OBJECTIVE: To investigate the prognostic value of a model based on ferroptosis-related long non-coding RNAs (lncRNAs) for patients with diffuse large B-cell lymphoma (DLBCL). METHODS: Ferroptosis-related lncRNA expression matrices and corresponding clinical information for DLBCL patients were downloaded from the GEO public database (datasets GSE10846 and GSE11318). A prognostic risk model was constructed using the GSE10846 dataset through the following steps: identification of ferroptosis-related genes, Pearson correlation analysis with lncRNAs, Kaplan-Meier survival analysis, univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis. The accuracy of the model was evaluated using Receiver Operating Characteristic (ROC) curves. The model was subsequently validated using the GSE11318 dataset. Further systematic assessments of the model's biological significance and clinical translational potential were conducted via immune infiltration analysis and drug sensitivity prediction. RESULTS: A prognostic model comprising 9 lncRNAs was established. Time-dependent ROC curve analysis showed that the Area Under the Curve (AUC) values for 3-year and 5-year survival predictions were both greater than 0.75. Consistent results were obtained in the validation set, indicating good model robustness. Immune microenvironment analysis revealed that the high-risk group exhibited an immunosuppressive "cold tumor" phenotype, characterized by a significant reduction in the infiltration of effector immune cells such as cytotoxic cells and CD8⁺ T cells (all P < 0.001). Drug sensitivity analysis indicated that the high-risk group was more sensitive to PI3K/mTOR inhibitors (e.g., Dactolisib) but showed resistance to certain histone deacetylase inhibitors. Enrichment analysis suggested that these lncRNAs might influence DLBCL progression by regulating pathways such as protein degradation and RNA processing. CONCLUSION: The prognostic model constructed based on ferroptosis-related lncRNAs demonstrates considerable reliability. It not only effectively predicts patient survival but also reflects tumor immune status and drug response characteristics, showing potential as an auxiliary tool for prognosis assessment and personalized treatment in DLBCL.
Wang et al. (Tue,) studied this question.