肠道菌群、T细胞在结直肠癌发病中的因果关联：孟德尔随机化分析

doi:10.12122/j.issn.1673-4254.2025.12.23

南方医科大学学报 ›› 2025, Vol. 45 ›› Issue (12): 2756-2766.doi: 10.12122/j.issn.1673-4254.2025.12.23

• • 上一篇

肠道菌群、T细胞在结直肠癌发病中的因果关联：孟德尔随机化分析

喻珍妮¹(), 高竟哲¹, 孙惠¹, 冯芹², 那效旗¹, 张宁¹(), 沈昆双¹, 王媛媛¹, 王喜军¹()

^1.黑龙江中医药大学药学院经方与现代中药融合创新全国重点实验室，黑龙江哈尔滨 150040
^2.国家胶类中药工程技术研究中心，山东聊城 252201

收稿日期:2025-04-14 出版日期:2025-12-20 发布日期:2025-12-22
通讯作者: 张宁,王喜军 E-mail:yzhennni@163.com;zhangning0454@163.com;xijunw@sina.com
作者简介:喻珍妮，在读硕士研究生，E-mail: yzhennni@163.com
基金资助:
国家中医药多学科创新团队项目(ZYYCXTD-D-202407);东阿阿胶股份有限公司横向课题(22042240002);黑龙江省重点研发计划(2022ZX02C04)

Causal relationship between gut microbiota and T cell subsets in the development of colorectal cancer: a Mendelian randomization analysis

Zhenni YU¹(), Jingzhe GAO¹, Hui SUN¹, Qin Feng², Xiaoqi NA¹, Ning ZHANG¹(), Kungshuang SHEN¹, Yuanyuan WANG¹, Xijun WANG¹()

^1.School of Pharmacy, State Key Laboratory of Integration and Innovation of Classic Formula and Modern Chinese Medicine, Heilongjiang University of Chinese Medicine, Harbin 150040, China
^2.National Engineering Technology Research Center for Gelatin-based Traditional Chinese Medicine, Liaocheng 252201, China

Received:2025-04-14 Online:2025-12-20 Published:2025-12-22
Contact: Ning ZHANG, Xijun WANG E-mail:yzhennni@163.com;zhangning0454@163.com;xijunw@sina.com

摘要/Abstract

摘要：

目的采用孟德尔随机化（MR）方法探讨肠道菌群、T细胞功能与结直肠癌（CRC）风险之间的因果关系。方法从MiBioGen数据库收集肠道菌群，T细胞和结直肠癌数据从公开的GWAS数据中获得，对三者进行双样本MR分析。将逆方差加权法作为主分析方法，同时采用MR-Egger、加权中位数法（Weighted median）、简单模式法（Simple mode）、加权模式法（Weighted mode）作为补充，MR-PRESSO和MR-Egger回归方法来检测水平多效性，通过Cochran's Q检验来识别异质性，采用留一法进行敏感性分析。结果在肠道菌群与T细胞的正向MR分析中，有11种肠道菌群存在因果关系，其中有6种肠道菌与T细胞是正相关（Prevotella7属：P=0.003；Ruminococcaceae UCG011属：P=0.033；Ruminococcaceae UCG004属：P=0.010；Eubacterium brachy group属：P=0.005；Lachnospiraceae FCS020 group属：P=0.028；Coprobacter属：P=0.033），另外5种肠道菌呈负相关；在T细胞与结直肠癌的正向MR分析中，发现CD25⁺⁺CD45RA^-CD4非调节性T细胞与结直肠癌风险呈负相关（IVW： OR=0.935，95% CI：0.878~0.995，P=0.035）；在肠道菌群与结直肠癌的正向MR分析中，有11种肠道菌群存在因果关系，其中有6种肠道菌与结直肠癌是正相关（Eubacterium xylanophilum group属：P=0.039；Selenomonadales目：P=0.014；Negativicutes纲：P=0.014；Bifidobacteriaceae科：P=0.048；Bifidobacteriales目：P=0.048；Coprococcus1属：P=0.033），另外5种肠道菌呈负相关。结论在肠道菌群、T细胞和结直肠癌三者关系中，Coprobacter属和Eubacterium xylanophilum group属是共有的菌，Eubacterium xylanophilum group属菌可通过促进CD25⁺⁺ CD45RA^-CD4非调节性T细胞的活性从而抑制结直肠癌的发展；而Coprobacter属菌可导致CD25⁺⁺CD45RA^-CD4非调节性T细胞失活从而使结直肠癌恶化。

