A Novel Integrated Metabolism-Immunity Gene Expression Model Predicts the Prognosis of Lung Adenocarcinoma Patients

Background: Although multiple metabolic pathways are involved in the initiation, progression, and therapy of lung adenocarcinoma (LUAD), the tumor microenvironment (TME) for immune cell infiltration that is regulated by metabolic enzymes has not yet been characterized. Methods: 517 LUAD samples and 59 non-tumor samples were obtained from The Cancer Genome Atlas (TCGA) database as the training cohort. Kaplan-Meier analysis and Univariate Cox analysis were applied to screen the candidate metabolic enzymes for their role in relation to survival rate in LUAD patients. A prognostic metabolic enzyme signature, termed the metabolic gene risk score (MGRS), was established based on multivariate Cox proportional hazards regression analysis and was verified in an independent test cohort, GSE31210. In addition, we analyzed the immune cell infiltration characteristics in patients grouped by their Risk Score. Furthermore, the prognostic value of these four enzymes was verified in another independent cohort by immunohistochemistry and an optimized model of the metabolic-immune protein risk score (MIPRS) was constructed. Results: The MGRS model comprising 4 genes (TYMS, NME4, LDHA, and SMOX) was developed to classify patients into high-risk and low-risk groups. Patients with a high-risk score had a poor prognosis and exhibited activated carbon and nucleotide metabolism, both of which were associated with changes to TME immune cell infiltration characteristics. In addition, the optimized MIPRS model showed more accurate predictive power in prognosis of LUAD. Conclusion: Our study revealed an integrated metabolic enzyme signature as a reliable prognostic tool to accurately predict the prognosis of LUAD.