Prognostic model based on M2 macrophage-related signatures for predicting outcomes, enhancing risk stratification, and providing therapeutic insights in diffuse large B-cell lymphoma

Abstract

Purpose: The tumor microenvironment (TME) in lymphoma is influenced by M2 macrophages. This research proposes an novel predictive model that leverages M2 macrophage-associated genes to categorize risk, forecast outcomes, and evaluate the immune profile in patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) undergoing R-CHOP therapy. Methods: Gene expression data and clinical information from DLBCL patients were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Co-expressed genes linked to M2 macrophage in DLBCL were analyzed using CIBERSORT. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to explore associated signaling pathways. The M2 macrophage-related gene prognostic model was developed and validated using Cox and LASSO regression. Prognostic signature genes were verified by single-cell RNA-seq analysis. Results: 92 M2 macrophage-related genes were identified based on bulk-seq data. MS4A4A, CCL13, LTB, CCL23, CCL18, XKR4, IL22RA2, and FOLR2 were used to construct the risk model. AUC values for 1-, 3-, and 5-year survival were 0.74, 0.72, and 0.72, respectively. High-risk patients demonstrated elevated immune scores and poorer overall survival. The high-risk subgroup also exhibited greater sensitivity to both chemotherapeutic agents and immune checkpoint inhibitors. Conclusion: This study presents an accurate and reliable M2 macrophage-related risk model, enhancing understanding of distinct prognostic subsets in DLBCL. It offers potential novel drug options for future treatments.

Keywords

• DLBCL
• Immune
• M2 macrophage
• Prognostic model
• Tumor microenvironment