RESUMEN

BACKGROUND: Lung cancer is a frequent malignancy with a poor prognosis. Extensive metabolic alterations are involved in carcinogenesis and could, therefore, serve as a reliable prognostic phenotype. AIMS: Our study aimed to develop a prognosis signature and explore the relationship between metabolic characteristic-related signature and immune infiltration in lung adenocarcinoma (LUAD). OBJECTIVE: TCGA-LUAD and GSE31210 datasets were used as a training set and a validation set, respectively. METHOD: A total of 513 LUAD samples collected from The Cancer Genome Atlas database (TCGA-LUAD) were used as a training dataset. Molecular subtypes were classified by consensus clustering, and prognostic genes related to metabolism were analyzed based on Differentially Expressed Genes (DEGs), Protein-Protein Interaction (PPI) network, the univariate/multivariate- and Lasso- Cox regression analysis. RESULTS: Two molecular subtypes with significant survival differences were divided by the metabolism gene sets. The DEGs between the two subtypes were identified by integrated analysis and then used to develop an 8-gene signature (TTK, TOP2A, KIF15, DLGAP5, PLK1, PTTG1, ECT2, and ANLN) for predicting LUAD prognosis. Overexpression of the 8 genes was significantly correlated with worse prognostic outcomes. RiskScore was an independent factor that could divide LUAD patients into low- and high-risk groups. Specifically, high-risk patients had poorer prognoses and higher immune escape. The Receiver Operating Characteristic (ROC) curve showed strong performance of the RiskScore model in estimating 1-, 3- and 5-year survival in both training and validation sets. Finally, an optimized nomogram model was developed and contributed the most to the prognostic prediction in LUAD. CONCLUSION: The current model could help effectively identify high-risk patients and suggest the most effective drug and treatment candidates for patients with LUAD.