Identification and Validation of a Prognostic Prediction Model in Diffuse Large B-Cell Lymphoma

Area of Science:

• Oncology
• Genetics
• Immunology

Background:

• Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous cancer with poor survival rates.
• Existing prognostic biomarkers are insufficient for accurate risk stratification.
• Novel biomarkers are crucial for optimizing DLBCL treatment strategies.

Purpose of the Study:

• To identify and validate a novel gene-based prognostic signature for DLBCL.
• To develop a predictive model for overall survival (OS) in DLBCL patients.
• To explore immune cell landscape differences between risk groups.

Main Methods:

• Gene screening using univariate Cox analysis on DLBCL datasets (GSE117556, GSE10846, GSE31312).
• Prognostic model development using LASSO and multivariate Cox regression.
• Validation in independent cohorts (TCGA-NCICCR and clinical cohort).
• Immune cell infiltration analysis using CIBERSORT and ssGSEA.

Main Results:

• An eight-gene prognostic signature accurately predicted DLBCL patient outcomes across training, testing, and validation sets.
• The signature demonstrated strong predictive performance, with significant differences in OS between high- and low-risk groups.
• A nomogram was developed for visualizing the prognostic model, and immune cell enrichment was observed in the low-risk group.

Conclusions:

• The eight-gene signature serves as a reliable prognostic tool for DLBCL.
• The developed nomogram effectively visualizes survival predictions for DLBCL patients.
• The study highlights the role of immune alterations in DLBCL prognosis.