关键词: 肠道菌群, T细胞, 结直肠癌, 孟德尔随机化, 因果关联

Abstract:

Objective To investigate the causal relationship between gut microbiota, T-cell function, and the risk of colorectal cancer. Methods Gut microbiota data from the MiBioGen database and T-cell and colorectal cancer data from publicly available GWAS datasets were obtained for analyzing the causality between gut microbiota, T-cell subsets, and the risk of colorectal cancer with two-sample Mendelian randomization (MR) analyses, using inverse variance weighting as the primary analytical method supplemented with MR-Egger, weighted median, simple mode, and weighted mode methods. Horizontal pleiotropy was assessed using MR-PRESSO and MR-Egger regression. Cochran's Q test was used to evaluate heterogeneity, and sensitivity analysis was performed using the leave-one-out method. Results In the Forward MR analysis of gut microbiota and T cells, 11 gut microbiota species showed causal relationships. Six of these species exhibited positive correlations with T cells, including Prevotella7 (P=0.003), Ruminococcaceae UCG011 (P=0.033), Ruminococcaceae UCG004 (0.010), Ebacterium brachy group (P=0.005), Lachnospiraceae FCS020 group (P=0.028), and Coprobacter (P=0.033), and the remaining 5 species showed negative correlations with T cells. Forward MR analysis of T cells and colorectal cancer suggested that CD25⁺⁺CD45RA^-CD4⁺non-regulatory T cells were negatively correlated with colorectal cancer risk (IVW: OR=0.935, 95% CI: 0.878-0.995; P=0.035). The analysis of gut microbiota and colorectal cancer suggested that 11 gut microbiota species were causally associated with colorectal cancer, and 6 of them (Eubacterium xylanophilum group, P=0.039; Selenomonadales, P=0.014; Negativicutes, P=0.014; Bifidobacteriaceae, P=0.048; Bifidobacteriales, P=0.048; and Coprococcus1, P=0.033) showed positive correlations and the remaining 5 showed negative correlations. Conclusion Coprobacter spp. and Eubacterium xylanophilum group spp. are causally associated with both T cell activity and colorectal cancer risk, and the former bacteria induce inactivation of CD25⁺⁺CD45RA^-CD4⁺ non-regulatory T cells to promote colorectal cancer progression, whereas the latter bacteria promote CD25⁺⁺CD45RA^-CD4⁺ non-regulatory T cell activity to inhibit colorectal cancer development.

Key words: gut microbiota, T cells, colorectal cancer, Mendelian randomization, causal association

喻珍妮, 高竟哲, 孙惠, 冯芹, 那效旗, 张宁, 沈昆双, 王媛媛, 王喜军. 肠道菌群、T细胞在结直肠癌发病中的因果关联：孟德尔随机化分析[J]. 南方医科大学学报, 2025, 45(12): 2756-2766.

Zhenni YU, Jingzhe GAO, Hui SUN, Qin Feng, Xiaoqi NA, Ning ZHANG, Kungshuang SHEN, Yuanyuan WANG, Xijun WANG. Causal relationship between gut microbiota and T cell subsets in the development of colorectal cancer: a Mendelian randomization analysis[J]. Journal of Southern Medical University, 2025, 45(12): 2756-2766.

