Causality Between Immune Cells, Metabolites and Breast Cancer: Mendelian Randomization and Mediation Analysis.

IF 2.1 4区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Changlong Wei, Changwang Li, Gongyin Zhang, Honghui Li, Jingsong Li, Jinsheng Zeng
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引用次数: 0

Abstract

Previous studies have shown that immune cells and metabolites are associated with the development of breast cancer, but the causal relationship is unclear. We use Mendelian randomization (MR) to explore potential connections between them. Based on two sample MR studies, we evaluated the causal relationship between 731 immune cell traits, 1400 metabolites and breast cancer. In addition, we evaluated the mediating role of metabolites between immune cells and breast cancer using two-step MR Studies. Pooled GWAS data on 731 immune cell traits (n = 3757), 1400 metabolites (n = 8299) and breast cancer (ncase = 122,977, ncontrol = 105,974) were obtained from publicly available authoritative databases. We mainly used inverse variance weighted (IVW) method combined with Bayesian weighted MR (BWMR), MR-Egger, weighted median, simple mode, and weighted mode methods. The results of MR Studies showed that 3 immune cells and 15 metabolites were associated with an increased risk of breast cancer. 8 immune cells and 11 metabolites are associated with a reduced risk of breast cancer. Mediating MR Analysis showed that 6 metabolites, Tricosanoyl sphingomyelin (d18:1/23:0) levels(Mediated proportion:17.5%), N-palmitoyl-heptadecasphingosine (d17:1/16:0) levels(8.74%), Inosine 5'-monophosphate (IMP) to phosphate ratio(11.3%), Glutamine to asparagine ratio(5.43%), Oleoyl-linoleoyl-glycerol (18:1/18:2) [2] levels(8.48%), and Sphinganine-1-phosphate levels(8.68%), were found to mediate the relationship between immune cells and breast cancer. Our study reveals a potential causal relationship among multiple immune cells traits, metabolites, and breast cancer. It provides valuable clues and potential therapeutic targets for breast cancer biomarker discovery and breast cancer treatment.

免疫细胞、代谢物与乳腺癌之间的因果关系:孟德尔随机化和中介分析
以往的研究表明,免疫细胞和代谢物与乳腺癌的发生有关,但其中的因果关系尚不清楚。我们采用孟德尔随机法(MR)来探索它们之间的潜在联系。基于两项样本 MR 研究,我们评估了 731 个免疫细胞性状、1400 个代谢物与乳腺癌之间的因果关系。此外,我们还利用两步 MR 研究评估了代谢物在免疫细胞和乳腺癌之间的中介作用。731 个免疫细胞性状(n = 3757)、1400 个代谢物(n = 8299)和乳腺癌(ncase = 122,977, ncontrol = 105,974)的汇总 GWAS 数据来自公开的权威数据库。我们主要采用了反方差加权法(IVW)结合贝叶斯加权磁共振法(BWMR)、MR-Egger 法、加权中值法、简单模式法和加权模式法。磁共振研究结果表明,3 种免疫细胞和 15 种代谢物与乳腺癌风险增加有关。8 种免疫细胞和 11 种代谢物与乳腺癌风险降低有关。介导磁共振分析显示,6 种代谢物、三苯甲酰基鞘磷脂(d18:1/23:0)水平(介导比例:17.5%)、N-棕榈酰基十七碳鞘磷脂(d17:1/16:0)水平(8.74%)、肌苷-5'-单磷酸(IMP)与磷酸的比率(11.3%)、谷氨酰胺与天冬酰胺之比(5.43%)、油酰-亚油酰-甘油(18:1/18:2)[2]水平(8.48%)和Sphinganine-1-磷酸水平(8.68%)被发现介导了免疫细胞与乳腺癌之间的关系。我们的研究揭示了多种免疫细胞特征、代谢物与乳腺癌之间的潜在因果关系。它为乳腺癌生物标志物的发现和乳腺癌的治疗提供了有价值的线索和潜在的治疗靶点。
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来源期刊
Biochemical Genetics
Biochemical Genetics 生物-生化与分子生物学
CiteScore
3.90
自引率
0.00%
发文量
133
审稿时长
4.8 months
期刊介绍: Biochemical Genetics welcomes original manuscripts that address and test clear scientific hypotheses, are directed to a broad scientific audience, and clearly contribute to the advancement of the field through the use of sound sampling or experimental design, reliable analytical methodologies and robust statistical analyses. Although studies focusing on particular regions and target organisms are welcome, it is not the journal’s goal to publish essentially descriptive studies that provide results with narrow applicability, or are based on very small samples or pseudoreplication. Rather, Biochemical Genetics welcomes review articles that go beyond summarizing previous publications and create added value through the systematic analysis and critique of the current state of knowledge or by conducting meta-analyses. Methodological articles are also within the scope of Biological Genetics, particularly when new laboratory techniques or computational approaches are fully described and thoroughly compared with the existing benchmark methods. Biochemical Genetics welcomes articles on the following topics: Genomics; Proteomics; Population genetics; Phylogenetics; Metagenomics; Microbial genetics; Genetics and evolution of wild and cultivated plants; Animal genetics and evolution; Human genetics and evolution; Genetic disorders; Genetic markers of diseases; Gene technology and therapy; Experimental and analytical methods; Statistical and computational methods.
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