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Updated: Jul 1, 2026
Neutrophil Isolation and Analysis to Determine their Role in Lymphoma Cell Sensitivity to Therapeutic Agents
Published on: March 25, 2016
Risk Model Based On Neutrophil-Related Genes Constructs to Assess Prognosis and Immune Landscape in Diffuse Large
Xinfang Gao1, Xinguo Luo1, Hongwei Ye1
1Department of Hematology.
Objectives:
Diffuse large B-cell lymphoma (DLBCL), the most common type of non-Hodgkin lymphoma, represents a highly heterogeneous cancer. Neutrophils, as the core effector cells of intrinsic immunity, play an important role in regulating the tumor microenvironment (TME) due to their functional complexity. This study aimed to assess the prognostic significance of neutrophil-related genes (NRGs) in DLBCL and their association with the TME.
Methods:
Transcriptomic data and clinical information of DLBCL patients were retrieved from TCGA and GEO databases. Characterized genes were screened by LASSO, random forest, and XGBoost algorithm. A prognostic model was constructed by multivariate Cox regression analysis, and the predictive efficacy of the accuracy of the model was assessed through receiver operating characteristic (ROC) curves and Kaplan-Meier (K-M) survival analysis. Subsequently, immune cell infiltration, gene enrichment, tumor mutation burden (TMB), and drug sensitivity were analyzed across different risk groups. Finally, consensus clustering was used to identify molecular subtypes of DLBCL, and immune cell activity and immune function differences among these subtypes were compared through immune infiltration analysis.
Results:
A risk stratification model established based on NRGs (TGFB2, LAMA4, GGH, F5, CD163, RasGRP4, ANXA2, S100A4, and PTEN) significantly differentiated the survival prognosis of patients in the high and low-risk groups. The low-risk group was found to have elevated immunoreactivity and a higher ESTIMATE composite score, according to immune infiltration analysis. Enrichment analysis revealed that the high risk exhibited upregulation of cell cycle regulation, DNA repair and chromosome dynamics pathways, while the low risk group exhibited extracellular matrix remodeling and activation of cytokine signaling pathways.
Conclusions:
The NRG-based risk model can effectively predict the survival outcomes and immune profiles of DLBCL patients, offering a novel perspective on the link between NRGs and DLBCL.