图/表 11

参考文献 37

[1]	Ionescu VA, Gheorghe G, Bacalbasa N, et al. Colorectal cancer: from risk factors to oncogenesis[J]. Medicina: Kaunas, 2023, 59(9): 1646. doi：10.3390/medicina59091646
[2]	Belkaid Y, Harrison OJ. Homeostatic immunity and the microbiota[J]. Immunity, 2017, 46(4): 562-76. doi：10.1016/j.immuni.2017.04.008
[3]	Hu ZJ, Zhu HR, Jin YJ, et al. Correlation between gut microbiota and tumor immune microenvironment: a bibliometric and visualized study[J]. World J Clin Oncol, 2025, 16(2): 101611. doi：10.5306/wjco.v16.i2.101611
[4]	Schwabe RF, Jobin C. The microbiome and cancer[J]. Nat Rev Cancer, 2013, 13(11): 800-12. doi：10.1038/nrc3610
[5]	Yu LC. Microbiota dysbiosis and barrier dysfunction in inflammatory bowel disease and colorectal cancers: exploring a common ground hypothesis[J]. J Biomed Sci, 2018, 25(1): 79. doi：10.1186/s12929-018-0483-8
[6]	Hou X, Zheng Z, Wei J, et al. Effects of gut microbiota on immune responses and immunotherapy in colorectal cancer[J]. Front Immunol, 2022, 13: 1030745. doi：10.3389/fimmu.2022.1030745
[7]	Scarpellini E, Ianiro G, Attili F, et al. The human gut microbiota and virome: Potential therapeutic implications[J]. Dig Liver Dis, 2015, 47(12): 1007-12. doi：10.1016/j.dld.2015.07.008
[8]	Quaglio AEV, Grillo TG, De Oliveira ECS, et al. Gut microbiota, inflammatory bowel disease and colorectal cancer[J]. World J Gastroenterol, 2022, 28(30): 4053-60. doi：10.3748/wjg.v28.i30.4053
[9]	Shim JA, Ryu JH, Jo Y, et al. The role of gut microbiota in T cell immunity and immune mediated disorders[J]. Int J Biol Sci, 2023, 19(4): 1178-91. doi：10.7150/ijbs.79430
[10]	Kumar BV, Connors TJ, Farber DL. Human T cell development, localization, and function throughout life[J]. Immunity, 2018, 48(2): 202-13. doi：10.1016/j.immuni.2018.01.007
[11]	Ni JJ, Li XS, Zhang H, et al. Mendelian randomization study of causal link from gut microbiota to colorectal cancer[J]. BMC Cancer, 2022, 22(1): 1371. doi：10.1186/s12885-022-10483-w
[12]	Goodwin AC, Destefano Shields CE, Wu S, et al. Polyamine catabolism contributes to enterotoxigenic Bacteroides fragilis-induced colon tumorigenesis[J]. Proc Natl Acad Sci USA, 2011, 108(37): 15354-9. doi：10.1073/pnas.1010203108
[13]	Brennan CA, Clay SL, et al. Fusobacterium nucleatumdrives a pro-inflammatory intestinal microenvironment through metabolite receptor-dependent modulation of IL-17 expression[J]. Gut Microbes, 2021, 13: 1987780. doi：10.1080/19490976.2021.1987780
[14]	Galon J, Costes A, Sanchez-Cabo F, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome[J]. Science, 2006, 313(5795): 1960-4. doi：10.1126/science.1129139
[15]	Salama P, Phillips M, Grieu F, et al. Tumor-infiltrating FOXP3⁺ T regulatory cells show strong prognostic significance in colorectal cancer[J]. J Clin Oncol, 2009, 27(2): 186-92. doi：10.1200/jco.2008.18.7229
[16]	Zheng Z, Wieder T, Mauerer B, et al. T cells in colorectal cancer: unravelling the function of different T cell subsets in the tumor microenvironment[J]. Int J Mol Sci, 2023, 24(14): 11673. doi：10.3390/ijms241411673
[17]	Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data[J]. Genet Epidemiol, 2013, 37(7): 658-65. doi：10.1002/gepi.21758
[18]	Fong W, Li Q, Yu J. Gut microbiota modulation: a novel strategy for prevention and treatment of colorectal cancer[J]. Oncogene, 2020, 39(26): 4925-43. doi：10.1038/s41388-020-1341-1
