血液代谢物与结直肠癌发病风险的孟德尔随机化研究-现代医学

血液代谢物与结直肠癌发病风险的孟德尔随机化研究

作者：马小彤¹ 2 张惠芳¹ 2 王慧敏¹ 2 汪彦平¹ 2 郭庆红² 陈兆峰²

单位：1. 兰州大学第一临床医学院, 甘肃兰州 730000;
2. 兰州大学第一医院消化内科, 甘肃兰州 730000

分类号：R735.3

出版年·卷·期（页码）：2025·53·第三期（362-370）

摘要：

目的：采用两样本孟德尔随机化(MR)方法评估血液代谢物与结直肠癌(CRC)发病风险之间的因果关系。方法：基于公共数据库获取1 400种血液代谢物与CRC的全基因组关联研究数据。主要采用逆方差加权法进行分析，MR-Egger回归法、加权中位数法、简单模式法和加权模式法作为补充分析。利用MR-Egger回归截距、Cochrane's检验、MR-PRESSO和留一法进行敏感性分析以评价结果的稳健性。运用基于网络的Metabo Analyst软件对代谢物进行代谢通路分析。结果： MR分析结果显示，48种血液代谢物与CRC风险相关，其中26种血液代谢物与CRC发病风险呈负相关，22种血液代谢物与CRC发病风险呈正相关。敏感性分析显示，MR结果稳健且可靠。代谢通路分析显示，色氨酸代谢、β-丙氨酸代谢、鞘脂代谢、嘧啶代谢、泛酸和辅酶A的生物合成及缬氨酸、亮氨酸和异亮氨酸的生物合成等6条代谢通路与CRC相关。结论： 48种血液代谢物与CRC发病风险之间存在潜在的因果关联，6条代谢通路可能参与CRC的发生发展。然而，本研究结果及其与CRC发生发展的具体机制仍需进一步的实验研究验证，并有待更大样本量的研究提供证据支持。

Objective: To explore the causal relationship between blood metabolites and colorectal cancer(CRC) risk by using two-sample Mendelian randomization(MR) method. Methods: A genome-wide association study of 1 400 blood metabolites and CRC was obtained based on a public database. Analyses were mainly performed using inverse-variance weighted, with MR-Egger regression, weighted median method, simple mode method and weighted mode method as supplementary analysis. MR-Egger regression intercept, Cochrane's test, MR-PRESSO and leave-one-out method were used for sensitivity analyses to evaluate the robustness of the results. In addition, metabolic pathway analysis was performed using web-based Metabo Analyst. Results: MR analysis showed 48 blood metabolites were associated with CRC risk, 26 blood metabolites were negatively associated with CRC risk, and 22 blood metabolites were positively associated with CRC risk. Sensitivity analysis showed that the results of MR were robust and reliable. Six metabolic pathways, including tryptophan metabolism, β-alanine metabolism, sphingolipid metabolism, pyrimidine metabolism, pantothenate and coenzyme A biosynthesis, and valine, leucine, and isoleucine biosynthesis, were found to be associated with CRC. Conclusion: There is a potential causal association between 48 blood metabolites and the risk of CRC, and six metabolic pathways may be involved in the development of CRC. However, the results of this study and its specific mechanism with CRC development still need further experimental studies to validate and to be supported by evidence from studies with larger sample sizes.

