Zhuang Xin, Zhang Xia, Yin Qingning, Yang Rong, Man Xiaoying, Wang Ruochen, Shi Yifen, Wang Hailin, Jiang Songfu
Department of Hematology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Qinghai Province Women and Children's Hospital, Xining, Qinghai, China.
Int Immunopharmacol. 2025 Jan 10;144:113593. doi: 10.1016/j.intimp.2024.113593. Epub 2024 Nov 25.
Prior research has demonstrated significant roles of metabolites and immune cells in the progression of lymphoma. Mendelian randomization studies have been conducted to assess the causal relationships among serum metabolites, immune cells, and lymphoma, further exploring the mediating role of serum metabolites.
Using summary-level data from genome-wide association studies (GWAS), we applied two-sample Mendelian randomization (TSMR) techniques, including Inverse Variance Weighted (IVW), Weighted Median, MR-Egger, Simple Mode, and Weighted Mode. These methods were employed to examine the causal links between genetically determined serum metabolites, immune cells, and six types of lymphoma. Additionally, reverse MR analysis investigated reverse causality, and two-step MR quantified the proportion of lymphoma effects mediated by immune cells through serum metabolites. MR-Egger regression and leave-one-out sensitivity tests evaluated the stability and reliability of our findings.
The study pinpointed specific serum metabolites and immune cell types causally related to six lymphoma variants. Serum metabolites were identified as mediators in the relationship between immune cells and lymphoma. The two-step Mendelian randomization confirmed this mediated causal relationship, with sensitivity analyses supporting the results' reliability and lack of pleiotropy.
The study establishes a causal connection between immune cells and lymphoma, partially mediated by serum metabolites, although the majority of the influence remains undefined. Future research should explore additional potential mediators. Clinically, there should be an increased focus on immune cells biomarkers for lymphoma patients. These results offer valuable insights for identifying lymphoma biomarkers and potential therapeutic targets.
先前的研究已证明代谢物和免疫细胞在淋巴瘤进展中具有重要作用。已开展孟德尔随机化研究以评估血清代谢物、免疫细胞和淋巴瘤之间的因果关系，进一步探索血清代谢物的中介作用。
利用全基因组关联研究（GWAS）的汇总水平数据，我们应用了双样本孟德尔随机化（TSMR）技术，包括逆方差加权（IVW）、加权中位数、MR-Egger、简单模式和加权模式。这些方法用于检验基因决定的血清代谢物、免疫细胞与六种淋巴瘤类型之间的因果联系。此外，反向MR分析研究了反向因果关系，两步MR量化了免疫细胞通过血清代谢物介导的淋巴瘤效应的比例。MR-Egger回归和留一法敏感性检验评估了我们研究结果的稳定性和可靠性。
该研究确定了与六种淋巴瘤变异因果相关的特定血清代谢物和免疫细胞类型。血清代谢物被确定为免疫细胞与淋巴瘤之间关系的中介。两步孟德尔随机化证实了这种介导的因果关系，敏感性分析支持结果的可靠性和不存在多效性。
该研究建立了免疫细胞与淋巴瘤之间的因果联系，部分由血清代谢物介导，尽管大部分影响仍不明确。未来的研究应探索其他潜在的中介因素。临床上，应更加关注淋巴瘤患者的免疫细胞生物标志物。这些结果为识别淋巴瘤生物标志物和潜在治疗靶点提供了有价值的见解。