[19]	Yuan H, Gui R, Wang Z, et al. Gut microbiota: a novel and potential target for radioimmunotherapy in colorectal cancer[J]. Front Immunol, 2023, 14: 1128774. doi：10.3389/fimmu.2023.1128774
[20]	Smith GD, Ebrahim S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental deter-minants of disease[J]? Int J Epidemiol, 2003, 32(1): 1-22. doi：10.1093/ije/dyg070
[21]	胡旭焘.肠道菌群,循环代谢物与胆石症：一项孟德尔随机化研究[D].吉林大学, 2024. 003850.
[22]	Shan J, Hu X, Chen T, et al. COVID-19 vaccination and the risk of autoimmune diseases: a Mendelian randomization study[J]. Front Public Health, 2024, 12: 1322140. doi：10.3389/fpubh.2024.1322140
[23]	Burgess S, Bowden J, Fall T, et al. Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants[J]. Epidemiology, 2017, 28(1): 30-42. doi：10.1097/ede.0000000000000559
[24]	Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through egger regression[J]. Int J Epidemiol, 2015, 44(2): 512-25. doi：10.1093/ije/dyv080
[25]	Bowden J, Davey Smith G, Haycock PC, et al. Consistent estimation in mendelian randomization with some invalid instruments using a weighted Median estimator[J]. Genet Epidemiol, 2016, 40(4): 304-14. doi：10.1002/gepi.21965
[26]	Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption[J]. Int J Epidemiol, 2017, 46(6): 1985-98. doi：10.1093/ije/dyx102
[27]	Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method[J]. Eur J Epidemiol, 2017, 32(5): 377-89. doi：10.1007/s10654-017-0255-x
[28]	Verbanck M, Chen CY, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases[J]. Nat Genet, 2018, 50(5): 693-8. doi：10.1038/s41588-018-0099-7
[29]	Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies[J]. Hum Mol Genet, 2018, 27(r2): R195-208. doi：10.1093/hmg/ddy163
[30]	Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data[J]. PLoS Genet, 2017, 13(11): e1007081. doi：10.1371/journal.pgen.1007081
[31]	Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians[J]. BMJ, 2018, 362: k601. doi：10.1136/bmj.k601
[32]	Teng NMY, Kiu R, Evans R, et al. Allocoprobacillus halotolerans gen. nov., sp. nov and Coprobacter tertius sp. nov., isolated from human gut microbiota[J]. Int J Syst Evol Microbiol, 2023, 73(7): 5950-8. doi：10.1099/ijsem.0.005950
[33]	Yu J, Feng Q, Wong SH, et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer[J]. Gut, 2017, 66(1): 70-8. doi：10.1136/gutjnl-2015-309800
[34]	Silva M, Brunner V, Tschurtschenthaler M. Microbiota and colorectal cancer: from gut to bedside[J]. Front Pharmacol, 2021, 12: 760280. doi：10.3389/fphar.2021.760280
[35]	Wang Y, Wan X, Wu X, et al. Eubacterium rectale contributes to colorectal cancer initiation via promoting colitis[J]. Gut Pathog, 2021, 13(1): 2. doi：10.1186/s13099-020-00396-z
[36]	Li HY, Wang Y, Shao SM, et al. Rabdosia serra alleviates dextran sulfate sodium salt-induced colitis in mice through anti-inflammation, regulating Th17/Treg balance, maintaining intestinal barrier integrity, and modulating gut microbiota[J]. J Pharm Anal, 2022, 12(6): 824-38. doi：10.1016/j.jpha.2022.08.001
[37]	Park JS, Gazzaniga FS, Wu M, et al. Targeting PD-L2-RGMb overcomes microbiome-related immunotherapy resistance[J]. Nature, 2023, 617(7960): 377-85. doi：10.1038/s41586-023-06026-3

