您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(医学版)》

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    下一篇

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
[1] 杨宇凡,李悦,谢灏,林春华. 胆固醇水平与神经源性膀胱风险的因果关系[J]. 山东大学学报 (医学版), 2026, 64(5): 67-73.
[2] 段盈竹,董波,于睿. 内在情感与类风湿关节炎患者冠状动脉粥样硬化风险关系的孟德尔随机化分析[J]. 山东大学学报 (医学版), 2026, 64(4): 63-71.
[3] 赵万霞,詹群璋,金婷,刘玉新,曲崇正,吴剑纯. 基于孟德尔随机化分析肠道菌群、血液代谢物和肥胖的因果关系[J]. 山东大学学报 (医学版), 2026, 64(4): 72-82.
[4] 吴志晓,赵红洋. 孟德尔随机化分析免疫细胞表型与孤独症谱系障碍的因果关联[J]. 山东大学学报 (医学版), 2026, 64(3): 83-92.
[5] 陈婵,李巨章,何稳,吴巧珍. 基于两样本孟德尔随机化研究抑郁症和抗抑郁药物靶基因与睡眠呼吸暂停的关联[J]. 山东大学学报 (医学版), 2026, 64(1): 28-36.
[6] 王乐,罗清馨,吴思佳,吴雨桐,葛祎蕾,俞一凡,韦云,吉寒冰,刘铁梅,张紫妍,修佳伟,薛付忠,李洪凯. 虚弱和癫痫关联研究:前瞻性队列和孟德尔随机化分析[J]. 山东大学学报 (医学版), 2025, 63(9): 20-30.
[7] 刘位龙,王玎,赵超,王宁,张旭,苏萍,宋书典,张娜,迟蔚蔚. 基于BERT和图注意力网络的医疗文本因果关系抽取算法[J]. 山东大学学报 (医学版), 2025, 63(8): 61-68.
[8] 王雪梅,杨豪,宋洋,程世超,张婷婷,王艳春. 抗糖尿病药物与女性恶性肿瘤的因果关联:一项两样本孟德尔随机化分析[J]. 山东大学学报 (医学版), 2025, 63(6): 67-77.
[9] 黄馨,王梦雪,付书璠,张琦悦,徐力. 代谢综合征及其组分与消化系统恶性肿瘤的因果关联:两样本孟德尔随机化研究[J]. 山东大学学报 (医学版), 2025, 63(5): 86-94.
[10] 徐晶晶,王新起,张洋,许旺旺,高进. P2X7受体抑制剂对青春期创伤后应激障碍大鼠行为及肠道菌群的影响[J]. 山东大学学报 (医学版), 2025, 63(4): 1-9.
[11] 李建锋,张展,丁新华,高奋堂,何勤利,谢萍. 欧洲人群饮食因素与认知功能障碍关系的孟德尔随机化分析[J]. 山东大学学报 (医学版), 2025, 63(4): 36-43.
[12] 杨慧,苏士晶,李芬. 基于双向孟德尔随机化法探讨组织蛋白酶与衰弱的因果关联[J]. 山东大学学报 (医学版), 2025, 63(2): 67-76.
[13] 常宇,胡云峰,王会丰,郭静,张跳,郝雅琴,刘雨. 阑尾切除术与结直肠癌发病风险关联的孟德尔随机化研究[J]. 山东大学学报 (医学版), 2025, 63(2): 77-83.
[14] 杨慧敏,龚万里,侯雅琪,吴静,王洋,贺培凤,于琦. 20种氨基酸与冠心病的因果关联:孟德尔随机化研究[J]. 山东大学学报 (医学版), 2025, 63(12): 6-16.
[15] 杜凯豪,侯立朝,东小鸽,薛伟伟,何洁洁,罗兰明慧,蒋威,汪占金,王展. 东亚人肠道菌群与胰腺癌关系:基于孟德尔随机化方法的遗传学证据[J]. 山东大学学报 (医学版), 2025, 63(12): 44-52.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!