Genetic correlation between prostate cancer and central obesity: a Mendelian randomization study

Genetic correlation between prostate cancer and central obesity: a Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic correlation between prostate cancer and central obesity: a Mendelian randomization study Chen Shuai, Chen Jingya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5728950/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Our aim was to apply Mendelian randomization to find possible causal relationships between prostate cancer and central obesity. Based on waist-to-hip ratio GWAS and prostate cancer GWAS data as the experimental group, we used 8 MR methods to explore the possible causal relationship between waist-to-hip ratio and prostate cancer. Situation with P < 0.05 was suggested causal relationship. Gene annotation and Gene Ontology (GO) molecular functional enrichment analysis of meaningful SNPs were performed to find potentially significantly molecular function and enriched genes. Finally, we searched for the overlapping molecular functions of the experimental group and the validation group and the overlapping genes enriched in them. All the 8 methods MR methods showed causal relationship between waist-to-hip ratio and prostate cancer in experimental group. The GO enrichment analysis showed that the molecular function of the main enriched pathway, called DNA-binding transcription activator activity, RNA polymerase II-specific, overlapped in the experimental group and the validation group. Our results manifest that waist-to-hip ratio has a potential causal relationship with prostate cancer. MR waist-to-hip ratio prostate cancer central obesity Figures Figure 1 Figure 2 Figure 3 Introduction Prostate cancer (PCa) has the highest incidence among men worldwide[ 1 ]. It is also the cancer with the highest incidence and the third highest mortality in Europe [ 2 ]. About 299,010 new cases and 35,250 deaths of PCa were reported in the United States in 2023[ 3 ]. Potential and especially controllable factors related to the development and progression of PCa are significant. Initial factors such as age, race, family history, and genetic factors have been found to be related with PCa.[ 4 – 5 ]. In recent years, the correlation between PCa and controllable factors such as diet, physical activity, obesity and waist-to-hip ratio has been attracting increasing attention[ 6 ]. Previous studies have found that obesity is closely related to the risk of PCa.The Continuous Update Project expert report has been presented by the World Cancer Research Fund (WCRF) and the American Institute for Cancer Research (AICR) in 2018, which aims to prevent cancer through diet, nutrition and physical activity[ 7 – 8 ]. Obesity affects a variety of hormone levels and metabolic pathways such as testosterone and insulin, which may promotes the growth of hormone-dependent cancer cells, and may cause a low-grade chronic inflammatory state and promotes the progression of aggressive tumors[ 8 – 10 ]. The indicators of obesity include waist circumference, BMI, and waist-to-hip ratio. Among them, waist-to-hip ratio, which can suggest concentric obesity, is more likely to predict the risk of PCa[ 11 – 14 ]. However, these studies are mostly observational studies or controlled trials, which cannot reflect the one-way causal relationship between waist-to-hip ratio and PCA, and the results could be affected by confounding factors. Mendelian randomization (MR) research the causal relationship between exposure factor and outcome through gene loci[ 15 ]. Previous studies have successfully applied MR methods to explore the causal relationship between obesity and cancer[ 16 – 17 ]. MR methods has been used to explore the causal relationship between obesity and PCA[ 18 – 19 ], but studies on the causal relationship between waist-to-hip ratio and PCA are relatively rare[ 20 ], and methods of those research did not involve the application of reverse MR method, external data set verification and enrichment analysis. To explore the causal relationship between waist-to-hip ratio and prostate cancer, it is essential to apply external validation to improve the reliability of the results, and enrichment analysis should be applied to reflect possible gene expression pathways[ 21 – 23 ]. Based on the waist-to-hip ratio and prostate cancer Genome-wide association study (GWAS) data as the experimental group, we used a variety of MR methods to explore the possible one-way causal relationship between waist-to-hip ratio and prostate cancer, and enrichment analysis was applied to find possible enrichment functions and potential genes that appeared to be involved in it. In addition, we used another set of waist-to-hip ratio GWAS data as an external validation group for Mendelian analysis, and tried to find possible overlapping enrichment pathways and genes. Materials and methods The whole research process is reflected in Figure 1. In order to explore the potential causal relationship between waist-to-hip ratio and prostate cancer, we used a variety of MR methods to study the causal relationship between waist-to-hip ratio data and prostate cancer data in the experimental group and the validation group. The waist-to-hip ratio data and prostate cancer data of the experimental group and the validation group were independent GWAS data. The single nucleotide polymorphisms (SNPs) for MR studies must meet three necessary assumptions with waist-to-hip ratio and prostate cancer data. We performed genetic interpretation of these SNPs and explored the molecular functions that may be enriched in the gene. Search for possible molecular functions and action genes in the overlapping results of the experimental group and the validation group. Data inclusion The GWAS data we used were from Integrative Epidemiology Unit (IEU) GWAS database (https://gwas.mrcieu.ac.uk/). All data were derived from published studies or published GWAS summary data. It provides ethical approval and informed consent, and this study does not require separate ethical approval. To explore the causal relationship between prostate cancer and waist-to-hip ratio, we used the waist-to-hip ratio data of the experimental group and the waist-to-hip ratio data of the validation group as the exposure data and the prostate cancer data as the outcome. The GWAS data of prostate cancer data contains 182,625 European individuals, ID name is ieu-b-4809, a total of 12,097,504 SNPs; the GWAS data ID of the waist-to-hip ratio data in the experimental group was named ieu-b-4809. There were 85,978 European individuals and 7,908,954 SNPs in the GWAS data. The ID of the waist-to-hip ratio data in the validation group was ebi-a-GCST90029009, containing 11,555 Hispanic or Latino individuals and a total of 30,077,714 SNPs. Tool variable selection Using MR analysis to explore the relationship between waist-to-hip ratio and prostate cancer requires screening instrumental variables to meet three necessary assumptions. Correlation test:We applied correlation analysis to calculate the correlation between SNPs and exposure in GWAS data. We defined SNPs with P10 were defined as not weak instrumental variable bias and included. If too many exposure-related SNPs were generated, subsequent MR analysis would be affected. To reduce the number of SNPs included, we defined SNPs with P<1* -10 and F<10 as strongly associated with exposure as a stricter criteria. Independence hypothesis: Due to the possible confounding factors affecting the causal analysis of waist-to-hip ratio and prostate cancer, we searched the Phenoscanner database (http://www.phenoscanner.medschl.cam.ac.uk/)) to exclude potential pleiotropic instrumental variables. Linkage disequilibrium test was performed on the above included SNPs, and SNPs with r 2 10000 were defined as independent and included. Exclusive hypothesis: We calculated the correlation between the above included SNPs and the outcome, and excluded SNPs with P<1*10 -6 to ensure that the included SNPs can only affect the outcome through exposure. In order to ensure the reliability of the results of Mendel 's randomization analysis, we preprocessed the SNPs selected in the above steps. For the extracted exposure and outcome GWAS data, we apply the harmonise function to preprocess them so that their effect alleles remain uniform. Cochran 's Q test in IVW was used for analysis, and the condition of P > 0.05 was defined as no heterogeneity in exposure and outcome data. The pleiotropy method was used to define that there was no horizontal pleiotropy for data with P value > 0.05. We used the Outlier-corrected method to eliminate SNPs with P values less than 1 in the model to correct the effect of outliers on MR analysis results. MR analysis After the above-mentioned three necessary tests and data preprocessing, the common SNPs in the selected exposure and outcome data were included for the MR analysis. The waist-to-hip ratio of the experimental group was used as exposure, and prostate cancer was used as the outcome. We applied 8 MR methods to explore the causal relationship between them, including MR Egger, Weighted median, Penalised weighted median, Inverse variance weighted (IVW), IVW radial, Inverse variance weighted (multiplicative random effects), Inverse variance weighted (fixed effects). P 0.05, we chose IVW (multiplicative random effects) method as the main results, otherwise we chose IVW (fixed effects) method as the main results. Enrichment analysis In order to explore the effect of SNP on prostate cancer caused by waist-to-hip ratio at the genetic level, we tried to find the upstream and downstream adjacent genes of SNPs to perform genetic annotations of SNPs related to exposure and outcome. We performed Gene Ontology (GO) molecular functional enrichment analysis of these genes using Metascape. Metascape is a specially designed web-based portal that provides comprehensive resources for annotating and analyzing gene lists, thereby enhancing the comprehending of the functions of the genes studied. Hypergeometric test is used for tissue enrichment analysis, aiming at gene interpretation and enrichment analysis of meaningful SNPs, and finding potential significantly enriched functions or specific features. In