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山东大学学报 (医学版) ›› 2025, Vol. 63 ›› Issue (4): 44-50.doi: 10.6040/j.issn.1671-7554.0.2024.0686

• 营养、肠道微生态及相关疾病 • 上一篇    

两样本孟德尔随机化分析肠道菌群与肝外胆管癌的因果关系

王小磊1,方骏1,王安1,朱武晖1,史光军2   

  1. 1.青岛市第三人民医院肝胆外科, 山东 青岛 266041;2.青岛市市立医院肝胆外科, 山东 青岛 266011
  • 发布日期:2025-04-08
  • 通讯作者: 史光军. E-mail:sgjzp@hotmail.com
  • 基金资助:
    山东省自然科学基金面上项目(ZR202103030420)

Two-sample Mendelian randomization of the relationship between gut microbiota and the risk of extrahepatic cholangiocarcinoma

WANG Xiaolei1, FANG Jun1, WANG An1, ZHU Wuhui1, SHI Guangjun2   

  1. 1. Department of Hepatobiliary Surgery, Qingdao Third Peoples Hospital, Qingdao 266041, Shandong, China;
    2. Department of Hepatobiliary Surgery, Qingdao Municipal Hospital, Qingdao 266011, Shandong, China
  • Published:2025-04-08

摘要: 目的 采用两样本孟德尔随机化(Mendelian randomization, MR)方法评估肠道菌群与肝外胆管癌之间的因果关系。 方法 从MiBioGen数据库下载人类肠道菌群的全基因组关联研究(genome-wide association study, GWAS)数据作为暴露因素,从GWAS Catalog官网获取肝外胆管癌GWAS数据作为结局,使用随机效应逆方差加权法、加权中位数法、MR-Egger回归、简单模式和加权模式等5种方法进行MR分析,探索肠道菌群与肝外胆管癌发病风险之间的因果关系,并对MR结果进行敏感性分析、异质性检验、多效性检验,评估结果的可靠性和准确性。 结果 最终筛选出55个与肠道菌群有关的工具变量。MR结果显示颤螺菌属(OR=6.705,95%CI:1.612~27.890,P=0.009)、普氏菌属7(OR=2.565,95%CI:1.108~5.940,P=0.028)和Family_XIII_AD3011_group(OR=5.513,95%CI:1.186~25.620,P=0.029)与肝外胆管癌之间呈正向因果关系;氨基酸球菌科(OR=0.113,95%CI:0.026~0.500,P=0.004)、乳酸杆菌科(OR=0.349,95%CI:0.132~0.924,P=0.034)和丁酸弧菌属(OR=0.493,95%CI:0.252~0.963,P=0.039)与肝外胆管癌之间呈负向因果关系,同时MR Egger、加权中位数法、简单模式和加权模式与逆方差加权法的结果方向一致。敏感性分析结果提示不存在水平多效性及异质性,MR结果可靠。 结论 颤螺菌属、普氏菌属7、Family_XIII_AD3011_group、氨基酸球菌科、乳酸杆菌科和丁酸弧菌属与肝外胆管癌之间具有潜在因果关系。