Outcome	nSNP	SE	P	OR	95% CI
genus.Prevotella7	22	0.076	0.944	1.005	0.866-1.1167
genus.Faecalibacterium	23	0.040	0.216	1.051	0.972-1.136
genus.Ruminococcaceae UCG011	22	0.057	0.347	0.948	0.848-1.06
genus.Ruminococcaceae UCG004	23	0.036	0.406	0.970	0.904-1.042
genus.Eubacterium brachy group	22	0.054	0.734	1.019	0.916-1.132
genus.Lachnospiraceae FCS020 group	23	0.033	0.427	0.974	0.913-1.039
genus.Eubacterium xylanophilum group	23	0.035	0.774	0.990	0.924-1.061
genus.Coprobacter	23	0.052	0.468	1.038	0.938-1.149
genus.Prevotella9	23	0.039	0.835	1.008	0.934-1.088
family.Enterobacteriaceae	23	0.032	0.987	1.001	0.940-1.065
order.Enterobacteriales	23	0.032	0.987	1.001	0.940-1.065

Outcome	nSNP	SE	P	OR	95% CI
genus.Prevotella7	22	0.076	0.944	1.005	0.866-1.1167
genus.Faecalibacterium	23	0.040	0.216	1.051	0.972-1.136
genus.Ruminococcaceae UCG011	22	0.057	0.347	0.948	0.848-1.06
genus.Ruminococcaceae UCG004	23	0.036	0.406	0.970	0.904-1.042
genus.Eubacterium brachy group	22	0.054	0.734	1.019	0.916-1.132
genus.Lachnospiraceae FCS020 group	23	0.033	0.427	0.974	0.913-1.039
genus.Eubacterium xylanophilum group	23	0.035	0.774	0.990	0.924-1.061
genus.Coprobacter	23	0.052	0.468	1.038	0.938-1.149
genus.Prevotella9	23	0.039	0.835	1.008	0.934-1.088
family.Enterobacteriaceae	23	0.032	0.987	1.001	0.940-1.065
order.Enterobacteriales	23	0.032	0.987	1.001	0.940-1.065

MR method	nSNP	SE	P	OR	95% CI
IVW	3	0.032	0.035	0.935	0.878-0.995
Weighted median	3	0.031	0.006	0.919	0.866-0.976
MR-Egger	3	0.107	0.566	0.917	0.743-1.131
Simple mode	3	0.044	0.176	0.913	0.837-0.996
Weighted mode	3	0.041	0.144	0.909	0.839-0.985

MR method	nSNP	SE	P	OR	95% CI
IVW	3	0.032	0.035	0.935	0.878-0.995
Weighted median	3	0.031	0.006	0.919	0.866-0.976
MR-Egger	3	0.107	0.566	0.917	0.743-1.131
Simple mode	3	0.044	0.176	0.913	0.837-0.996
Weighted mode	3	0.041	0.144	0.909	0.839-0.985

MR method	nSNP	SE	P	OR	95% CI
IVW	25	0.056	0.195	1.076	0.963-1.201
Weighted median	25	0.079	0.142	1.123	0.962-1.312
MR-Egger	25	0.231	0.838	0.953	0.607-1.499
Simple mode	25	0.140	0.277	1.168	0.888-1.536
Weighted mode	25	0.124	0.222	1.168	0.916-1.489

肠道菌群、T细胞在结直肠癌发病中的因果关联：孟德尔随机化分析

Causal relationship between gut microbiota and T cell subsets in the development of colorectal cancer: a Mendelian randomization analysis

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 37

相关文章 15

编辑推荐

Metrics

本文评价

Outcome	nSNP	SE	P	OR	95% CI
genus.Ruminococcaceae NK4A214 group	5	0.028	0.947	0.998	0.945-1.054
genus.Eubacterium xylanophilum group	5	0.030	0.321	0.971	0.916-1.029
genus.Ruminococcaceae UCG010	5	0.037	0.527	0.977	0.909-1.050
order.Selenomonadales	5	0.037	0.483	0.974	0.906-1.048
genus.Family XIII AD3011 group	5	0.029	0.762	1.009	0.953-1.067
genus.Coprobacter	5	0.069	0.843	1.014	0.885-1.162
Class.Negativicutes	5	0.037	0.483	0.974	0.906-1.048
family.Bifidobacteriaceae	5	0.029	0.675	0.988	0.934-1.045
genus.Allisonella	3	0.122	0.028	0.765	0.603-0.971
order.Bifidobacteriales	5	0.029	0.675	0.988	0.934-1.045
genus.Coprococcus1	5	0.029	0.892	0.996	0.942-1.054

