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.

The empirical likelihood ratio test (ELRT) statistic is constructed for testing the homogeneity of several nonparametric populations in the presence of some auxiliary information. It is shown—under some regularity conditions and under the null hypothesis that all distribution functions of the populations are equal—that the asymptotic distribution of the ELRT is a chi-squared distribution. The proposed ELRT could be more powerful than the Kruskal–Wallis test, as extra information can be efficiently employed by ELRT. The advantage of ELRT over T&P (2006) is that researchers do not need to select approximately normal statistics for inter-group comparisons, and ELRT is more suitable for the multi-population consistency test with a small sample size.