Abstract
Background/Objectives: Diffuse large B cell lymphoma (DLBCL) is a highly heterogeneous and aggressive lymphoma with a high incidence rate. Although modern therapeutic approaches have significantly improved patient survival rates, treatment relapse and drug resistance remain major clinical challenges. Programmed cell death (PCD) promotes tumorigenesis and regulates the tumor microenvironment (TME) and drug sensitivity. Exploring the application potential of PCD in DLBCL could pave the way for new treatment strategies for this malignancy. Methods: We systematically analyzed 13 types of PCD pathways and integrated transcriptomic and clinical data from 832 DLBCL patients (GSE10846, GSE11318, and GSE87371). A PCD-based prognostic signature, termed the Programmed Cell Death Score (PCDS), was constructed using 20 key PCD-related genes. Its clinical relevance was evaluated through survival analysis, drug response profiling, and tumor immune infiltration assessment using CIBERSORT, ESTIMATE, and ssGSEA algorithms. Results: The PCDS robustly stratified patients by survival and outperformed conventional clinical indicators such as age, stage, Eastern Cooperative Oncology Group (ECOG), and lactate dehydrogenase (LDH) in prognostic prediction. High-PCDS tumors were associated with immune suppression, characterized by reduced CD8(+) T cell infiltration, elevated M2 macrophages, and increased programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) expression. Drug sensitivity analysis revealed that high-PCDS patients may benefit more from agents like sorafenib and fulvestrant, while low-PCDS patients responded better to NU7441. Functional validation using DLBCL cell lines and xenografts confirmed the oncogenic role of a representative gene (CTH) within the model. Conclusions: This study presents a novel prognostic scoring system derived from multiple PCD pathways that effectively stratifies DLBCL patients by risk and therapeutic responsiveness. Notably, the PCDS is closely associated with key immunological characteristics of the TME. These findings advance personalized treatment strategies and support clinically relevant decision-making in DLBCL.