A glycolysis-related gene signatures in diffuse large B-Cell lymphoma predicts prognosis and tumor immune microenvironment - Cui, Yingying; Leng, Changsen (Frontiers in Cell and Developmental Biology, 2023)
Background: Diffuse large B-cell lymphoma (DLBCL), the most common type of lymphoma, is aggressive and highly heterogeneous. Glycolysis plays important role in tumor development and the tumor microenvironment (TME). In this study, we aimed to establish a glycolysis-related prognostic model for the risk stratification, prognosis prediction, and immune landscape evaluation in patients with DLBCL. Methods: Three independent datasets GSE181063, GSE10846, and GSE53786 containing gene expression profiles and clinical data were downloaded from the Gene Expression Omnibus (GEO) database. The glycolysis-related prognostic model was developed with Cox and Least Absolute Shrinkage and Selector Operation (LASSO) regression and validated. The nomogram integrating clinical factors and glycolytic risk scores was established. ESTIMATE algorithm and single-sample gene set enrichment analysis (ssGSEA) were applied to analyze the composition of the TME. Results: A glycolytic risk model containing eight genes (ADH1B, ALDH2, ANGPTL4, BPGM, CTH, NUP98, PAM, and PLOD2) was developed. The area under the receiver operating characteristic (ROC) curve (AUC) values of 1-, 3-, and 5-year were 0.718, 0.695, and 0.688, respectively. Patients in the high-risk group exhibited significantly decreased immune scores, elevated tumor purity, and worse survival than those in the low-risk group. The nomogram constructed by glycolytic risk score, age, Eastern Cooperative Oncology Group performance status (ECOG-PS), use of rituximab, and cell of origin (COO) displayed a better predictive performance than International Prognostic Index (IPI) in DLBCL. The glycolytic risk score was negatively correlated with the infiltration of activated CD8 T cells, activated dendritic cells, natural killer cells, and macrophages and immune checkpoint molecules including PD-L2, CTLA4, TIM-3, TIGHT, and B7-H3. Conclusions: Our findings suggested that the glycolytic risk model was an accurate and stable predictor for the prognosis of patients with DLBCL and might unearth the possible explanation for the glycolysis-related poor prognosis.