关键词: 肠道菌群, 肝外胆管癌, 孟德尔随机化, 全基因组关联研究, 因果关系

Abstract: Objective To evaluate the causal relationship between gut microbiota and extrahepatic cholangiocarcinoma(ECC)by using two-sample Mendelian randomization(MR)analysis. Methods Genome-wide association study(GWAS)data of human gut microbiota were obtained from the official website of MiBioGen Alliance as exposure factors, and GWAS data of ECC were obtained from the official website of GWAS Catalog as the outcome. Five methods, including random effect inverse variance weighting, weighted median, MR-Egger regression, simple model and weighted model, were used to conduct two-sample MR to explore the causal relationship between gut microbiota and the risk of ECC. Sensitivity analysis, heterogeneity test and pleiotropy test were also conducted to evaluate the reliability and accuracy of the results. Results Finally, 55 instrumental variables related to gut microbiota were selected. MR results showed that the Oscillospira(OR=6.705, 95%CI: 1.612-27.890, P=0.009), Prevotella 7(OR=2.565, 95%CI: 1.108-5.940, P=0.028) and Family_XIII_AD3011_group(OR=5.513, 95%CI: 1.186-25.620, P=0.029)were positively correlated with ECC. There was a negative causal relationship between Acidaminococcaceae(OR=0.113, 95%CI: 0.026-0.500, P=0.004), Lactobacillaceae(OR=0.349, 95%CI: 0.132-0.924, P=0.034), Butyrivibrio(OR=0.493, 95%CI: 0.252-0.963, P=0.039)and ECC. The direction of MR Egger, weighted median method, simple model and weighted model was consistent with inverse variance weighted method. Sensitivity analysis showed no horizontal pleiotropy and heterogeneity, and MR results were reliable. Conclusion There is a potential causal relationship between Oscillospira, Prevotella 7, Family_XIII_AD3011_group, Acidaminococcaceae, Lactobacillaceae, Butyrivibrio and ECC.

Key words: Gut microbiota, Extrahepatic cholangiocarcinoma, Mendelian randomization, Genome-wide association study, Causal relationship

中图分类号: 

