Expression of N6-Methyladenosine (m6A) Regulators Correlates With Immune Microenvironment Infiltration and Predicts Prognosis in Diffuse Large Cell Lymphoma (DLBCL)

Background: N6-methyladenosine (m6A) and immune microenvironment infiltration have been widely reported to play important roles in various cancers. However, in diffuse large cell lymphoma (DLBCL), the clinical significance of m6A regulators and their relationship with immune microenvironment infiltration have not been illuminated.Methods: The expression of m6A regulators in DLBCL was investigated using The Cancer Genome Atlas and Genotype-Tissue Expression databases. The capacity of m6A regulators in dividing molecular clusters of DLBCL was determined using Consensus Clustering algorithm and validated via principal component analysis. The clinical traits and prognosis difference in m6A-sorted clusters were revealed. The m6A prognostic signature was established and validated based on Gene Expression Omnibus dataset using univariate Cox and LASSO regression analysis. The immune cell infiltration and the expression of immune checkpoint genes in m6A low/high-risk DLBCL were studied. Gene set enrichment analysis (GSEA) was adopted to unveil the underlying molecular mechanism in m6A low/high-risk DLBCL.Results: Differentially expressed m6A regulators were able to molecularly discriminate DLBCL as two clusters based on consensus clustering and principal component analysis. A six m6A regulators-based risk prediction signature was established and validated as an independent predictor, which separated patients into m6A low- and high-risk groups. High-risk m6A indicates worse survival, for which the predictive AUC at 1-, 2-, and 5-year achieved 0.605, 0.640, and 0.652, respectively. Immune cell infiltration analysis revealed the B cells naïve and T cells gamma delta were the top increased and decreased immune cells in high-risk m6A patients. Up-regulated (PDCD1 and KIR3DL1) and down-regulated (TIGIT, DO1, and BTLA) immune checkpoint genes in high-risk m6A patients were identified. GSEA analysis unveiled high-risk m6A related to tumor proliferation associated process, while low-risk m6A related to defense response associated process. Conclusions: This study provided a comprehensive analysis for the clinical significance of m6A regulators and their association with immune microenvironment infiltration. An m6A regulators-based risk signature may be applied for the risk stratification of DLBCL patients, thus may facilitate the clinical management of DLBCL. Immune microenvironment was found to be closely related to m6A risk, which may be part of the mechanism of m6A regulators in DLBCL.