遗传确定的代谢产物的因果关系对泌尿外科癌的敏感性：两样本的孟德尔随机研究和荟萃分析

包括肾脏，前列腺，膀胱和睾丸癌在内的普遍泌尿外科癌症对全球癌症的发病率和死亡率做出了重大贡献。专注于小分子中间体的代谢组学已成为了解癌症病因的一种工具。鉴于该领域的知识差距，我们采用了两样本的孟德尔随机化（MR）分析来研究遗传确定的代谢产物（GDM）与四种常见泌尿外科癌症的易感性之间的因果关系。该研究采用了欧洲人口的基因组研究（GWAS）数据，这些数据具有最广泛的案例，其中包括最广泛的案例，这些案例可用于均可用于血液学和四个预期的尿液学和四个预期的素养脉。分别进行了初步和次级MR分析，采用反向差异加权（IVW）作为主要方法。执行了多项统计分析，包括MR-Steiger检验，Cochran的Q检验，外出分析，MR-Egger截距分析和MR-Presso分析，以确保鲁棒性。此外，进行了荟萃分析以巩固发现。加权中位数（WM）方法用于相对宽松的校正（PWM <0.05）。严格的遗传变异过滤后，初步和次要MR分析中都包括1,400个代谢物中的645个。初步MR分析确定了94个不同代谢产物和四种泌尿外科癌症之间的96个潜在因果关系。基于芬兰结果数据的二级分析显示93个潜在的因果关系。跨数据库荟萃分析确定了与四种泌尿外科癌症相关的68种血液代谢产物。值得注意的是，使用WM进行校正后，有31个代谢产物仍然显着，并具有37个暗示性的因果关系。反向MR分析表明，遗传预测的前列腺癌与4-羟基氯噻氨醇的水平升高（IVW，组合OR：1.039，95％CI 1.014-1.064，p = 0.002; WM，合并，合并或合并：1.052，95％CI 1.010-1.095，P = 0.014），MR进入血液代谢产物与泌尿外科癌的因果关系，揭示了潜在的生物标志物和治疗靶标，从而解决了理解和奠定泌尿外科研究和治疗中有针对性干预措施的基础的差距。

Prevalent urological cancers, including kidney, prostate, bladder, and testicular cancers, contribute significantly to global cancer incidence and mortality. Metabolomics, focusing on small-molecule intermediates, has emerged as a tool to understand cancer etiology. Given the knowledge gap in this field, we employ a two-sample Mendelian randomization (MR) analysis to investigate the causal relationships between genetically determined metabolites (GDMs) and the susceptibility to four common urological cancers.The study employs genome-wide association studies (GWAS) data from European populations, featuring the most extensive case count available for both blood metabolites and four prevalent urological cancers. Preliminary and secondary MR analyses were separately conducted, employing inverse variance weighted (IVW) as the primary method. Multiple statistical analyses, including the MR-Steiger test, Cochran's Q test, leave-one-out analysis, MR-Egger intercept analysis, and MR-PRESSO analysis, were executed to ensure robustness. Additionally, a meta-analysis was carried out to consolidate findings. The weighted median (WM) method was utilized for a relatively lenient correction (PWM < 0.05).After rigorous genetic variation filtering, 645 out of 1,400 metabolites were included in both preliminary and secondary MR analyses. Preliminary MR analysis identified 96 potential causal associations between 94 distinct metabolites and four urological cancers. Secondary analysis based on Finnish outcome data revealed 93 potential causal associations. Cross-database meta-analysis identified 68 blood metabolites associated with four urological cancers. Notably, 31 metabolites remained significant after using WM for correction, with additional 37 suggestive causal relationships. Reverse MR analysis revealed a significant causal association between genetically predicted prostate cancer and elevated 4-hydroxychlorothalonil levels (IVW, combined OR: 1.039, 95% CI 1.014-1.064, p = 0.002; WM, combined OR: 1.052, 95% CI 1.010-1.095, p = 0.014).This comprehensive MR study provides insights into the causal relationships between blood metabolites and urological cancers, revealing potential biomarkers and therapeutic targets, thereby addressing gaps in understanding and laying the foundation for targeted interventions in urological cancer research and treatment.