Development and validation of a pyroptosis -related prognostic model for predicting the prognosis in Lung Adenocarcinoma

Abstract Background Pyroptosis is an inflammatory response associated with programmed death ，and its role has been reported in multiple studies of malignant tumors. However, research on pyroptosis-related genes in LUAD and their relationship with prognosis are still unclear. Therefore, in this study, we tried to establish an apoptosis-related prognostic model and evaluated its prognostic role in LUAD.Methods We collected data on LUAD patients in the TCGA database. Differentially expressed genes (DEGs) were recognized and paired by applying univariate Cox regression analysis. Based on DEGs, a prognostic risk score model was tried to constructed and validated based on TCGA and GEO database (GSE26939). By setting a median risk score, patients are divided into high- and low-risk groups. After implementing the machine learning screening methods. Model prediction ability, pathway enrichment analysis, Gene Set Enrichment Analysis, immune cell infiltrate, and drug susceptibility were analyzed in some aspects between two groups (high- or low-risk groups).Results A predictive model with six pyroptosis-related genes (NLRC4, NLRP1, NOD1, PRKACA, IL1Aand RELA) was constructed. Using this model, patient's risk score was assessed the, after verification, this risk score was an independent and sensitive predictive signature model. Analysis including Functional analyses, immune cell infiltrate, drug susceptibility showed promising clinical prediction value in LUAD. Conclusion: This prognostic model can accurately and effectively predict the prognosis of lung adenocarcinoma patients and the response to drug therapy.