Background: Lung adenocarcinoma is the most common fatal disease and has a poor prognosis. Pyroptosis regulates tumour cell proliferation, invasion, and metastasis, thereby affecting the prognosis of cancer patients. However, the role of pyroptosis-related lncRNAs in Lung adenocarcinoma remains unclear. The study seeks to identify potential biomarkers to predict prognosis and provide precision medication to improve conditions.
Methods: Firstly, this study searched lung adenocarcinoma transcriptome data from The Cancer Genome Atlas and pyroptosis-related genes from GeneCards. Pyroptosis-related prognostic lncRNAs were identified by coexpression analysis and univariate Cox regression. Then, we constructed a prognostic model of pyroptosisrelated lncRNAs in the training set using least absolute shrinkage, selection operator penalty Cox regression analysis, and multivariate Cox regression analysis. Finally, Kaplan–Meier analysis, time-dependent receiver operating characteristics, univariate Cox regression, multivariate Cox regression, nomograms, calibration curves, and clinical grouping were performed to validate and assess the model. lncRNA enrichment analysis, principal component analysis , immune analysis, and prediction of the half-maximal inhibitory concentration in the risk group were also analysed.
Results: We constructed a model containing 6 pyroptosis-related lncRNAs. In the model, we found good agreement between the calibration plots and the prognosis prediction. The 1-year, 3-year, and 5-year overall survival of the area under the ROC curve were 0.725, 0.705, and 0.717, respectively. Because the IC50 differs significantly between risk groups, risk groups could be used as a guide for treatment. The results of this study demonstrated that pyroptosis-related lncRNAs can predict prognosis to improve the treatment of individuals with Lung adenocarcinoma.