addition, we also look for genes enriched in function. External verification We used correlation analysis to calculate the correlation between SNPs and exposure in the waist-to-hip ratio data of the validation group with ID ebi-a-GCST008050. SNPs with P < 1*10 -6 , LD < 0.001 and F < 10 were defined as strongly correlated with exposure and included. If there are many strongly correlated SNPs, in order to reduce the included SNPs, only SNPs with P < 1*10 -10 , LD < 0.001 and F < 10 were defined as strongly correlated with exposure and included. We used the same 8 MR methods as the above experimental group study to analyse the causal relationship between the waist-to-hip ratio and prostate cancer in the validation group, including MR Egger, Weighted median, Penalised weighted median, Inverse variance weighted, IVW radial, Inverse variance weighted (multiplicative random effects), Inverse variance weighted (fixed effects). Gene annotation and GO enrichment analysis of meaningful SNPs were performed to find potentially significantly molecular function and enriched genes. Result Tool variable selection In order to screen out SNPs closely related to exposure and outcome, we defined SNPs with P < 1*10 -6 , LD < 0.001 and F < 10 as strongly associated with exposure.A total of 37 SNPs were screened to be strongly associated with exposure (Supplementary Table 1). After applying linkage disequilibrium test, we found all SNPs had good independence, therefore no SNP was eliminated. The P values of the SNPs included in the association analysis with the outcome were all greater than 1*10 -6 (Supplementary Table 2), suggesting that SNPs can only affect the outcome through exposure, and no SNP was eliminated. The result of Cochran 's Q test was P = 0.316, indicating that there was no heterogeneity between exposure and outcome. The P values of the results of the pleiotropy method and the MR-PRESSO method were 0.163 and 0.336, respectively, indicating that there was no horizontal pleiotropy in the sample. MR analysis All the 8 methods MR methods, including MR Egger, Weighted median, Penalised weighted median, Inverse variance weighted, IVW radial, Inverse variance weighted (multiplicative random effects), Inverse variance weighted (fixed effects), showed causal relationship between waist-to-hip ratio and prostate cancer (Table 1). Among them, the Maximum likelihood and Inverse variance weighted (fixed effects) methods significantly suggested a positive causal relationship between waist-to-hip ratio and prostate cancer (P = 0.005, bete = 0.212, or = 1.237 ; p = 0.005, bete = 0.205, or = 1.227) (Fig. 2a). In the reverse MR analysis, the P values of these MR methods were all greater than 0.05, manifesting that there was a one-way causal relationship between waist-to-hip ratio and prostate cancer (Supplementary Table 3). SNP related genes and enrichment analysis We performed genetic explanations for SNPs which were significantly associated with exposure and outcome in the experimental group, and Annotated gene were showed in Supplementary Table 4. The result of GO enrichment analysis showed that the causal relationship between waist-to-hip ratio and prostate cancer was mainly enriched in four molecular functions which are nuclear receptor coactivator activity, heparin binding, DNA-binding transcription activator activity and RNA polymerase II-specific and phospholipid binding (Fig.3a). Multiple genes were enriched in these molecular functions, as shown in Supplementary Table 5. External verification When we first defined SNPs with P<1*10 -10 , LD<0.001 and F<10 as strongly correlated with exposure, a total of 416 SNPS were screened out, which was too many. Considering the large number of SNPs in the validation group data, we defined SNPs with P < 1 * 10 -10 , LD < 0.001, and F < 10 as strongly associated with exposure, and a total of 201 SNPs were strongly associated with exposure (Supplementary Table 6). After applying linkage disequilibrium test, we found that all SNPs had good independence, so no SNP was eliminated. Among these SNPs, 195 SNPs were included in the oucome. The P value of the correlation analysis between rs10107982 and the outcome was greater than 1 * 10 -10 , which did not satisfy the exclusive hypothesis, and led to the exclusion of this SNP. The remaining 194 SNPs were included to ensure that SNPs could only affect the outcome through exposure (Supplementary Table 7). The P value of the results of the pleiotropy method was 0.931, suggesting that there was no horizontal pleiotropy in the sample. The causal relationship between waist-to-hip ratio and prostate cancer in validation group was analyzed by using the above 8 MR methods(Table 1). The results of Maximum likelihood and Inverse variance weighted (fixed effects) methods suggest a causal relationship between the two. (P = 0.002, beta = 0.008, or = 1.008) ; p = 0.002, beta = 0.008, or = 1.008) (Fig.2b). We performed genetic explanations for SNPs that were significantly associated with exposure and outcome in the validation group (Supplementary Table 4). The GO enrichment analysis of the annotated genes showed that the molecular function of the main enriched pathway, called DNA-binding transcription activator activity, RNA polymerase II-specific, overlapped with the main analysis (Figure 3b). There were 31 genes enriched in this molecular function (Supplementary Table 8), of which 6 genes overlapped with the genes enriched in the experimental group data results, namely HOXC4, HOXC10, HOXC11, HOXC13, CEBPG and JUND (Supplementary Table 8). Discussion Based on the large-scale GWAS data of European population, we used the bi-directional two-sample MR to explore the cause-and-effect relationship between waist-to-hip ratio and PCa. The results suggested that there was a significant positive causal relationship between waist-to-hip ratio and prostate cancer, and no potential reverse causality was observed. We performed genetic interpretation by looking for genes adjacent to SNPs which are strongly associated with waist-to-hip ratio and prostate cancer. GO enrichment analysis of these genes found that a series of GO biologic processes play vital roles in potential causality between waist-to-hip ratio and prostate cancer, including nuclear receptor coactivator activity, heparin binding, DNA-binding transcription activator activity, RNA polymerase II-specific and phospholipid binding. To enhance the reliability of the results, we implemented external verification. We used large-scale GWAS data of Hispanic or Latino populations as exposure, and repeated the above methods to explore its causal relationship with prostate cancer. The results showed that there was still a positive causal relationship between waist-to-hip ratio and prostate cancer. Many pivotal GO biologic processes were found after GO enrichment analysis, and the DNA-binding transcription activator activity,RNA polymerase Il-specific pathway overlaps with the results of the experimental group enrichment analysis, suggesting that it is a potential functional way for the gene action of prostate cancer caused by waist-hip ratio. Many studies have shown that obesity is a risk factor for prostate cancer. Existing data from the World Cancer Research Fund have reported that body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) may be important factors for advanced PCa [24]. Eric et al.found that abdominal obesity increased the risk of invasive prostate cancer based on the application of abdominal circumference in Canadian population[25]. Based on the application of waist-to-hip ratio in the southern French population, Lavalette et al.found that abdominal obesity was associated with the incidence of prostate cancer, especially invasive prostate cancer[13]. In the study of the relationship between obesity and prostate cancer in the Chinese population, Bo Tang et al. found that waist-to-hip ratio was more associated with prostate cancer than BMI[12]. In summary, our results are broadly in accord with the above observational studies, that is, abdominal obesity as indicated by waist-to-hip ratio may be closely related to an increased risk of prostate cancer. Abdominal obesity may affect the occurrence of prostate cancer through inflammation, insulin levels or endocrine disruptors. Inflammation plays a crucial part in the malignant transformation of prostate cells. From a biochemical perspective, the microenvironment that promotes inflammation makes the inflammatory state associated with visceral obesity. Abdominal obesity is associated with chronic inflammation based on the accumulation of immune cells (such as macrophages and white blood cells) between fat cells. These immune cells produce some pro-inflammatory cytokines and interleukins, such as IL-6, IL-8, TNFαand CRP (C-reactive protein) [26]. This mechanism causes macrophages to surround adipocytes, called “coronary adipocytes”, and affect the microenvironment of surrounding adipocytes in an autocrine and paracrine manner, thereby inhibiting the expression of adiponectin genes. Adiponectin is one of the most important activators of AMPK and PPARα, which can stimulate fatty acid oxidation, reduce inflammation and regulate cancer survival [27]. Studies have shown that the progression of prostate cancer is closely related to plasma growth factors[28]. Insulin is one of the most important and studied growth factors associated with prostate cancer and obesity. Men with high serum insulin levels and abdominal obesity have a higher risk of prostate cancer[29]. Biological and epidemiological studies have found that endocrine disruptor exposure is associated with obesity, MS, type 2 diabetes and cancer[30]. For example, bisphenol A, used as an additive in polycarbonate plastics (wine bottles, food containers, cans of paint, etc.), is a common endocrine disruptor associated with several cancers[31]. Experimental animals exposed to low doses of bisphenol a have diabetes, reproductive problems (precocious puberty, sperm reduction shame), obesity, breast cancer and prostate cancer, while there are few human studies related to bisphenol a exposure and cancer risk, and more studies are needed. MR design has been used in many studies to find causal relationship between obesity