■常见的泌尿系癌症,包括肾脏,前列腺,膀胱,和睾丸癌,对全球癌症发病率和死亡率有重要贡献。代谢组学,专注于小分子中间体,已经成为了解癌症病因的工具。鉴于这一领域的知识差距,我们采用双样本孟德尔随机化(MR)分析来研究基因确定代谢物(GDMs)与4种常见泌尿系癌症易感性之间的因果关系.
■该研究采用了来自欧洲人群的全基因组关联研究(GWAS)数据,具有血液代谢物和四种常见泌尿系癌症的最广泛病例数。分别进行了初步和二次MR分析,采用逆方差加权(IVW)作为主要方法。多重统计分析,包括MR-Steiger测试,Cochran的Q测试,遗漏分析,MR-Egger截距分析,和MR-PRESSO分析,被执行以确保健壮性。此外,进行了荟萃分析以巩固研究结果.加权中位数(WM)方法用于相对宽松的校正(PWM<0.05)。
■经过严格的遗传变异过滤,1,400种代谢物中的645种被包括在初步和二次MR分析中。初步MR分析确定了94种不同代谢物与4种泌尿系癌症之间的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癌症相关的68种血液代谢物。值得注意的是,31种代谢物在使用WM进行校正后仍然显著,另有37个暗示性因果关系。反向MR分析显示,遗传预测的前列腺癌与升高的4-羟基百菌清水平之间存在显着因果关系(IVW,合并OR:1.039,95%CI1.014-1.064,p=0.002;WM,合并OR:1.052,95%CI1.010-1.095,p=0.014)。
■这项全面的MR研究提供了对血液代谢物与泌尿系癌症之间因果关系的见解,揭示潜在的生物标志物和治疗靶点,从而解决理解方面的差距,并为泌尿系癌症研究和治疗中的针对性干预奠定基础。
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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).
UNASSIGNED: 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.