Machine learning‐driven investigation on liquid–liquid phase separation‐related prognostic signature in diffuse large B‐cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive non-Hodgkin lymphoma and is characterized by substantial heterogeneity. This study aimed to develop a liquid-liquid phase separation (LLPS)-related prognostic model to improve risk stratification. Transcriptomic and clinical data from four cohorts (n = 768) were analysed. Multiple machine learning algorithms were applied to identify prognostic LLPS-related genes (LRGs) and construct a 6-LRG model. Model performance was assessed using survival analysis, time-dependent receiver operating characteristic curves and multivariable modelling. Additional analyses were conducted to explore potential biological and microenvironmental differences between risk groups. The 6-LRG model stratified patients into groups with significantly different overall survival across datasets, with 1-year area under curve (AUCs) ranging from 0.661 to 0.820, 3-year AUCs from 0.683 to 0.779 and 5-year AUCs from 0.711 to 0.807. The 6-LRG model remained independent of established clinical variables and improved risk prediction when integrated into a nomogram. Distinct biological and immune characteristics were observed between groups. The 6-LRG model may provide additional prognostic information in DLBCL and generates hypotheses regarding underlying biological mechanisms. However, prospective validation in larger populations is essential before any implementation.