The predictive value of necroptosis-related lncRNAs in prostate cancer prognosis and their potential to distinguish between cold and hot tumors

Background: Prostate cancer (PCa) is frequently associated with poor prognosis, and immunotherapy has shown limited efficacy. This study aimed to identify novel necroptosis-related long non-coding RNAs (lncRNAs) that could predict patient outcomes and guide personalized treatment. Methods: Transcriptomic data from The Cancer Genome Atlas (TCGA) were analyzed using co-expression analysis and univariate Cox regression to identify lncRNAs associated with PCa progression. A necroptosis-related lncRNA prognostic model was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) and validated via Kaplan-Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, Cox regression, and calibration plots. Functional analyses included Gene set enrichment analysis (GSEA), principal component analysis (PCA), immune profiling, and half-maximal inhibitory concentration (IC50) predictions to explore therapeutic implications. Results: . The two inferred risk groups demonstrated distinct immune characteristics: hot tumors (Cluster 2) exhibited higher infiltration of activated immune cells, increased immune checkpoint expression, and greater predicted sensitivity to immunotherapy, whereas cold tumors showed immunosuppressive infiltration patterns and lower checkpoint levels. These features allowed the model to robustly distinguish cold from hot tumor phenotypes. Conclusion: Necroptosis-related lncRNAs, particularly NR2F1-AS1, may serve as prognostic biomarkers and inform immune-based stratification, supporting more precise personalized treatment strategies for PCa.