参考文献：

[1] SUNG H,FERLAY J,SIEGEL R L,et al.Global cancer statistics 2020:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J].CA Cancer J Clin,2021,71(3):209-249.
[2] 李文静,杨杰,陈冬梅.WGCNA与机器学习算法识别结直肠癌发病机制中潜在panoptosis关键基因[J].东南大学学报(医学版),2023,42(5):688-696.
[3] PATEL S G,KARLITZ J J,YEN T,et al.The rising tide of early-onset colorectal cancer:a comprehensive review of epidemiology,clinical features,biology,risk factors,prevention,and early detection[J].Lancet Gastroenterol Hepatol,2022,7(3):262-274.
[4] SUN T,LIU Z,YANG Q.The role of ubiquitination and deubiquitination in cancer metabolism[J].Mol Cancer,2020,19(1):146-165.
[5] ANG J E,PAL A,ASAD Y J,et al.Modulation of plasma metabolite biomarkers of the MAPK pathway with MEK inhibitor RO4987655:pharmacodynamic and predictive potential in metastatic melanoma[J].Mol Cancer Ther,2017,16(10):2315-2323.
[6] IONESCU V A,GHEORGHE G,BACALBASA N,et al.Colorectal cancer:from risk factors to oncogenesis[J].Medicina(Kaunas),2023,59(9):1646-1659.
[7] SHEN Y,SUN M,ZHU J,et al.Tissue metabolic profiling reveals major metabolic alteration in colorectal cancer[J].Mol Omics,2021,17(3):464-471.
[8] ZABOR E C,KAIZER A M,HOBBS B P.Randomized controlled trials[J].Chest,2020,158(1S):S79-S87.
[9] SKRIVANKOVA V W,RICHMOND R C,WOOLF B A R,et al.Strengthening the reporting of observational studies in epidemiology using Mendelian randomization:the STROBE-MR statement[J].JAMA,2021,326(16):1614-1621.
[10] 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-R208.
[11] 黄为,廖建辉,陈顺德,等.甲状腺功能障碍与膝关节骨性关节炎因果关系的双向两样本孟德尔随机化分析[J].现代医学,2024,52(8):1159-1168.
[12] 王涛,姚韵靓,易燕锋.血浆脂质体与双向情感障碍的因果关联：一项孟德尔随机化研究[J].现代医学,2024,52(8):1224-1233.
[13] CHEN Y,LU T,PETTERSSON-KYMMER U,et al.Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases[J].Nat Genet,2023,55(1):44-53.
[14] YU X H,CAO R R,YANG Y Q,et al.Identification of causal metabolites related to multiple autoimmune diseases[J].Hum Mol Genet,2022,31(4):604-613.
[15] HOU Y,XIAO Z,ZHU Y,et al.Blood metabolites and chronic kidney disease:a Mendelian randomization study[J].BMC Med Genomics,2024,17(1):147-161.
[16] LING K,HONG M,JIN L,et al.Blood metabolomic and postpartum depression:a Mendelian randomization study[J].BMC Pregnancy Childbirth,2024,24(1):429-437.
[17] SANDERSON E.Multivariable Mendelian randomization and mediation[J].Cold Spring Harb Perspect Med,2021,11(2):a038984-a038996.
[18] GU Y,JIN Q,HU J,et al.Causality of genetically determined metabolites and metabolic pathways on osteoarthritis:a two-sample Mendelian randomization study[J].J Transl Med,2023,21(1):357-370.
[19] BOWDEN J,HEMANI G,DAVEY SMITH G.Invited commentary:detecting individual and global horizontal pleiotropy in Mendelian randomization-a job for the humble heterogeneity statistic?[J].Am J Epidemiol,2018,187(12):2681-2685.
[20] CAO Z,WU Y,LI Q,et al.A causal relationship between childhood obesity and risk of osteoarthritis:results from a two-sample Mendelian randomization analysis[J].Ann Med,2022,54(1):1636-1645.
[21] ZHENG J,BAIRD D,BORGES M C,et al.Recent developments in Mendelian randomization studies[J].Curr Epidemiol Rep,2017,4(4):330-345.
[22] PANG Z,ZHOU G,EWALD J,et al.Using MetaboAnalyst 5.0 for LC-HRMS spectra processing,multi-omics integration and covariate adjustment of global metabolomics data[J].Nat Protoc,2022,17(8):1735-1761.
[23] MODOUX M,ROLHION N,MANI S,et al.Tryptophan metabolism as a pharmacological target[J].Trends Pharmacol Sci,2021,42(1):60-73.
[24] MRSTNA K,KRCMOVA L K,SVEC F.Advances in kynurenine analysis[J].Clin Chim Acta,2023,547:117441-117466.
[25] KILUK M,LEWKOWICZ J,PAWLAK D,et al.Crosstalk between tryptophan metabolism via kynurenine pathway and carbohydrate metabolism in the context of cardio-metabolic risk-review[J].J Clin Med,2021,10(11):2484-2504.
[26] PARK J H,LEE J M,LEE E J,et al.Kynurenine promotes the goblet cell differentiation of HT-29 colon carcinoma cells by modulating Wnt,Notch and AhR signals[J].Oncol Rep,2018,39(4):1930-1938.
[27] YANG C S,LI G,YANG Z,et al.Cancer prevention by tocopherols and tea polyphenols[J].Cancer Lett,2013,334(1):79-85.
[28] YIN L,YAN H,CHEN K,et al.Diet-derived circulating antioxidants and risk of digestive system tumors:a Mendelian randomization study[J].Nutrients,2022,14(16):3274-3287.
[29] GU J,XIAO Y,SHU D,et al.Metabolomics analysis in serum from patients with colorectal polyp and colorectal cancer by(1)H-NMR spectrometry[J].Dis Markers,2019,3491852-3491866.
[30] WANG X,WANG J,RAO B,et al.Gut flora profiling and fecal metabolite composition of colorectal cancer patients and healthy individuals[J].Exp Ther Med,2017,13(6):2848-2854.
[31] SUN M,MA N,HE T,et al.Tryptophan(Trp) modulates gut homeostasis via aryl hydrocarbon receptor(AhR)[J].Crit Rev Food Sci Nutr,2020,60(10):1760-1768.
[32] ALA M.Tryptophan metabolites modulate inflammatory bowel disease and colorectal cancer by affecting immune system[J].Int Rev Immunol,2022,41(3):326-345.

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