Causal relationships between immune cells, plasma metabolites and lung adenocarcinoma: a two-step, two-sample Mendelian randomization study

免疫细胞、血浆代谢物与肺腺癌之间的因果关系:一项两步双样本孟德尔随机化研究

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Abstract

Background: Immune cells are crucial components of the immune system and significantly influence tumor progression. However, their direct involvement in lung adenocarcinoma (LUAD) and the potential mediation by plasma metabolites remain unclear. We performed a two-step, two-sample Mendelian randomization (MR) study to explore these connections between immune cells, plasma metabolites, and LUAD. Methods: We collected data from the GWAS database and performed an MR study employing the inverse variance weighting (IVW) method. We calculated the total effect of immune cells on LUAD, the effect of immune cells on plasma metabolites, and the effect of plasma metabolites on LUAD. Additionally, we calculated the mediating effect and mediated proportion to explore the causal role of immune cells on LUAD and the mediating role of related metabolites in this association. Results: Mendelian randomization analysis identified a causal relationship between 14 immune cell traits (Plasmacytoid DC %DC, Granulocyte %leukocyte, CCR2 on granulocyte, etc.) and LUAD, while LUAD showed no causal relationship with these 14 immune cell traits. Furthermore, 21 plasma metabolites (Gamma-glutamylmethionine levels, Malonylcarnitine levels, Linoleoyl ethanolamide levels, etc.) were suggestively associated with LUAD. Moreover, a causal relationship was identified between these plasma metabolites and 11 immune cell traits. Notably, mediator MR analysis identified 9 mediating pathways (CCR2 on granulocyte via 5alpha-androstan-3beta, 17alpha-diol disulfate, etc.). KEGG enrichment analysis revealed significant enrichment in the Valine, leucine and isoleucine biosynthesis. Conclusions: Immune cells can affect the risk of LUAD through the above 9 pathways based on plasma metabolites which provide potential insights for constructing risk models for LUAD and identifying clinical biomarkers.

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