH may influence the analysis of causality between BPH and PCa. Forth, we were not involved in the creation of the original datasets used in this study, only summary statistics were analyzed and the nonlinear association between BPH and PCa could not be evaluated in the current study, which limits the full understanding of their interaction. Last but not least, our study provides a novel evidence for the causality between BPH and PCa, which was derived from a unique approach. This causality should be validated by further clinical studies.

Comparison with similar researches and explanations of findings

The association between BPH and PCa was first mentioned in study based on prostate glands in the 1950s, which reported that BPH was found in 80% and 45% of cadavers with or without PCa, respectively (2). A meta-analysis with 21 studies enrolled revealed a conspicuous association between BPH and PCa (25). Despite the fact that the two diseases share features such as hormone-dependent growth and response to treatment with antiandrogen therapy, BPH is generally not considered a premalignant lesion (26). In addition to cross-sectional evidences, longitudinal studies were also conducted to investigate the association between BPH and risk of PCa incidence. A study with 5,068 men (no major comorbidities, prostate specific antigen <4, and International Prostate Symptom Score <20) were followed up for seven years found that BPH was not associated with PCa risk (7). However, a study indicated that BPH with a faster growth speed was a risk factor for developing PCa and higher clinical PCa grade (6). Moreover, a study enrolled 3,009,258 Danish men who were followed for up to 27 years and concluded that BPH is associated with a two- to three-fold increased risk of PCa (26). One possible explanation is that BPH may be associated with a subset of PCas that develop in the transition zone, perhaps in relation to atypical adenomatous hyperplasia (2). In addition, some studies have reported that gene expression could be a causal factor in the development of PCa from BPH and may even affect the degree of malignancy of PCa (27,28). A study identified seven hub genes, that can be used as markers for predicting the occurrence of PCa from BPH (29). Another study also demonstrated that prostate volume is a reason for the aggressiveness of PCa. PCa located in smaller glands is more aggressive (30), which indicates that BPH may influence the malignancy degree of PCa.

Implications and actions needed

It is reported that patients with BPH with and without surgical intervention experienced different prostate carcinoma risk patterns (31). Patients with BPH who did not receive surgical intervention experienced significant excesses of both prostate carcinoma incidence. Those undergoing transvesicular adenomectomy had a significant 23% lower mortality, and those undergoing transurethral resection had a significant 17% lower mortality. Our current study identified a causal relationship genetically predicted between BPH and PCa, which may improve the accuracy of prognostication (32) and expedite surgical treatment for relevant BPH patients to detect and remove the potentially carcinogenic prostate tissue and prevent aggressive cancer development in the future.

Conclusions

The genetically-predicted BPH causally elevates the risk of PCa in respect to the four SNPs. This study provides additional insights regarding the link BPH and PCa, suggesting that genetically predicted BPH increases the risk of PCa.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE-MR reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2024-673/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2024-673/prf

Funding: This study was supported by the National Natural Science Foundation of China (grant No. 81702989).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2024-673/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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Cite this article as: Huang H, Hu Z, Chen Z, Zhang Y, Yang C. Genetically predicted benign prostate hyperplasia causally affects prostate cancer: a two-sample Mendelian randomization. Transl Androl Urol 2025;14(3):661-668. doi: 10.21037/tau-2024-673

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