Prediction of Prognosis, Efficacy of Lung Adenocarcinoma by Machine Learning Model Based on Immune and Metabolic Related Genes

Abstract Background The aim of this study is to integrate immune and metabolism-related genes in order to construct a predictive model for predicting the prognosis and treatment response of LUAD(lung adenocarcinoma) patients, aiming to address the challenges posed by this highly lethal and heterogeneous disease. Material and Methods Using TCGA-LUAD as the training subset, differential gene expression analysis, batch survival analysis, Lasso regression analysis, univariate and multivariate Cox regression analysis were performed to construct prognostic related gene models. GEO queue as validation subsets, is used to validate build RiskScore. Then, we explore the RiskScore and mutation status, immune cell infiltration, the relationship between immune therapy and chemotherapy, and build the model of the nomogram. Results The RiskScore has been determined to be composed of seven gene. In the high-risk group defined by this score, both early-stage and advanced-stage LUAD patients exhibit a decreased overall survival rate. The mutation status of patients as well as immune cell infiltration show associations with the RiskScore value obtained from these genes' expression levels. Furthermore, there exist variations in response to immunotherapy as well as sensitivity to commonly used chemotherapy drugs among different individuals. Lastly, when using a column line plot model based on the calculated RiskScore values, we obtain a concordance index (C-index) was 0 .716 (95% CI: 0.671–0.762), and time-dependent ROC predicted probabilities of 1-, 3- and 5-year survival for LUAD patients were 0.752、0.725 and 0.654, respectively. Conclusion In summary, by combining immune- and metabolism-related genes, we successfully con-structed a novel model for predicting prognosis and treatment response in LUAD patients.