流行的泌尿系统癌症，包括肾癌、前列腺癌、膀胱癌和睾丸癌，对全球癌症发病率和死亡率有重大影响。专注于小分子中间体的代谢组学已成为了解癌症病因学的工具。鉴于该领域的知识差距，我们采用两个样本孟德尔随机化 (MR) 分析来研究遗传决定的代谢物 (GDM) 与四种常见泌尿系统癌症的易感性之间的因果关系。该研究采用全基因组关联研究（ GWAS）来自欧洲人群的数据，具有血液代谢物和四种常见泌尿系统癌症的最广泛的病例数。初步和二次 MR 分析分别进行，采用逆方差加权 (IVW) 作为主要方法。执行了多项统计分析，包括 MR-Steiger 检验、Cochran's Q 检验、留一分析、MR-Egger 截距分析和 MR-PRESSO 分析，以确保稳健性。此外，还进行了荟萃分析以巩固研究结果。采用加权中值 (WM) 方法进行相对宽松的校正 (PWM < 0.05)。经过严格的遗传变异过滤后，1,400 种代谢物中的 645 种被纳入初步和二次 MR 分析。初步 MR 分析确定了 94 种不同代谢物与四种泌尿系统癌症之间的 96 种潜在因果关系。基于芬兰结果数据的二次分析揭示了 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系统癌症相关的 68 种血液代谢物。值得注意的是，使用 WM 进行校正后，31 种代谢物仍然显着，另外 37 种代谢物具有暗示性因果关系。反向 MR 分析揭示了遗传预测的前列腺癌与 4-羟基百菌清水平升高之间存在显着因果关系（IVW，组合 OR：1.039，95% CI 1.014-1.064，p = 0.002；WM，组合 OR：1.052，95% CI 1.010- 1.095，p = 0.014）。这项全面的 MR 研究深入了解了血液代谢物与泌尿系统癌症之间的因果关系，揭示了潜在的生物标志物和治疗靶点，从而弥补了理解上的差距，并为泌尿系统癌症研究和治疗的针对性干预奠定了基础。版权所有 © 2024 戴、王、钟、李、侯。

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.Copyright © 2024 Dai, Wang, Zhong, Li and Hou.