A novel defined necroptosis-related lncRNAs signature for predicting the prognosis of non-small cell lung cancer

Abstract Background: Faced with the current poor prognosis of non-small cell lung cancer, this topic attempts to find new potential prognostic biomarkers to improve the situation.Materials and Methods: To form the synthetic matrix, we searched GTEX and The Cancer Genome Atlas for NSCLC transcriptomic data.The necroptosis-related prognostic factor LncRNAs were identified by co-expression analysis and univariate COX regression analysis, and the necroptosis-related LncRNA model was constructed using the least absolute contraction and selection operator (LASSO).Models were then validated using Kaplan-Meier analysis, time-dependent receiver operating characteristics (ROC), univariate COX (uni-COX) regression, multivariate COX (MULTI-COX) regression, nomograms and calibration curves and evaluation.Gene set enrichment analysis (GSEA) was performed in high-risk groups.Results: We constructed a model containing 4 lncRNAs associated with necroptosis.In the model, we found that the calibration map was in good agreement with prognosis prediction.The 1-, 2-, and 3-year survival rates were 0.663, 0.623, and 0.595, respectively.In conclusion, the results of this project support that necroptosis-related lncRNAs can predict prognosis and help to improve individualized treatment of non-small cell lung cancer.Conclusion: Necroptosis-related lncRNAs could help to suggest developable therapeutic strategies that would greatly enhance the level of individualized therapy and improv patient outcomes.