Exposure	Outcome	MR method	Q	Q_df	Q_pval
genus.Prevotella7	CRC	MR-Rgger	7.239	10	0.703
genus.Prevotella7	CRC	IVW	9.043	11	0.618
genus.Faecalibacterium	CRC	MR-Rgger	13.261	11	0.277
genus.Faecalibacterium	CRC	IVW	13.262	12	0.350
genus.Ruminococcaceae UCG011	CRC	MR-Rgger	6.409	6	0.379
genus.Ruminococcaceae UCG011	CRC	IVW	6.820	7	0.448
genus.Ruminococcaceae UCG004	CRC	MR-Rgger	10.434	10	0.403
genus.Ruminococcaceae UCG004	CRC	IVW	11.115	11	0.434
genus.Eubacterium brachy group	CRC	MR-Rgger	9.150	9	0.424
genus.Eubacterium brachy group	CRC	IVW	9.264	10	0.507
genus.Lachnospiraceae FCS020 group	CRC	MR-Rgger	6.834	13	0.910
genus.Lachnospiraceae FCS020 group	CRC	IVW	6.848	14	0.940
genus.Eubacterium xylanophilum group	CRC	MR-Rgger	4.699	9	0.860
genus.Eubacterium xylanophilum group	CRC	IVW	7.284	10	0.698
genus.Coprobacter	CRC	MR-Rgger	15.480	12	0.216
genus.Coprobacter	CRC	IVW	15.643	13	0.269
genus.Prevotella9	CRC	MR-Rgger	16.705	16	0.405
genus.Prevotella9	CRC	IVW	16.715	17	0.474
family.Enterobacteriaceae	CRC	MR-Rgger	9.278	9	0.412
family.Enterobacteriaceae	CRC	IVW	9.399	10	0.495
order.Enterobacteriales	CRC	MR-Rgger	9.278	9	0.412
order.Enterobacteriales	CRC	IVW	9.399	10	0.495
T Cells	CRC	MR-Rgger	3.227	1	0.072
T Cells	CRC	IVW	3.356	2	0.187
genus.Ruminococcaceae NK4A214 group	T Cells	MR-Rgger	13.403	14	0.495
genus.Ruminococcaceae NK4A214 group	T Cells	IVW	14.064	15	0.521
genus.Eubacterium xylanophilum group	T Cells	MR-Rgger	12.167	9	0.204
genus.Eubacterium xylanophilum group	T Cells	IVW	12.770	10	0.237
genus.Ruminococcaceae UCG010	T Cells	MR-Rgger	5.807	6	0.445
genus.Ruminococcaceae UCG010	T Cells	IVW	6.325	7	0.502
order.Selenomonadales	T Cells	MR-Rgger	9.926	11	0.537
order.Selenomonadales	T Cells	IVW	13.547	12	0.331
genus.Family XIII AD3011 group	T Cells	MR-Rgger	16.241	13	0.236
genus.Family XIII AD3011 group	T Cells	IVW	16.387	14	0.290
genus.Coprobacter	T Cells	MR-Rgger	17.482	12	0.132
genus.Coprobacter	T Cells	IVW	17.762	13	0.167
Class.Negativicutes	T Cells	MR-Rgger	9.926	11	0.537
Class.Negativicutes	T Cells	IVW	13.547	12	0.331
family.Bifidobacteriaceae	T Cells	MR-Rgger	5.992	15	0.980
family.Bifidobacteriaceae	T Cells	IVW	7.605	16	0.960
genus.Allisonella	T Cells	MR-Rgger	5.460	7	0.604
genus.Allisonella	T Cells	IVW	6.660	8	0.574
order.Bifidobacteriales	T Cells	MR-Rgger	5.992	15	0.980
order.Bifidobacteriales	T Cells	IVW	7.605	16	0.960