and cancer. We used the MR study to explore the genetic evidence of the causal relationship between waist-to-hip ratio and prostate cancer. Previous studies have found that obesity indicators such as waist circumference, BMI and waist-hip ratio have a causal relationship with breast cancer, ovarian cancer, prostate cancer, lung cancer, colorectal cancer, endometrial cancer and thyroid cancer[32]. Chen, Young et al. reported that there is no causal relationship between waist-to-hip ratio and prostate cancer, which was differ from our results[20]. We discovered that there was a significant positive causal relationship between waist-to-hip ratio and prostate cancer. Different exposure sources and selection method of SNP may be the reasons for this discrepancy. Chi Yuan et al. obtained the waist circumference and hip circumference data of the target population from the UK database (UKB), and then used the waist circumference divided by the hip circumference to obtain the waist-hip ratio as the exposure data in the research, which may cause bias in the results. We used the independent waist-to-hip ratio data named ieu-b-4809 in the Integrative Epidemiology Unit (IEU) database as the exposure data. In addition, to ensure the robustness of the research results, experimental group exposure and outcome data were European populations, while the validation group exposure data was Spanish and Latino populations. After the MR analysis of the experimental and the validation group data, we performed gene interpretation on the SNPs generated by the two results and further performed enrichment analysis. We found that there may be a large number of GO biological processes that work in the potential causality from waist-to-hip ratio to prostate cancer. There was one Molecular Function overlap in the results of the two enrichment analyses, DNA-binding transcription activator activity, RNA polymerase II-specific, suggesting that it may be a potential way for waist-to-hip ratio to increase the risk of prostate cancer. This is a DNA-binding transcription factor activity that activates or increases transcription of specific gene sets transcribed by RNA polymerase II. Enrichment analysis found that many genes were enriched in this molecular function, among which six genes overlapped, namely HOXC4, HOXC10, HOXC11, HOXC13, CEBPG and JUND. Previous studies have found that HOX family genes are closely related to obesity, and the HOXC gene cluster is up-regulated in subcutaneous adipose tissue[33]. Many studies have shown that HOX family genes play roles in the growth of prostate. Miller et al.confirmed the overexpression of HOXC4 in prostate malignant cell lines and lymph node metastases by using specific reverse transcription PCR for HOXC4[34]. Zhifei Luo et al.found that HOXC4 is a key gene that regulates the proliferation of prostate cancer. HOXC4 and HOXB13 are co-localized with AR, and these two transcription factors have previously been shown to contribute to the development of prostate cancer[35].Previous studies have found that the HOXC11 gene is essential for the embryonic development of the prostate[36]. Yuefu Han et al.found that hsa_circ_0075542 inhibited the malignant characteristics of LNCa P and PC3 cells and promoted apoptosis by acting as a competitive endogenous RNA of miR-1197. The hsa_circ_0075542/miR-1197 axis may function through HOXC11[37]. In the study of endocrine quality resistance of breast cancer, Azlena Ali et al.found that PSAP, the target gene of HOXC11, is an AR activator, and PSAP activates AR in prostate cancer cells, which will be proved in prostate cancer samples in future. Maozhang Li et al.found that the HOX family exists independently in 25 TFs, among which HOXC10 and HOXC13 have unique clinical features, and PCa patients with high expression of HOXC13 have a poor prognosis. HOXC13 is highly expressed in tumor tissues and is associated with Gleason score and pathological grade. In vitro experiments showed that silencing HOXC13 inhibited the function of 22RV1 and DU145 cells by inducing cell DNA damage and activating the cGAS / STING / IRF3 pathway, thereby regulating the progression of prostate cancer[38]. Xiaoyu Zhang et al. used the results of PAINTOR meta-analysis to conduct a Bayesian statistical fine mapping study on WHRadj BMI, trying to further understand the mechanism of central obesity, and found that SNPrs12608504 (JUND) is particularly important[39]. Jun D is a member of the AP-1 family and is essential for the proliferation of prostate cancer (PCa) cells[40]. Bethtrice Elliott et al. found that JunD is a key regulator of cell cycle progression, and inhibition of its target genes may be an effective way to block the occurrence of prostate cancer[41]. At present, no previous literature has found that CEBPG is related with prostate cancer or obesity. It is expected to be found in future research. The advantages of this study are as follows: First, in order to minimize possible false positive results, two independent aggregated data were used for the research, and the same MR and enrichment analysis methods were performed using external validation data sets to enhance the robustness of the results. Secondly, eight methods of MR analysis were performed on the three groups of GWAS summary data, and the population of the external validation data set was different from the main research population, which increased the credibility of the results. Thirdly, GO enrichment analysis was used to find potential enriched genes and explore the possible molecular function of waist-to-hip ratio leading to prostate cancer. The limitations of this study are as follows: First, most of the GWAS summary data come from European and Hispanic or Latino populations, and have not yet involved other populations such as Asian populations. Our findings should be applied cautiously to populations in other regions. Secondly, we explored the causal relationship between waist-to-hip ratio and prostate cancer in MR analysis, but we did not include other obesity indicators such as BMI and waist circumference for comparison. Third, because the aggregated data is used in the analysis rather than the original data, subgroup analysis cannot be performed, such as studies on invasive or advanced prostate cancer. Fourth, the results of waist-to-hip ratio and prostate cancer are only based on bioinformatics analysis, and our findings need to be verified by in vitro and in vivo experiments in the future. Abbreviations PCa Prostate cancer GWAS Genome-wide association study SNP Single nucleotide polymorphism IVW Inverse variance weighted MR Mendelian randomization GO Gene Ontology OR Odds ratio Declarations Authors’ contributions Wang Yajing collected the data.Chen Shuai analyzed the data and wrote the manuscript. Chen Jingya approved the final version. Both authors contributed to created the tables and plots. Funding Open Access funding enabled and organized by Integrative Epidemiology Unit. The authors did not receive funding for this study. Funding information of the genome-wide association studies is specified in the cited studies. Availability of data and materials The present study is based on freely available summary statistics from genome-wide association studies. Data can be obtained from https://gwas.mrcieu.ac.uk/datasets Consent for publication Not applicable. Competing interests The authors declare no competing interests. Ethical approval and consent to participate All the data for this study were publicly available summary statistics. Therefore, ethical approval and consent to participate were not required. References Rawla P. Epidemiology of Prostate Cancer. World J Oncol. 2019 Apr;10(2):63-89. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018 Nov;68(6):394-424. Cancer of the Prostate - Cancer Stat Facts. [Internet]. 2024. https://seer.cancer.gov/statfacts/html/prost.html. Eeles RA, Olama AA, Benlloch S, et al. Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array. Nat Genet. 2013;45(4):385-391. Tan SH, Petrovics G, Srivastava S. Prostate Cancer Genomics: Recent Advances and the Prevailing Underrepresentation from Racial and Ethnic Minorities. Int J Mol Sci. 2018;19(4):1255. Ziglioli F, Patera A, Isgrò G, Campobasso D, Guarino G, Maestroni U. Impact of modifiable lifestyle risk factors for prostate cancer prevention: a review of the literature. Front Oncol. 2023;13:1203791. Clinton SK, Giovannucci EL, Hursting SD. The World Cancer Research Fund/American Institute for Cancer Research Third Expert Report on Diet, Nutrition, Physical Activity, and Cancer: Impact and Future Directions. J Nutr. 2020;150(4):663-671. World Cancer Research Fund/American Institute for Cancer Research. Diet, Nutrition, Physical Activity and Cancer: A Global Perspective (2018). Continuous Update Project. Available at: http://dietandcancerreport.org (Accessed 14 February 2023). Allott EH, Hursting SD. Obesity and cancer: mechanistic insights from transdisciplinary studies. Endocr Relat Cancer. 2015;22(6):R365-R386. Calle EE, Kaaks R. Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nat Rev Cancer. 2004;4(8):579-591. Boehm K, Sun M, Larcher A, et al. Waist circumference, waist-hip ratio, body mass index, and prostate cancer risk: results from the North-American case-control study Prostate Cancer & Environment Study. Urol Oncol. 2015;33(11):494.e1-494.e4947. Tang B, Han CT, Zhang GM, et al. Waist-hip Ratio (WHR), a Better Predictor for Prostate Cancer than Body Mass Index (BMI): Results from a Chinese Hospital-based Biopsy Cohort. Sci Rep. 2017;7:43551. Lavalette C, Trétarre B, Rebillard X, Lamy PJ, Cénée S, Menegaux F. Abdominal obesity and prostate cancer risk: epidemiological evidence from the EPICAP study. Oncotarget. 2018;9(77):34485-34494. Vallières E, Mésidor M, Roy-Gagnon MH, Richard H, Parent MÉ. General and abdominal obesity trajectories across adulthood, and risk of prostate cancer: results from the PROtEuS study, Montreal, Canada. Cancer Causes Control. 2021;32(6):653-665. Burgess S, Butterworth A, Thompson SG. MR analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65. Hazelwood E, Sanderson E, Tan VY, et al. Identifying molecular mediators of the relationship between body mass index and endometrial cancer risk: a MR analysis. BMC Med. 2022;20(1):125. Fussey JM, Beaumont RN, Wood AR, Vaidya B, Smith J, Tyrrell J. Does Obesity Cause Thyroid Cancer? A MR Study. J Clin Endocrinol Metab. 