The Prediction of diffuse large B-cell lymphoma by a Novel Glycolysis-Related Gene Signature

Abstract

Abstract Objectives Globally, diffuse large B-cell lymphoma (DLBCL) accounts for approximately 30–40% of all cases of non-Hodgkin's lymphoma. There is often rapid enlargement of a single or multiple external nodes or nodules in patients. glycolysis is the process by which glucose transporters on the cell membrane transport glucose into the cell to split into two molecules of pyruvate. The large amounts of lactic acid produced by glycolysis are secreted extracellular to create an acidic microenvironment. This change leads to the remodeling of the cell matrix, which is essential for tumor cell proliferation. To date, only a few studies have attempted to determine whether glycolysis plays a prognostic role in DLBCL. Methods On DLBCL patients, we gathered RNA-seq data and clinical details from the TCGA and GEO databases. Based on glycolysis genes, we divided them into two clusters by consensus clustering method. The two discovered clusters were compared for survival, function, and tumor microenvironments (TME) using the K-M survival analysis, ESTIMATE, TIMER, and ssGSEA analysis. A variety of methods were used to elucidate the mechanisms involved, including GO, KEGG, GSVA, and GSEA. Utilizing the LASSO tool and multivariate Cox regression analysis, a predictive risk model for genes associated to glycolysis was formed, and its value was verified by calibration and ROC curve. Results As shown by the K-M survival curve and Tumor microenvironment analysis both clusters had significantly difference. the survival rate, Immune Score, Stromal Score and ESTIMATE Score of the C1 cluster was substantially higher than the C2 cluster. GO and KEGG analysis indicated that DEGs between the two clusters tended to be enriched in extracellular matrix and immune pathways. It was suggested by GSVA and GSEA analyses that glycolysis-associated genes (GRGs) expression perhaps connected to immunosuppression and poor prognosis in DLBCL patients. With DLBCL, factors such as risk models and clinical features can be combined to accurately predict a patient's prognosis. Conclusion Patients with DLBCL exhibit glycolysis-related gene expression that predicts their prognosis, as well as the tumor microenvironment.