[Construction of a methionine metabolism-related prognostic signature and the effect of targeting methionine metabolism on diffuse large B-cell lymphoma].

Abstract

Objective: To construct a prognostic risk model based on methionine metabolism-related genes in diffuse large B-cell lymphoma (DLBCL), investigate its prognostic significance, and clarify the regulatory role of abnormal methionine metabolism in DLBCL progression. Methods: Clinical prognostic information and methionine metabolism-related gene expression data of patients with DLBCL were obtained from the GEO dataset GSE10846. Lasso regression and Cox regression analyses were performed to screen core prognostic genes and establish a risk model, which was further validated in GSE11318 and GSE181063 datasets. The potential biological functions were analyzed by using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Set Enrichment Analysis. The DLBCL cell lines U2932 and SUDHL-4 were cultured in vitro and divided into the normal culture group (control group), methionine deprivation group, methionine deprivation + methionine supplementation group (methionine supplementation group), and methionine deprivation + S-adenosylmethionine (SAM) supplementation group (SAM supplementation group). Intracellular concentrations of methionine and SAM were detected by liquid chromatography-mass spectrometry. Cell proliferation and apoptosis were measured by using CCK-8 assay and flow cytometry, respectively. A U2932 cell-derived xenograft model was established in NOD-SCID mice, which were assigned to the control diet group (n=5) and methionine-restricted diet group (n=5), and tumor growth was monitored dynamically. Results: Eight core genes were screened from 33 methionine metabolism-related genes to construct the prognostic risk model. Kaplan-Meier analysis in the training set revealed that overall survival was significantly shorter in the high-risk group than in the low-risk group (HR=2.50, 95% CI: 1.84-3.39, P<0.001). Stable prognostic performance was confirmed in two validation sets: GSE11318 (HR=1.89, 95% CI: 1.30-2.75, P<0.001) and GSE181063 (HR=1.39, 95% CI: 1.20-1.62, P<0.001). In vitro, compared with the normal culture group, the methionine deprivation group showed significantly decreased intracellular levels of methionine and SAM (both P<0.001), completely inhibited cell proliferation, and increased apoptosis (both P<0.001); compared with the methionine deprivation group, the methionine supplementation group and SAM supplementation group exhibited significantly restored apoptosis (all P value <0.001). In vivo, methionine restriction evidently reduced tumor volume [ (438.3±179.1) mm(3) vs (1 451.0±464.9) mm(3), P=0.002] and tumor weight [ (0.3±0.1) g vs (1.1±0.4) g, P=0.004] compared with the control diet. Conclusion: The prognostic risk model based on methionine metabolism-related genes shows potential prognostic value for DLBCL. Abnormal methionine metabolism regulates the proliferation and apoptosis of DLBCL cells, providing experimental evidence for the intervention targeting methionine metabolism in DLBCL.