2020;105(7):e2398-e2407. Perez-Cornago A, Smith-Byrne K, Hazelwood E, et al. Genetic predisposition to metabolically unfavourable adiposity and prostate cancer risk: A MR analysis. Cancer Med. 2023;12(15):16482-16489. Ahmed M, Mulugeta A, Lee SH, Mäkinen VP, Boyle T, Hyppönen E. Adiposity and cancer: a MR analysis in the UK biobank. Int J Obes (Lond). 2021;45(12):2657-2665. Yuan C, Jian Z, Feng S, et al. Do Obesity-Related Traits Affect Prostate Cancer Risk through Serum Testosterone? A MR Study. Cancers (Basel). 2023;15(19):4884. Xin J, Jiang X, Ben S, et al. Association between circulating vitamin E and ten common cancers: evidence from large-scale MR analysis and a longitudinal cohort study. BMC Med. 2022;20(1):281. Yang Y, Chen B, Zheng C, et al. Association of glucose-lowering drug target and risk of gastrointestinal cancer: a MR study. Cell Biosci. 2024;14(1):36. Zhang W, Ma L, Zhou Q, Gu T, Zhang X, Xing H. Therapeutic Targets for Diabetic Kidney Disease: Proteome-Wide MR and Colocalization Analyses. Diabetes. 2024;73(4):618-627. Diet, nutrition, physical activity and prostate cancer (revised 2018). [Internet]. 2021. Available from:https://www.wcrf.org/wp-content/uploads/2021/02/prostate-cancer-report.pdf Vallières E, Mésidor M, Roy-Gagnon MH, Richard H, Parent MÉ. General and abdominal obesity trajectories across adulthood, and risk of prostate cancer: results from the PROtEuS study, Montreal, Canada. Cancer Causes Control. 2021;32(6):653-665. Greenberg AS, Obin MS. Obesity and the role of adipose tissue in inflammation and metabolism. Am J Clin Nutr. 2006; 83:461S–65S. Kelesidis I, Kelesidis T, Mantzoros CS. Adiponectin and cancer: a systematic review. Br J Cancer. 2006; 94:1221– 25. Rowlands MA, Gunnell D, Harris R, Vatten LJ, Holly JM, Martin RM. Circulating insulin-like growth factor peptides and prostate cancer risk: a systematic review and meta analysis. Int J Cancer. 2009; 124:2416–29. Hsing AW, Deng J, Sesterhenn IA, Mostofi FK, Stanczyk FZ, Benichou J, Xie T, Gao YT. Body size and prostate cancer: a population-based case-control study in China. Cancer Epidemiol Biomarkers Prev. 2000; 9:1335–41. Casals-Casas C, Desvergne B. Endocrine disruptors: from endocrine to metabolic disruption. Annu Rev Physiol. 2011; 73:135–62. Erickson BE. Bisphenol A under scrutiny. Chem Eng News. 2008; 86:36–39. Gao C, Patel CJ, Michailidou K, et al. MR study of adiposity-related traits and risk of breast, ovarian, prostate, lung and colorectal cancer. Int J Epidemiol. 2016;45(3):896-908. Ahn J, Wu H, Lee K. Integrative Analysis Revealing Human Adipose-Specific Genes and Consolidating Obesity Loci. Sci Rep. 2019;9(1):3087. Miller GJ, Miller HL, van Bokhoven A, et al. Aberrant HOXC expression accompanies the malignant phenotype in human prostate. Cancer Res. 2003;63(18):5879-5888. Luo Z, Farnham PJ. Genome-wide analysis of HOXC4 and HOXC6 regulated genes and binding sites in prostate cancer cells. PLoS One. 2020;15(2):e0228590. Javed S, Langley SE. Importance of HOX genes in normal prostate gland formation, prostate cancer development and its early detection. BJU Int. 2014;113(4):535-540. Javed S, Langley SE. Importance of HOX genes in normal prostate gland formation, prostate cancer development and its early detection. BJU Int. 2014;113(4):535-540. Li M, Bai G, Cen Y, et al. Silencing HOXC13 exerts anti-prostate cancer effects by inducing DNA damage and activating cGAS/STING/IRF3 pathway. J Transl Med. 2023;21(1):884. Zhang X, Cupples LA, Liu CT. A fine-mapping study of central obesity loci incorporating functional annotation and imputation. Eur J Hum Genet. 2018;26(9):1369-1377. Millena AC, Vo BT, Khan SA. JunD Is Required for Proliferation of Prostate Cancer Cells and Plays a Role in Transforming Growth Factor-β (TGF-β)-induced Inhibition of Cell Proliferation. J Biol Chem. 2016;291(34):17964-17976. Elliott B, Millena AC, Matyunina L, et al. Essential role of JunD in cell proliferation is mediated via MYC signaling in prostate cancer cells. Cancer Lett. 2019;448:155-167. Table Table 1 MR causal relationship between waist-to-hip ratio and prostate cancer in experimental group and the validation group. Method 2018 2022 Beta SE p or Beta SE p or Maximum likelihood 0.007 0.003 0.009 1.007 0.212 0.075 0.005 1.237 MR Egger 0.012 0.009 0.204 1.012 0.450 0.187 0.023 1.568 Weighted median 0.005 0.004 0.280 1.005 0.324 0.107 0.002 1.382 Penalised weighted median 0.003 0.004 0.453 1.003 0.334 0.116 0.004 1.396 Inverse variance weighted 0.007 0.004 0.060 1.007 0.205 0.077 0.008 1.227 IVW radial 0.007 0.004 0.060 1.007 0.205 0.077 0.008 1.227 Inverse variance weighted (multiplicative random effects) 0.007 0.004 0.060 1.007 0.205 0.077 0.008 1.227 Inverse variance weighted (fixed effects) 0.007 0.003 0.009 1.007 0.205 0.074 0.005 1.227 Additional Declarations No competing interests reported. Supplementary Files SupportingInformation.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5728950","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":396161668,"identity":"7a62937f-b8ed-41ff-b415-eb54b6875ac2","order_by":0,"name":"Chen Shuai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACNvbm4x8+GNjIyTPzf3yQUFFDWAsfz7E0xhkVacaG7QzGBg/OHCOsRU4ix4yZ58zhxIbzDGaSD1uYiXAY0JYHvG2HExubGdIqEhvYGPjbuxMI+sVAsi3duJ2Z4diNxB0yDBJnzm4gZEuChGGbtWxjM2PbjcQzbAwGErkEtEjkGEgktjEzNhxmZisAMojSYiZx4IyzYsNhNjYG4rTwHEs2bAAFcjMPs0TCmWM8BP0i39588PEfUFTyn2H8+KOiRo6/vRe/FgzAQ5ryUTAKRsEoGAVYAQDzb0n2F4RGsQAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"","lastName":"Shuai","suffix":""},{"id":396161669,"identity":"90876c86-4f66-45f0-9275-2f953a320d20","order_by":1,"name":"Chen Jingya","email":"","orcid":"","institution":"Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Jingya","suffix":""}],"badges":[],"createdAt":"2024-12-29 08:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5728950/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5728950/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72783480,"identity":"513c91b1-7b8e-4e1d-a4d2-b590493f2fb9","added_by":"auto","created_at":"2025-01-02 06:35:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23461,"visible":true,"origin":"","legend":"\u003cp\u003eThe fowchart of the study.\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-5728950/v1/ce984e7bd5cf450ff892b1e5.png"},{"id":72783481,"identity":"92bf2299-af9e-42f4-ac4a-e53690c21593","added_by":"auto","created_at":"2025-01-02 06:35:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147038,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots for the causal association between gut microbiota and PE. The horizontal axis represents the effect of SNPs on exposure, and the vertical axis represents the effect of SNPs on outcome. Slopes less than 0 suggest that exposure factors are favorable for outcomes.\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-5728950/v1/13278f622dd315a916e199b9.png"},{"id":72784489,"identity":"f464421f-490e-4031-b81d-8d09f473aede","added_by":"auto","created_at":"2025-01-02 06:43:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":77025,"visible":true,"origin":"","legend":"\u003cp\u003eThe horizontal axis represents the P value converted by -log\u003csup\u003e10\u003c/sup\u003e. DNA-binding transcription activator activity, RNA polymerase II-specific is a molecular pathway where two enrichment analyses overlap.\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-5728950/v1/fc6ec2c4de943fb32d5fdcd2.png"},{"id":72805413,"identity":"a373dac5-0794-4dac-a222-81c0df63f603","added_by":"auto","created_at":"2025-01-02 10:17:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":614536,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5728950/v1/af13c3e6-746d-4cb1-b7e9-0dab4c9fe1d4.pdf"},{"id":72783484,"identity":"f066a052-ed2c-465a-88ff-36529a3e6bfd","added_by":"auto","created_at":"2025-01-02 06:35:34","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":170583,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.zip","url":"https://assets-eu.researchsquare.com/files/rs-5728950/v1/97b6934a7f21c5db77718ad1.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic correlation between prostate cancer and central obesity: a Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer (PCa) has the highest incidence among men worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is also the cancer with the highest incidence and the third highest mortality in Europe [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. About 299,010 new cases and 35,250 deaths of PCa were reported in the United States in 2023[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Potential and especially controllable factors related to the development and progression of PCa are significant. Initial factors such as age, race, family history, and genetic factors have been found to be related with PCa.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In recent years, the correlation between PCa and controllable factors such as diet, physical activity, obesity and waist-to-hip ratio has been attracting increasing attention[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have found that obesity is closely related to the risk of PCa.The Continuous Update Project expert report has been presented by the World Cancer Research Fund (WCRF) and the American Institute for Cancer Research (AICR) in 2018, which aims to prevent cancer through diet, nutrition and physical activity[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Obesity affects a variety of hormone levels and metabolic pathways such as testosterone and insulin, which may promotes the growth of hormone-dependent cancer cells, and may cause a low-grade chronic inflammatory state and promotes the progression of aggressive tumors[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The indicators of obesity include waist circumference, BMI, and waist-to-hip ratio. Among them, waist-to-hip ratio, which can suggest concentric obesity, is more likely to predict the risk of PCa[\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, these studies are mostly observational studies or controlled trials, which cannot reflect the one-way causal relationship between waist-to-hip ratio and PCA, and the results could be affected by confounding factors.