The causal relationship between gut microbiota (GM) and pancreatic cancer (PC) remains unclear. This study aimed to investigate the potential genes underlying this mechanism. GM Genome‐wide association study (GWAS) summary data were from the MiBioGen consortium. PC GWAS data were from the National Human Genome Research Institute‐European Bioinformatics Institute (NHGRI‐EBI) GWAS Catalogue. To detect the causal relationship between GM and PC, we implemented three complementary Mendelian randomization (MR) methods: Inverse Variance Weighting (IVW), MR‐Egger and Weighted Median, followed by sensitivity analyses. Furthermore, we integrated GM GWAS data with blood cis‐expression quantitative trait loci (eQTLs) and blood cis‐DNA methylation QTL (mQTLs) using Summary data‐based Mendelian Randomization (SMR) methods. This integration aimed to prioritize potential GM‐affecting genes through SMR analysis of two molecular traits. PC cis‐eQTLs and cis‐mQTLs were summarized from The Cancer Genome Atlas (TCGA) data. Through colocalization analysis of GM cis‐QTLs and PC cis‐QTLs data, we identified common genes that influence both GM and PC. Our study found a causal association between GM and PC, including four protective and five risk‐associated GM [Inverse Variance Weighted (IVW), p < 0.05]. No significant heterogeneity of instrumental variables (IVs) or horizontal pleiotropy was found. The gene SVBP was identified as a GM‐affecting gene using SMR analysis of two molecular traits (FDR<0.05, P_HEIDI>0.05). Additionally, two genes, MCM6 and RPS26, were implicated in the interaction between GM and PC based on colocalization analysis (PPH4>0.5). In summary, this study provides evidence for future research aimed at developing suitable therapeutic interventions and disease prevention.

中文翻译：

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肠道微生物群对胰腺癌的因果影响：孟德尔随机化和共定位研究

肠道微生物群 (GM) 与胰腺癌 (PC) 之间的因果关系仍不清楚。本研究旨在研究该机制背后的潜在基因。 GM 全基因组关联研究 (GWAS) 摘要数据来自 MiBioGen 联盟。 PC GWAS 数据来自国家人类基因组研究所-欧洲生物信息学研究所 (NHGRI-EBI) GWAS 目录。为了检测 GM 和 PC 之间的因果关系，我们实施了三种互补的孟德尔随机化 (MR) 方法：逆方差加权 (IVW)、MR-Egger 和加权中位数，然后进行敏感性分析。此外，我们使用基于汇总数据的孟德尔随机化（SMR）方法将 GM GWAS 数据与血液顺式表达数量性状位点（eQTL）和血液顺式 DNA 甲基化 QTL（mQTL）进行整合。这种整合旨在通过对两个分子性状的 SMR 分析来优先考虑潜在的 GM 影响基因。 PC cis-eQTL 和 cis-mQTL 是从癌症基因组图谱 (TCGA) 数据中总结出来的。通过对 GM 顺式 QTL 和 PC 顺式 QTL 数据的共定位分析，我们确定了影响 GM 和 PC 的共同基因。我们的研究发现 GM 和 PC 之间存在因果关系，包括四种保护性 GM 和五种风险相关 GM [逆方差加权 (IVW)，p< 0.05]。没有发现工具变量（IV）或水平多效性的显着异质性。使用两个分子性状的 SMR 分析（FDR<0.05，P_HEIDI>0.05）将基因 SVBP 鉴定为 GM 影响基因。此外，根据共定位分析（PPH4>0.5），两个基因 MCM6 和 RPS26 与 GM 和 PC 之间的相互作用有关。总之，这项研究为未来旨在开发合适的治疗干预措施和疾病预防的研究提供了证据。