Exposure	Outcome	MR method	Q	Q_df	Q_pval
CRC	genus.Prevotella7	MR-Rgger	33.667	20	0.028
CRC	genus.Prevotella7	IVW	36.090	21	0.021
CRC	genus.Faecalibacterium	MR-Rgger	54.873	21	<0.001
CRC	genus.Faecalibacterium	IVW	55.369	22	<0.001
CRC	genus.Ruminococcaceae UCG011	MR-Rgger	13.506	20	0.855
CRC	genus.Ruminococcaceae UCG011	IVW	15.151	21	0.815
CRC	genus.Ruminococcaceae UCG004	MR-Rgger	23.003	21	0.344
CRC	genus.Ruminococcaceae UCG004	IVW	23.825	22	0.356
CRC	genus.Eubacterium brachy group	MR-Rgger	15.511	20	0.746
CRC	genus.Eubacterium brachy group	IVW	18.053	21	0.646
CRC	genus.Lachnospiraceae FCS020 group	MR-Rgger	29.009	21	0.114
CRC	genus.Lachnospiraceae FCS020 group	IVW	29.009	22	0.145
CRC	genus.Eubacterium xylanophilum group	MR-Rgger	30.851	21	0.076
CRC	genus.Eubacterium xylanophilum group	IVW	31.688	22	0.083
CRC	genus.Coprobacter	MR-Rgger	34.800	21	0.030
CRC	genus.Coprobacter	IVW	34.918	22	0.040
CRC	genus.Prevotella9	MR-Rgger	19.765	21	0.536
CRC	genus.Prevotella9	IVW	30.024	22	0.118
CRC	family.Enterobacteriaceae	MR-Rgger	26.429	21	0.191
CRC	family.Enterobacteriaceae	IVW	26.554	22	0.229
CRC	order.Enterobacteriales	MR-Rgger	26.429	21	0.191
CRC	order.Enterobacteriales	IVW	26.554	22	0.229
CRC	T Cells	MR-Rgger	17.594	23	0.779
CRC	T Cells	IVW	17.885	24	0.809
T Cells	genus.Ruminococcaceae NK4A214 group	MR-Rgger	1.310	3	0.727
T Cells	genus.Ruminococcaceae NK4A214 group	IVW	1.356	4	0.852
T Cells	genus.Eubacterium xylanophilum group	MR-Rgger	2.079	3	0.556
T Cells	genus.Eubacterium xylanophilum group	IVW	2.242	4	0.691
T Cells	genus.Ruminococcaceae UCG010	MR-Rgger	0.939	3	0.816
T Cells	genus.Ruminococcaceae UCG010	IVW	5.875	4	0.209
T Cells	order.Selenomonadales	MR-Rgger	6.388	3	0.094
T Cells	order.Selenomonadales	IVW	8.155	4	0.086
T Cells	genus.Family XIII AD3011 group	MR-Rgger	1.403	3	0.705
T Cells	genus.Family XIII AD3011 group	IVW	1.482	4	0.830
T Cells	genus.Coprobacter	MR-Rgger	11.202	3	0.011
T Cells	genus.Coprobacter	IVW	11.278	4	0.024
T Cells	Class.Negativicutes	MR-Rgger	6.388	3	0.094
T Cells	Class.Negativicutes	IVW	8.155	4	0.086
T Cells	family.Bifidobacteriaceae	MR-Rgger	2.909	3	0.406
T Cells	family.Bifidobacteriaceae	IVW	2.979	4	0.561
T Cells	genus.Allisonella	MR-Rgger	1.503	1	0.220
T Cells	genus.Allisonella	IVW	2.060	2	0.357
T Cells	order.Bifidobacteriales	MR-Rgger	2.909	3	0.406
T Cells	order.Bifidobacteriales	IVW	2.979	4	0.561
T Cells	genus.Coprococcus1	MR-Rgger	4.329	3	0.228
T Cells	genus.Coprococcus1	IVW	4.585	4	0.333