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) research the causal relationship between exposure factor and outcome through gene loci[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Previous studies have successfully applied MR methods to explore the causal relationship between obesity and cancer[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. MR methods has been used to explore the causal relationship between obesity and PCA[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], but studies on the causal relationship between waist-to-hip ratio and PCA are relatively rare[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and methods of those research did not involve the application of reverse MR method, external data set verification and enrichment analysis. To explore the causal relationship between waist-to-hip ratio and prostate cancer, it is essential to apply external validation to improve the reliability of the results, and enrichment analysis should be applied to reflect possible gene expression pathways[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on the waist-to-hip ratio and prostate cancer Genome-wide association study (GWAS) data as the experimental group, we used a variety of MR methods to explore the possible one-way causal relationship between waist-to-hip ratio and prostate cancer, and enrichment analysis was applied to find possible enrichment functions and potential genes that appeared to be involved in it. In addition, we used another set of waist-to-hip ratio GWAS data as an external validation group for Mendelian analysis, and tried to find possible overlapping enrichment pathways and genes.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThe whole research process is reflected in Figure 1. In order to explore the potential causal relationship between waist-to-hip ratio and prostate cancer, we used a variety of MR methods to study the causal relationship between waist-to-hip ratio data and prostate cancer data in the experimental group and the\u0026nbsp;validation group. The waist-to-hip ratio data and prostate cancer data of the experimental group and the validation group were independent GWAS data. The single nucleotide polymorphisms (SNPs) for MR studies must meet three necessary assumptions with waist-to-hip ratio and prostate cancer data. We performed genetic interpretation of these SNPs and explored the molecular functions that may be enriched in the gene. Search for possible molecular functions and action genes in the overlapping results of the experimental group and the validation group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData inclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GWAS data we used were from Integrative Epidemiology Unit (IEU) GWAS database (https://gwas.mrcieu.ac.uk/). All data were derived from published studies or published GWAS summary data. It provides ethical approval and informed consent, and this study does not require separate ethical approval. To explore the causal relationship between prostate cancer and waist-to-hip ratio, we used the waist-to-hip ratio data of the experimental group and the waist-to-hip ratio data of the validation group as the exposure data and the prostate cancer data as the outcome. The GWAS data of prostate cancer data contains 182,625 European individuals, ID name is ieu-b-4809, a total of 12,097,504 SNPs; the GWAS data ID of the waist-to-hip ratio data in the experimental group was named ieu-b-4809. There were 85,978 European individuals and 7,908,954 SNPs in the GWAS data. The ID of the waist-to-hip ratio data in the validation group was ebi-a-GCST90029009, containing 11,555 Hispanic or Latino individuals and a total of 30,077,714 SNPs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTool variable selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing MR analysis to explore the relationship between waist-to-hip ratio and prostate cancer requires screening instrumental variables to meet three necessary assumptions. Correlation test:We applied correlation analysis to calculate the correlation between SNPs and exposure in GWAS data. We defined SNPs with P\u0026lt;1*10\u003csup\u003e-6\u003c/sup\u003e as being associated with exposure. To avoid the possible bias of weak instrumental variables, SNPs with F\u0026gt;10 were defined as not weak instrumental variable bias and included. If too many exposure-related SNPs were generated, subsequent MR analysis would be affected. To reduce the number of SNPs included, we defined SNPs with P\u0026lt;1*\u003csup\u003e-10\u003c/sup\u003e and F\u0026lt;10 as strongly associated with exposure as a stricter criteria. Independence hypothesis: Due to the possible confounding factors affecting the causal analysis of waist-to-hip ratio and prostate cancer, we searched the Phenoscanner database (http://www.phenoscanner.medschl.cam.ac.uk/)) to exclude potential pleiotropic instrumental variables. Linkage disequilibrium test was performed on the above included SNPs, and SNPs with r\u003csup\u003e2\u003c/sup\u003e\u0026lt;0.001 and clustering distance(window size) kb\u0026gt;10000 were defined as independent and included. Exclusive hypothesis: We calculated the correlation between the above included SNPs and the outcome, and excluded SNPs with P\u0026lt;1*10\u003csup\u003e-6\u003c/sup\u003e to ensure that the included SNPs can only affect the outcome through exposure.\u003c/p\u003e\n\u003cp\u003eIn order to ensure the reliability of the results of Mendel 's randomization analysis, we preprocessed the SNPs selected in the above steps. For the extracted exposure and outcome GWAS data, we apply the harmonise function to preprocess them so that their effect alleles remain uniform. Cochran 's Q test in IVW was used for analysis, and the condition of P \u0026gt; 0.05 was defined as no heterogeneity in exposure and outcome data. The pleiotropy method was used to define that there was no horizontal pleiotropy for data with P value \u0026gt; 0.05. We used the Outlier-corrected method to eliminate SNPs with P values less than 1 in the model to correct the effect of outliers on MR analysis results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter the above-mentioned three necessary tests and data preprocessing, the common SNPs in the selected exposure and outcome data were included for the MR analysis. The waist-to-hip ratio of the experimental group was used as exposure, and prostate cancer was used as the outcome. We applied 8 MR methods to explore the causal relationship between them, including MR Egger, Weighted median, Penalised weighted median, Inverse variance weighted (IVW), IVW radial, Inverse variance weighted (multiplicative random effects), Inverse variance weighted (fixed effects). P \u0026lt; 0.05 indicated that the waist-to-hip ratio was associated with prostate cancer. If the results of the above heterogeneity test are P \u0026gt; 0.05, we chose IVW (multiplicative\u0026nbsp;random\u0026nbsp;effects) method as the main results, otherwise we chose IVW (fixed effects) method as the main results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to explore the effect of SNP on prostate cancer caused by waist-to-hip ratio at the genetic level, we tried to find the upstream and downstream adjacent genes of SNPs to perform genetic annotations of SNPs related to exposure and outcome.\u0026nbsp;We performed Gene Ontology (GO) molecular functional enrichment analysis of these genes using Metascape. Metascape is a specially designed web-based portal that provides comprehensive resources for annotating and analyzing gene lists, thereby enhancing the comprehending of the functions of the genes studied. Hypergeometric test is used for tissue enrichment analysis, aiming at gene interpretation and enrichment analysis of meaningful SNPs, and finding potential significantly enriched functions or specific features. In addition, we also look for genes enriched in function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExternal verification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used correlation analysis to calculate the correlation between SNPs and exposure in the waist-to-hip ratio data of the validation group with ID ebi-a-GCST008050. SNPs with P \u0026lt; 1*10\u003csup\u003e-6\u003c/sup\u003e, LD \u0026lt; 0.001 and F \u0026lt; 10 were defined as strongly correlated with exposure and included. If there are many strongly correlated SNPs, in order to reduce the included SNPs, only SNPs with P \u0026lt; 1*10\u003csup\u003e-10\u003c/sup\u003e, LD \u0026lt; 0.001 and F \u0026lt; 10 were defined as strongly correlated with exposure and included. We used the same 8 MR methods as the above experimental group study to analyse the causal relationship between the waist-to-hip ratio and prostate cancer in the validation group, including MR Egger, Weighted median, Penalised weighted median, Inverse variance weighted, IVW radial, Inverse variance weighted (multiplicative random effects), Inverse variance weighted (fixed effects). Gene annotation and GO enrichment analysis of meaningful SNPs were performed to find potentially significantly molecular function and enriched genes.