Exploring the disease impact of plasma metabolites on marginal zone lymphoma based on mendelian randomization

Abstract Background: It is reported that altered plasma metabolite levels are associated with disease progression in marginal zone lymphoma (MZL). However, the relationship has not been identified due to interference from factors. Therefore, we aimed to explore the profound relationship between plasma metabolites and genes of MZL patients. Methods: This study used two-sample Mendelian Randomization (MR), with exposure data derived from genome-wide association studies (GWAS) of 1,400 plasma metabolites, while outcome data for MZL patients were obtained from FinnGen database. The inverse variance weighted (IVW) method was used as the primary analysis, supplemented by the MR-Egger and weighted median methods. Heterogeneity testing, pleiotropy analysis, and sensitivity analyses were further conducted. We selected instrumental variables of plasma metabolites directly causally associated with MZL patients for SNP annotation to identify the genes in which these genetic variants reside. The above genes were further analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) analysis in conjunction with the Gene Expression Omnibus (GEO) database. In addition, we also analyzed the differential expression of the above genes in MZL patients and normal humans and performed single-sample genome enrichment (ssGSEA) analysis on them. Results: Our study found that 5-methylthioadenosine (mta) levels (OR=0.31, 95%CI: 0.18-0.54, p<0.001), Succinylcarnitine levels (OR=0.45, 95%CI: 0.29-0.70, p<0.001), and Methionine to methionine sulfoxide ratio (OR=0.33, 95%CI: 0.18-0.58, p<0.001) may be protective plasma metabolites of MZL patients. X-21471 levels (OR=1.86, 95% CI: 1.33-2.59, p<0.001) may be a possible risk plasma metabolite for MZL patients. The sensitivity analysis of all causal relationships does not show significant heterogeneity, level pleiotropy. Instrumental variables with statistically significant plasma metabolites were screened for SNPs and 71 patient genes potentially associated with the disease were identified in MZL patients. The KEGG enrichment analysis is mainly associated with propanoate metabolism, bile secretion, and ABC transporters. The GO analysis is mainly associated with sulfur compound metabolic process, basolateral plasma membrane, and xenobiotic transmembrane transporter activity. 8 statistically significant differentially expression genes were found in MZL patients and normal subjects. The ssGSEA enrichment analysis was mostly associated with plasmacytoid dendritic cell and central memory CD4+ T cell. Conclusions: This study identified 3 plasma metabolites as protective factors for MZL and 1 plasma metabolite as a risk factor for MZL. The combination of genomics and metabolomics provides a new perspective to explore disease progression in MZL. Further studies are necessary to detect the potential mechanisms by which patient metabolite genes develop in the disease.