遗传决定的代谢物对常见泌尿系统肿瘤易感性的因果关系：一项两样本孟德尔随机化研究与Meta分析

泌尿系统肿瘤，包括肾癌、前列腺癌、膀胱癌和睾丸癌，在全球癌症发病率和死亡率中占据重要地位。代谢组学以研究小分子中间产物为重点，已成为理解癌症发病机制的工具。鉴于该领域存在知识空白，本研究采用两样本孟德尔随机化（MR）分析，探讨遗传决定的代谢物（GDMs）与这四种常见泌尿系统肿瘤易感性的因果关系。利用来自欧洲人群的全基因组关联研究（GWAS）数据，覆盖血液代谢物和四种常见泌尿肿瘤的最大病例数。分别进行初步和二次MR分析，主要采用逆方差加权（IVW）法。通过MR-Steiger检验、Cochran Q检验、逐一剔除分析、MR-Egger截距检验和MR-PRESSO分析确保结果稳健。此外，还进行Meta分析整合结果。采用加权中位数（WM）法进行较宽松的校正（PWM < 0.05）。经过严格的遗传变异筛选，共有1400个代谢物中有645个在初步和二次分析中被纳入。初步MR分析发现94个不同的代谢物与四种泌尿系统肿瘤存在96个潜在的因果关系；基于芬兰人群数据的二次分析揭示93个潜在关系。跨数据库Meta分析识别出68个血液代谢物与四种泌尿肿瘤相关，且经过WM校正后，有31个代谢物仍具有显著性，另有37个具有提示性因果关系。反向MR分析显示，遗传预测的前列腺癌与升高的4-羟基氯噻酮水平存在显著的因果关系（IVW，合并比值比OR：1.039，95%置信区间CI：1.014-1.064，p = 0.002；WM，合并OR：1.052，95%CI：1.010-1.095，p = 0.014）。本研究深入揭示血液代谢物与泌尿系统肿瘤的因果关系，潜在的生物标志物和治疗靶点，为该领域的基础研究和临床干预提供了新的思路。

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.