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cstrong\u003eTool variable selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to screen out SNPs closely related to exposure and outcome, we defined SNPs with P \u0026lt; 1*10\u003csup\u003e-6\u003c/sup\u003e, LD \u0026lt; 0.001 and F \u0026lt; 10 as strongly associated with exposure.A total of 37 SNPs were screened to be strongly associated with exposure (Supplementary Table 1). After applying linkage disequilibrium test, we found all SNPs had good independence, therefore no SNP was eliminated. The P values of the SNPs included in the association analysis with the outcome were all greater than 1*10\u003csup\u003e-6\u003c/sup\u003e (Supplementary Table 2), suggesting that SNPs can only affect the outcome through exposure, and no SNP was eliminated. The result of Cochran \u0026apos;s Q test was P = 0.316, indicating that there was no heterogeneity between exposure and outcome. The P values of the results of the pleiotropy method and the MR-PRESSO method were 0.163 and 0.336, respectively, indicating that there was no horizontal pleiotropy in the sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the 8 methods MR methods, including MR Egger, Weighted median, Penalised weighted median, Inverse variance weighted, IVW radial, Inverse variance weighted (multiplicative random effects), Inverse variance weighted (fixed effects), showed causal relationship between waist-to-hip ratio and prostate cancer (Table 1). Among them, the Maximum likelihood and Inverse variance weighted (fixed effects) methods significantly suggested a positive causal relationship between waist-to-hip ratio and prostate cancer (P = 0.005, bete = 0.212, or = 1.237 ; p = 0.005, bete = 0.205, or = 1.227) (Fig. 2a). In the reverse MR analysis, the P values of these MR methods were all greater than 0.05, manifesting that there was a one-way causal relationship between waist-to-hip ratio and prostate cancer (Supplementary Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNP related genes and enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed genetic explanations for SNPs which were significantly associated with exposure and outcome in the experimental group, and Annotated gene were showed in Supplementary Table 4. The result of GO enrichment analysis showed that the causal relationship between waist-to-hip ratio and prostate cancer was mainly enriched in four molecular functions which are nuclear receptor coactivator activity, heparin binding, DNA-binding transcription activator activity and RNA polymerase II-specific and phospholipid binding (Fig.3a). Multiple genes were enriched in these molecular functions, as shown in Supplementary Table 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExternal verification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen we first defined SNPs with P\u0026lt;1*10\u003csup\u003e-10\u003c/sup\u003e, LD\u0026lt;0.001 and F\u0026lt;10 as strongly correlated with exposure, a total of 416 SNPS were screened out, which was too many. Considering the large number of SNPs in the validation group data, we defined SNPs with P \u0026lt; 1 * 10\u003csup\u003e-10\u003c/sup\u003e, LD \u0026lt; 0.001, and F \u0026lt; 10 as strongly associated with exposure, and a total of 201 SNPs were strongly associated with exposure (Supplementary Table 6). After applying linkage disequilibrium test, we found that all SNPs had good independence, so no SNP was eliminated. Among these SNPs, 195 SNPs were included in the oucome. The P value of the correlation analysis between rs10107982 and the outcome was greater than 1 * 10\u003csup\u003e-10\u003c/sup\u003e, which did not satisfy the exclusive hypothesis, and led to the exclusion of this SNP. The remaining 194 SNPs were included to ensure that SNPs could only affect the outcome through exposure (Supplementary Table 7). The P value of the results of the pleiotropy method was 0.931, suggesting that there was no horizontal pleiotropy in the sample.\u003c/p\u003e\n\u003cp\u003eThe causal relationship between waist-to-hip ratio and prostate cancer in validation group was analyzed by using the above 8 MR methods(Table 1). The results of Maximum likelihood and Inverse variance weighted (fixed effects) methods suggest a causal relationship between the two. (P = 0.002, beta = 0.008, or = 1.008) ; p = 0.002, beta = 0.008, or = 1.008) (Fig.2b).\u003c/p\u003e\n\u003cp\u003eWe performed genetic explanations for SNPs that were significantly associated with exposure and outcome in the validation group (Supplementary Table 4). The GO enrichment analysis of the annotated genes showed that the molecular function of the main enriched pathway, called DNA-binding transcription activator activity, RNA polymerase II-specific, overlapped with the main analysis (Figure 3b). There were 31 genes enriched in this molecular function (Supplementary Table 8), of which 6 genes overlapped with the genes enriched in the experimental group data results, namely HOXC4, HOXC10, HOXC11, HOXC13, CEBPG and JUND (Supplementary Table 8).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on the large-scale GWAS data of European population, we used the bi-directional\u0026nbsp;two-sample MR to explore the cause-and-effect relationship between waist-to-hip ratio and PCa. The results suggested that there was a significant positive causal relationship between waist-to-hip ratio and prostate cancer, and no potential reverse causality was observed. We performed genetic interpretation by looking for genes adjacent to SNPs which are strongly associated with waist-to-hip ratio and prostate cancer. GO enrichment analysis of these genes found that a series of GO biologic processes play vital roles in potential causality between waist-to-hip ratio and prostate cancer, including nuclear receptor coactivator activity, heparin binding, DNA-binding transcription activator activity, RNA polymerase II-specific and phospholipid binding. To enhance the reliability of the results, we implemented external verification. We used large-scale GWAS data of Hispanic or Latino populations as exposure, and repeated the above methods to explore its causal relationship with prostate cancer. The results showed that there was still a positive causal relationship between waist-to-hip ratio and prostate cancer. Many pivotal GO biologic processes were found after GO enrichment analysis, and the DNA-binding transcription activator activity,RNA polymerase Il-specific pathway overlaps with the results of the experimental group enrichment analysis, suggesting that it is a potential functional way for the gene action of prostate cancer caused by waist-hip ratio.\u003c/p\u003e\n\u003cp\u003eMany studies have shown that obesity is a risk factor for prostate cancer. Existing data from the World Cancer Research Fund have reported that body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) may be important factors for advanced PCa [24]. Eric et al.found that abdominal obesity increased the risk of invasive prostate cancer based on the application of abdominal circumference in Canadian population[25]. Based on the application of waist-to-hip ratio in the southern French population, Lavalette et al.found that abdominal obesity was associated with the incidence of prostate cancer, especially invasive prostate cancer[13]. In the study of the relationship between obesity and prostate cancer in the Chinese population, Bo Tang et al. found that waist-to-hip ratio was more associated with prostate cancer than BMI[12]. In summary, our results are broadly in accord with the above observational studies, that is, abdominal obesity as indicated by waist-to-hip ratio may be closely related to an increased risk of prostate cancer.\u003c/p\u003e\n\u003cp\u003eAbdominal obesity may affect the occurrence of prostate cancer through inflammation, insulin levels or endocrine disruptors. Inflammation plays a crucial part in the malignant transformation of prostate cells. From a biochemical perspective, the microenvironment that promotes inflammation makes the inflammatory state associated with visceral obesity. Abdominal obesity is associated with chronic inflammation based on the accumulation of immune cells (such as macrophages and white blood cells) between fat cells. These immune cells produce some pro-inflammatory cytokines and interleukins, such as IL-6, IL-8, TNF\u0026alpha;and CRP (C-reactive protein) [26]. This mechanism causes macrophages to surround adipocytes, called \u0026ldquo;coronary adipocytes\u0026rdquo;, and affect the microenvironment of surrounding adipocytes in an autocrine and paracrine manner, thereby inhibiting the expression of adiponectin genes. Adiponectin is one of the most important activators of AMPK and PPAR\u0026alpha;, which can stimulate fatty acid oxidation, reduce inflammation and regulate cancer survival [27]. Studies have shown that the progression of prostate cancer is closely related to plasma growth factors[28]. Insulin is one of the most important and studied growth factors associated with prostate cancer and obesity. Men with high serum insulin levels and abdominal obesity have a higher risk of prostate cancer[29]. Biological and epidemiological studies have found that endocrine disruptor exposure is associated with obesity, MS, type 2 diabetes and cancer[30]. For example, bisphenol A, used as an additive in polycarbonate plastics (wine bottles, food containers, cans of paint, etc.), is a common endocrine disruptor associated with several cancers[31]. Experimental animals exposed to low doses of bisphenol a have diabetes, reproductive problems (precocious puberty, sperm reduction shame), obesity, breast cancer and prostate cancer, while there are few human studies related to bisphenol a exposure and cancer risk, and more studies are needed.\u003c/p\u003e\n\u003cp\u003eMR design has been used in many studies to find causal relationship between obesity and cancer. We used the MR study to explore the genetic evidence of the causal relationship between waist-to-hip ratio and prostate cancer. Previous studies have found that obesity indicators such as waist circumference, BMI and waist-hip ratio have a causal relationship with breast cancer, ovarian cancer, prostate cancer, lung cancer, colorectal cancer, endometrial cancer and thyroid cancer[32]. Chen, Young et al. reported that there is no causal relationship between waist-to-hip ratio and prostate cancer, which was differ from our results[20]. We discovered that there was a significant positive causal relationship between waist-to-hip ratio and prostate cancer. Different exposure sources and selection method of SNP may be the reasons for this discrepancy. Chi Yuan et al. obtained the waist circumference and hip circumference data of the target population from the UK database (UKB), and then used the waist circumference divided by the hip circumference to obtain the waist-hip ratio as the exposure data in the research, which may cause bias in the results. We used the independent waist-to-hip ratio data named ieu-b-4809 in the Integrative Epidemiology Unit (IEU) database as the exposure data. In addition, to ensure the robustness of the research results, experimental group exposure and outcome data were European populations, while the validation group exposure data was Spanish and Latino populations.