  • R735.7
[1] 刘洋, 阮祥, 段安琪, 等. 《英国胃肠病学会胆管癌诊治指南(2023版)》更新解读[J]. 中国实用外科杂志, 2024, 44(3): 292-299. LIU Yang, RUAN Xiang, DUAN Anqi, et al. Update and interpretation of British gastroenterology association guidelines for diagnosis and treatment of cholangiocarcinoma(2023 edition)[J]. Chinese Journal of Practical Surgery, 2024, 44(3): 292-299.
[2] Vithayathil M, Khan SA. Current epidemiology of cholangiocarcinoma in western countries[J]. J Hepatol, 2022, 77(6): 1690-1698.
[3] Benson AB, DAngelica MI, Abrams T, et al. NCCN guidelines® insights: biliary tract cancers, version 2.2023[J]. J Natl Compr Canc Netw, 2023, 21(7): 694-704.
[4] Brindley PJ, Bachini M, Ilyas SI, et al. Cholangiocarcinoma[J]. Nat Rev Dis Primers, 2021, 7(1): 65. doi:10.1038/s41572-021-00300-2
[5] 彭彬彬,周智. 胆管癌危险因素研究进展[J]. 临床医学进展, 2023, 13(3): 4025-4031.
[6] 龙祯, 孔棣. 肝外胆管癌致病因素的研究进展[J]. 江西中医药, 2017, 48(8): 72-75. LONG Zhen, KONG Di. Research progress on pathogenic factors of extrahepatic cholangiocarcinoma[J]. Jiangxi Journal of Traditional Chinese Medicine, 2017, 48(8): 72-75.
[7] Qiu P, Ishimoto T, Fu LF, et al. The gut microbiota in inflammatory bowel disease[J]. Front Cell Infect Microbiol, 2022, 12: 733992. doi:10.3389/fcimb.2022.733992
[8] Zhang LL, Chu JJ, Hao WH, et al. Gut microbiota and type 2 diabetes mellitus: association, mechanism, and translational applications[J]. Mediators Inflamm, 2021, 2021: 5110276. doi:10.1155/2021/5110276
[9] Pant A, Maiti TK, Mahajan D, et al. Human gut microbiota and drug metabolism[J]. Microb Ecol, 2023, 86(1): 97-111.
[10] Campbell C, Kandalgaonkar MR, Golonka RM, et al. Crosstalk between gut microbiota and host immunity: impact on inflammation and immunotherapy[J]. Biomedicines, 2023, 11(2): 294. doi:10.3390/biomedicines11020294
[11] Quaglio AEV, Grillo TG, De Oliveira ECS, et al. Gut microbiota, inflammatory bowel disease and colorectal cancer[J]. World J Gastroenterol, 2022, 28(30): 4053-4060.
[12] Cai J, Sun LL, Gonzalez FJ. Gut microbiota-derived bile acids in intestinal immunity, inflammation, and tumorigenesis[J]. Cell Host Microbe, 2022, 30(3): 289-300.
[13] Birney E. Mendelian randomization[J]. Cold Spring Harb Perspect Med, 2022, 12(4): a041302. doi:10.1101/cshperspect.a041302
[14] 王莉娜, Zhang Zuofeng. 孟德尔随机化法在因果推断中的应用[J]. 中华流行病学杂志, 2017, 38(4): 547-552. WANG Lina, ZHANG Zuofeng. Mendelian randomization approach, used for causal inferences[J]. Chinese Journal of Epidemiology, 2017, 38(4): 547-552.
[15] Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomization: the STROBE-MR statement[J]. JAMA, 2021, 326(16): 1614-1621.
[16] Zheng ZM, Hou XX, Bian ZX, et al. Gut microbiota and colorectal cancer metastasis[J]. Cancer Lett, 2023, 555: 216039. doi:10.1016/j.canlet.2022.216039
[17] Matsushita M, Fujita K, Hayashi T, et al. Gut microbiota-derived short-chain fatty acids promote prostate cancer growth via IGF1 signaling[J]. Cancer Res, 2021, 81(15): 4014-4026.
[18] Long YW, Tang LH, Zhou YY, et al. Causal relationship between gut microbiota and cancers: a two-sample Mendelian randomisation study[J]. BMC Med, 2023, 21(1): 66. doi:10.1186/s12916-023-02761-6
[19] Ai DM, Xing YL, Zhang QC, et al. Joint analysis of microbial and immune cell abundance in liver cancer tissue using a gene expression profile deconvolution algorithm combined with foreign read remapping[J]. Front Immunol, 2022, 13: 853213. doi:10.3389/fimmu.2022.853213
[20] Guo H, Yu LL, Tian FW, et al. The potential therapeutic role of Lactobacillaceae rhamnosus for treatment of inflammatory bowel disease[J]. Foods, 2023, 12(4): 692. doi:10.3390/foods12040692
[21] Stoeva MK, Garcia-So J, Justice N, et al. Butyrate-producing human gut symbiont, Clostridium butyricum, and its role in health and disease[J]. Gut Microbes, 2021, 13(1): 1-28.
[22] Jia W, Xie GX, Jia WP. Bile acid-microbiota crosstalk in gastrointestinal inflammation and carcinogenesis[J]. Nat Rev Gastroenterol Hepatol, 2018, 15(2): 111-128.
[23] Collins SL, Stine JG, Bisanz JE, et al. Bile acids and the gut microbiota: metabolic interactions and impacts on disease[J]. Nat Rev Microbiol, 2023, 21(4): 236-247.
[24] Song YC, Wang XC, Lu XH, et al. Exposure to microcystin-LR promotes colorectal cancer progression by altering gut microbiota and associated metabolites in APCmin/+ mice[J]. Toxins, 2024, 16(5): 212. doi:10.3390/toxins16050212
[25] Yang JP, Li YN, Wen ZQ, et al. Oscillospira-a candidate for the next-generation probiotics[J]. Gut Microbes, 2021, 13(1): 1987783. doi:10.1080/19490976.2021.1987783
[26] Wu J, Xu S, Xiang CJ, et al. Tongue coating microbiota community and risk effect on gastric cancer[J]. J Cancer, 2018, 9(21): 4039-4048.
[27] Haneishi Y, Furuya Y, Hasegawa M, et al. Inflammatory bowel diseases and gut microbiota[J]. Int J Mol Sci, 2023, 24(4): 3817. doi:10.3390/ijms24043817
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