[1]	何榕茂, 方泽扬, 张芸芸, 吴友谅, 梁世秀, 计涛, 陈科全, 王斯琪. 铁死亡相关基因对溃疡性结肠炎具有诊断预测价值[J]. 南方医科大学学报, 2025, 45(9): 1927-1937.
[2]	邓楚玉, 王雪莹, 甘立祥, 王大禹, 郑晓燕, 唐纯志. 电针足三里改善高脂血症小鼠的血脂紊乱：基于肠道菌群结构的改善[J]. 南方医科大学学报, 2025, 45(8): 1633-1642.
[3]	张宏博, 闫梦宇, 张建东, 孙培旺, 汪蕊, 郭园园. 吡非尼酮抑制调节性T细胞延缓小鼠膀胱癌进展[J]. 南方医科大学学报, 2025, 45(7): 1513-1518.
[4]	黄凯悦, 齐景馨, 罗文谦, 林怡萱, 陈梅妹, 甘慧娟. 温胆汤通过调控肠道菌群-胆汁酸轴改善代谢综合征痰证大鼠的代谢表型[J]. 南方医科大学学报, 2025, 45(6): 1174-1184.
[5]	翁诺舟, 谭彬, 曾文涛, 古家宇, 翁炼基, 郑克鸿. 过表达RGL1通过激活CDC42/RAC1复合体上调运动型黏着斑组装促进结直肠癌转移[J]. 南方医科大学学报, 2025, 45(5): 1031-1038.
[6]	马振南, 刘福全, 赵雪峰, 张晓微. DTX2促进奥沙利铂耐药的结直肠癌细胞增殖、侵袭和上皮间质转化[J]. 南方医科大学学报, 2025, 45(4): 829-836.
[7]	李文婕, 洪耀南, 黄蕊, 李煜宸, 张莹, 张蕴, 吴迪炯. 自身免疫性疾病是再生障碍性贫血的危险因素：一项孟德尔随机化分析[J]. 南方医科大学学报, 2025, 45(4): 871-879.
[8]	庆顺杰, 沈智勇. 过表达己糖激酶2通过激活JAK/STAT途径促进结直肠癌细胞的增殖、迁移和侵袭并调节肿瘤免疫微环境[J]. 南方医科大学学报, 2025, 45(3): 542-553.
[9]	罗嘉纯, Sodnomjamts Batzaya, 高雪锋, 陈晶宇, 余政颖, 熊莎莎, 曹虹. Akkermansia muciniphila改善gp120转基因小鼠的肠-脑相互作用障碍[J]. 南方医科大学学报, 2025, 45(3): 554-565.
[10]	殷丽霞, 牛民主, 张可妮, 耿志军, 胡建国, 李江艳, 李静. 升麻素抑制MAPK通路调节辅助性T细胞免疫平衡改善小鼠克罗恩病样结肠炎[J]. 南方医科大学学报, 2025, 45(3): 595-602.
[11]	高俊杰, 叶开, 吴竞. 槲皮素通过调控TP53基因抑制肾透明细胞癌的增殖和迁移[J]. 南方医科大学学报, 2025, 45(2): 313-321.
[12]	刘莹, 李柏睿, 李永财, 常禄博, 王娇, 杨琳, 颜永刚, 屈凯, 刘继平, 张岗, 沈霞. 加味逍遥丸通过神经递质调节、抗炎抗氧化及肠道菌群调控改善大鼠的抑郁样行为[J]. 南方医科大学学报, 2025, 45(2): 347-358.
[13]	潘兴旭, 张秉祺, 张智华, 曹秋实. 戈登杆菌属丰度降低与肾结石风险增加相关：一项孟德尔随机化分析与动物实验研究[J]. 南方医科大学学报, 2025, 45(11): 2405-2415.
[14]	马会华, 闫奎坡, 刘刚, 徐亚洲, 张磊, 李一卓. 1990~2021年心房颤动/扑动流行病学及其危险因素分析：基于2021年中国全球疾病负担研究与孟德尔随机化研究的系统分析[J]. 南方医科大学学报, 2025, 45(10): 2182-2190.
[15]	姚辰, 李文佳, 庞瑞明, 周继红. 臀肌腱炎、原发性髋关节病可能导致髂胫束综合征—一项孟德尔随机化研究[J]. 南方医科大学学报, 2024, 44(9): 1821-1830.