\u003c/p\u003e\n\u003cp\u003eAfter the MR analysis of the experimental and the validation group data, we performed gene interpretation on the SNPs generated by the two results and further performed enrichment analysis. We found that there may be a large number of GO biological processes that work in the potential causality from waist-to-hip ratio to prostate cancer. There was one Molecular Function overlap in the results of the two enrichment analyses, DNA-binding transcription activator activity, RNA polymerase II-specific, suggesting that it may be a potential way for waist-to-hip ratio to increase the risk of prostate cancer. This is a DNA-binding transcription factor activity that activates or increases transcription of specific gene sets transcribed by RNA polymerase II. Enrichment analysis found that many genes were enriched in this molecular function, among which six genes overlapped, namely HOXC4, HOXC10, HOXC11, HOXC13, CEBPG and JUND.\u003c/p\u003e\n\u003cp\u003ePrevious studies have found that HOX family genes are closely related to obesity, and the HOXC gene cluster is up-regulated in subcutaneous adipose tissue[33]. Many studies have shown that HOX family genes play roles in the growth of prostate. Miller et al.confirmed the overexpression of HOXC4 in prostate malignant cell lines and lymph node metastases by using specific reverse transcription PCR for HOXC4[34]. Zhifei Luo et al.found that HOXC4 is a key gene that regulates the proliferation of prostate cancer. HOXC4 and HOXB13 are co-localized with AR, and these two transcription factors have previously been shown to contribute to the development of prostate cancer[35].Previous studies have found that the HOXC11 gene is essential for the embryonic development of the prostate[36]. Yuefu Han et al.found that hsa_circ_0075542 inhibited the malignant characteristics of LNCa P and PC3 cells and promoted apoptosis by acting as a competitive endogenous RNA of miR-1197. The hsa_circ_0075542/miR-1197 axis may function through HOXC11[37]. In the study of endocrine quality resistance of breast cancer, Azlena Ali et al.found that PSAP, the target gene of HOXC11, is an AR activator, and PSAP activates AR in prostate cancer cells, which will be proved in prostate cancer samples in future. Maozhang Li et al.found that the HOX family exists independently in 25 TFs, among which HOXC10 and HOXC13 have unique clinical features, and PCa patients with high expression of HOXC13 have a poor prognosis. HOXC13 is highly expressed in tumor tissues and is associated with Gleason score and pathological grade. In vitro experiments showed that silencing HOXC13 inhibited the function of 22RV1 and DU145 cells by inducing cell DNA damage and activating the cGAS / STING / IRF3 pathway, thereby regulating the progression of prostate cancer[38].\u003c/p\u003e\n\u003cp\u003eXiaoyu Zhang et al. used the results of PAINTOR meta-analysis to conduct a Bayesian statistical fine mapping study on WHRadj BMI, trying to further understand the mechanism of central obesity, and found that SNPrs12608504 (JUND) is particularly important[39]. Jun D is a member of the AP-1 family and is essential for the proliferation of prostate cancer (PCa) cells[40]. Bethtrice Elliott et al. found that JunD is a key regulator of cell cycle progression, and inhibition of its target genes may be an effective way to block the occurrence of prostate cancer[41]. At present, no previous literature has found that CEBPG is related with prostate cancer or obesity. It is expected to be found in future research.\u003c/p\u003e\n\u003cp\u003eThe advantages of this study are as follows: First, in order to minimize possible false positive results, two independent aggregated data were used for the research, and the same MR and enrichment analysis methods were performed using external validation data sets to enhance the robustness of the results. Secondly, eight methods of MR analysis were performed on the three groups of GWAS summary data, and the population of the external validation data set was different from the main research population, which increased the credibility of the results. Thirdly, GO enrichment analysis was used to find potential enriched genes and explore the possible molecular function of waist-to-hip ratio leading to prostate cancer.\u003c/p\u003e\n\u003cp\u003eThe limitations of this study are as follows: First, most of the GWAS summary data come from European and Hispanic or Latino populations, and have not yet involved other populations such as Asian populations. Our findings should be applied cautiously to populations in other regions. Secondly, we explored the causal relationship between waist-to-hip ratio and prostate cancer in MR analysis, but we did not include other obesity indicators such as BMI and waist circumference for comparison. Third, because the aggregated data is used in the analysis rather than the original data, subgroup analysis cannot be performed, such as studies on invasive or advanced prostate cancer. Fourth, the results of waist-to-hip ratio and prostate cancer are only based on bioinformatics analysis, and our findings need to be verified by in vitro and in vivo experiments in the future.\u003c/p\u003e"},{"header":"Abbreviations ","content":"\u003cp\u003ePCa\u0026nbsp;Prostate cancer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGWAS Genome-wide association study\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSNP Single nucleotide polymorphism\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIVW Inverse variance weighted\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMR Mendelian randomization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO\u0026nbsp;Gene Ontology\u003c/p\u003e\n\u003cp\u003eOR Odds ratio\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e Wang Yajing collected the data.Chen Shuai analyzed the data and wrote the manuscript. Chen Jingya approved the final version. Both authors contributed to created the tables and plots.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eOpen Access funding enabled and organized by Integrative Epidemiology Unit. The authors did not receive funding for this study. Funding information of the genome-wide association studies is specified in the cited studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e The present study is based on freely available summary statistics from genome-wide association studies. Data can be obtained from https://gwas.mrcieu.ac.uk/datasets\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e All the data for this study were publicly available summary statistics. Therefore, ethical approval and consent to participate were not required.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRawla P. Epidemiology of Prostate Cancer. World J Oncol. 2019 Apr;10(2):63-89.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018 Nov;68(6):394-424.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCancer of the Prostate - Cancer Stat Facts. [Internet]. 2024. https://seer.cancer.gov/statfacts/html/prost.html.\u003c/li\u003e\n \u003cli\u003eEeles RA, Olama AA, Benlloch S, et al. Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array.\u0026nbsp;Nat Genet. 2013;45(4):385-391.\u003c/li\u003e\n \u003cli\u003eTan SH, Petrovics G, Srivastava S. Prostate Cancer Genomics: Recent Advances and the Prevailing Underrepresentation from Racial and Ethnic Minorities. Int J Mol Sci. 2018;19(4):1255.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZiglioli F, Patera A, Isgr\u0026ograve; G, Campobasso D, Guarino G, Maestroni U. Impact of modifiable lifestyle risk factors for prostate cancer prevention: a review of the literature. Front Oncol. 2023;13:1203791.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eClinton SK, Giovannucci EL, Hursting SD. The World Cancer Research Fund/American Institute for Cancer Research Third Expert Report on Diet, Nutrition, Physical Activity, and Cancer: Impact and Future Directions.\u0026nbsp;J Nutr. 2020;150(4):663-671.\u003c/li\u003e\n \u003cli\u003eWorld Cancer Research Fund/American Institute for Cancer Research. Diet, Nutrition, Physical Activity and Cancer: A Global Perspective (2018). Continuous Update Project. Available at: http://dietandcancerreport.org (Accessed 14 February 2023).\u003c/li\u003e\n \u003cli\u003eAllott EH, Hursting SD. Obesity and cancer: mechanistic insights from transdisciplinary studies. Endocr Relat Cancer. 2015;22(6):R365-R386.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCalle EE, Kaaks R. Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nat Rev Cancer. 2004;4(8):579-591.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBoehm K, Sun M, Larcher A, et al. Waist circumference, waist-hip ratio, body mass index, and prostate cancer risk: results from the North-American case-control study Prostate Cancer \u0026amp; Environment Study. Urol Oncol. 2015;33(11):494.e1-494.e4947.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTang B, Han CT, Zhang GM, et al. Waist-hip Ratio (WHR), a Better Predictor for Prostate Cancer than Body Mass Index (BMI): Results from a Chinese Hospital-based Biopsy Cohort. Sci Rep. 2017;7:43551.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLavalette C, Tr\u0026eacute;tarre B, Rebillard X, Lamy PJ, C\u0026eacute;n\u0026eacute;e S, Menegaux F. Abdominal obesity and prostate cancer risk: epidemiological evidence from the EPICAP study. Oncotarget. 2018;9(77):34485-34494.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eValli\u0026egrave;res E, M\u0026eacute;sidor M, Roy-Gagnon MH, Richard H, Parent M\u0026Eacute;. General and abdominal obesity trajectories across adulthood, and risk of prostate cancer: results from the PROtEuS study, Montreal, Canada.\u0026nbsp;Cancer Causes Control. 2021;32(6):653-665.\u003c/li\u003e\n \u003cli\u003eBurgess S, Butterworth A, Thompson SG. MR analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658\u0026ndash;65.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHazelwood E, Sanderson E, Tan VY, et al. Identifying molecular mediators of the relationship between body mass index and endometrial cancer risk: a MR analysis. BMC Med. 2022;20(1):125.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFussey JM, Beaumont RN, Wood AR, Vaidya B, Smith J, Tyrrell J. Does Obesity Cause Thyroid Cancer? A MR Study. J Clin Endocrinol Metab. 2020;105(7):e2398-e2407.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePerez-Cornago A, Smith-Byrne K, Hazelwood E, et al. Genetic predisposition to metabolically unfavourable adiposity and prostate cancer risk: A MR analysis.\u0026nbsp;Cancer Med. 2023;12(15):16482-16489.\u003c/li\u003e\n \u003cli\u003eAhmed M, Mulugeta A, Lee SH, M\u0026auml;kinen VP, Boyle T, Hypp\u0026ouml;nen E. Adiposity and cancer: a MR analysis in the UK biobank. Int J Obes (Lond). 2021;45(12):2657-2665.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYuan C, Jian Z, Feng S, et al. Do Obesity-Related Traits Affect Prostate Cancer Risk through Serum Testosterone? A MR Study. Cancers (Basel). 2023;15(19):4884.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eXin J, Jiang X, Ben S, et al. Association between circulating vitamin E and ten common cancers: evidence from large-scale MR analysis and a longitudinal cohort study. BMC Med. 2022;20(1):281.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYang Y, Chen B, Zheng C, et al. Association of glucose-lowering drug target and risk of gastrointestinal cancer: a MR study. Cell Biosci. 2024;14(1):36.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang W, Ma L, Zhou Q, Gu T, Zhang X, Xing H. Therapeutic Targets for Diabetic Kidney Disease: Proteome-Wide MR and Colocalization Analyses. Diabetes. 2024;73(4):618-627.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDiet, nutrition, physical activity and prostate cancer (revised 2018). [Internet]. 2021. Available from:https://www.wcrf.org/wp-content/uploads/2021/02/prostate-cancer-report.pdf\u003c/li\u003e\n \u003cli\u003eValli\u0026egrave;res E, M\u0026eacute;sidor M, Roy-Gagnon MH, Richard H, Parent M\u0026Eacute;. General and abdominal obesity trajectories across adulthood, and risk of prostate cancer: results from the PROtEuS study, Montreal, Canada. Cancer Causes Control. 2021;32(6):653-665.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGreenberg AS, Obin MS. Obesity and the role of adipose tissue in inflammation and metabolism. Am J Clin Nutr. 2006; 83:461S\u0026ndash;65S.\u003c/li\u003e\n \u003cli\u003eKelesidis I, Kelesidis T, Mantzoros CS. Adiponectin and cancer: a systematic review. Br J Cancer. 2006; 94:1221\u0026ndash; 25.\u003c/li\u003e\n \u003cli\u003eRowlands MA, Gunnell D, Harris R, Vatten LJ, Holly JM, Martin RM. Circulating insulin-like growth factor peptides and prostate cancer risk: a systematic review and meta analysis. Int J Cancer. 2009; 124:2416\u0026ndash;29.\u003c/li\u003e\n \u003cli\u003eHsing AW, Deng J, Sesterhenn IA, Mostofi FK, Stanczyk FZ, Benichou J, Xie T, Gao YT. Body size and prostate cancer: a population-based case-control study in China. Cancer Epidemiol Biomarkers Prev. 2000; 9:1335\u0026ndash;41.\u003c/li\u003e\n \u003cli\u003eCasals-Casas C, Desvergne B. Endocrine disruptors: from endocrine to metabolic disruption. Annu Rev Physiol. 2011; 73:135\u0026ndash;62.\u003c/li\u003e\n \u003cli\u003eErickson BE. Bisphenol A under scrutiny. Chem Eng News. 2008; 86:36\u0026ndash;39.\u003c/li\u003e\n \u003cli\u003eGao C, Patel CJ, Michailidou K, et al. MR study of adiposity-related traits and risk of breast, ovarian, prostate, lung and colorectal cancer. Int J Epidemiol. 2016;45(3):896-908.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAhn J, Wu H, Lee K. Integrative Analysis Revealing Human Adipose-Specific Genes and Consolidating Obesity Loci.\u0026nbsp;Sci Rep. 2019;9(1):3087.\u003c/li\u003e\n \u003cli\u003eMiller GJ, Miller HL, van Bokhoven A, et al. Aberrant HOXC expression accompanies the malignant phenotype in human prostate. Cancer Res. 2003;63(18):5879-5888.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLuo Z, Farnham PJ. Genome-wide analysis of HOXC4 and HOXC6 regulated genes and binding sites in prostate cancer cells.\u0026nbsp;PLoS One. 2020;15(2):e0228590.\u003c/li\u003e\n \u003cli\u003eJaved S, Langley SE. Importance of HOX genes in normal prostate gland formation, prostate cancer development and its early detection. BJU Int. 2014;113(4):535-540.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJaved S, Langley SE. Importance of HOX genes in normal prostate gland formation, prostate cancer development and its early detection. BJU Int. 2014;113(4):535-540.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLi M, Bai G, Cen Y, et al. Silencing HOXC13 exerts anti-prostate cancer effects by inducing DNA damage and activating cGAS/STING/IRF3 pathway. J Transl Med. 2023;21(1):884.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang X, Cupples LA, Liu CT. A fine-mapping study of central obesity loci incorporating functional annotation and imputation. Eur J Hum Genet. 2018;26(9):1369-1377.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMillena AC, Vo BT, Khan SA. JunD Is Required for Proliferation of Prostate Cancer Cells and Plays a Role in Transforming Growth Factor-\u0026beta; (TGF-\u0026beta;)-induced Inhibition of Cell Proliferation.\u0026nbsp;J Biol Chem. 2016;291(34):17964-17976.\u003c/li\u003e\n \u003cli\u003eElliott B, Millena AC, Matyunina L, et al. Essential role of JunD in cell proliferation is mediated via MYC signaling in prostate cancer cells. Cancer Lett. 2019;448:155-167. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 MR causal relationship between waist-to-hip ratio and prostate cancer in experimental group and the validation group.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 181px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 181px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003eor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eMaximum likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.212\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.075\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.237\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.012\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.204\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.012\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.450\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.187\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.023\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.568\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.280\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.324\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.107\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.382\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003ePenalised weighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.453\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.334\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.116\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.396\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.060\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.205\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.077\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.227\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eIVW radial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.060\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.205\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.077\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.227\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eInverse variance weighted (multiplicative random effects)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.060\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.205\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.077\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.227\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eInverse variance weighted (fixed effects)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.009\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.205\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.074\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1.227\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"MR, waist-to-hip ratio, prostate cancer, central obesity","lastPublishedDoi":"10.21203/rs.3.rs-5728950/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5728950/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOur aim was to apply Mendelian randomization to find possible causal relationships between prostate cancer and central obesity. Based on waist-to-hip ratio GWAS and prostate cancer GWAS data as the experimental group, we used 8 MR methods to explore the possible causal relationship between waist-to-hip ratio and prostate cancer. Situation with P \u0026lt; 0.05 was suggested causal relationship. Gene annotation and Gene Ontology (GO) molecular functional enrichment analysis of meaningful SNPs were performed to find potentially significantly molecular function and enriched genes. Finally, we searched for the overlapping molecular functions of the experimental group and the validation group and the overlapping genes enriched in them. All the 8 methods MR methods showed causal relationship between waist-to-hip ratio and prostate cancer in experimental group. The GO enrichment analysis showed that the molecular function of the main enriched pathway, called DNA-binding transcription activator activity, RNA polymerase II-specific, overlapped in the experimental group and the validation group. Our results manifest that waist-to-hip ratio has a potential causal relationship with prostate cancer.\u003c/p\u003e","manuscriptTitle":"Genetic correlation between prostate cancer and central obesity: a Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-02 06:35:29","doi":"10.21203/rs.3.rs-5728950/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ca21176-46a1-4129-b5f2-8a23b541ddf4","owner":[],"postedDate":"January 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-02T10:09:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-02 06:35:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5728950","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5728950","identity":"rs-5728950","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}