Prognostic Implication of a Metabolism-Associated Gene Signature in Lung Adenocarcinoma

Lung cancer is the most common cancer worldwide, leading to high mortality each year. Metabolic pathways play a vital role in the initiation and progression of lung cancer. We aimed to establish a prognostic prediction model for lung adenocarcinoma (LUAD) patients based on a metabolism-associated gene (MTG) signature. Differentially expressed (DE)-MTGs were screened from The Cancer Genome Atlas (TCGA) LUAD cohorts. Univariate Cox regression analysis was performed on these DE-MTGs to identify genes significantly correlated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression was performed on the resulting genes to establish an optimal risk model. Survival analysis was used to assess the prognostic ability of the model. The prognostic value of the gene signature was further validated in independent Gene Expression Omnibus (GEO) datasets. A gene signature with 13 metabolic genes was identified as an independent prognostic factor. Kaplan-Meier survival analysis demonstrated the good performance of the risk model in both TCGA training and GEO validation cohorts. Finally, a nomogram incorporating clinical parameters and the metabolic gene signature was constructed to help individualize outcome predictions. The calibration curves showed excellent agreement between the actual and predicted survival. Lung cancer is the most common cancer worldwide, leading to high mortality each year. Metabolic pathways play a vital role in the initiation and progression of lung cancer. We aimed to establish a prognostic prediction model for lung adenocarcinoma (LUAD) patients based on a metabolism-associated gene (MTG) signature. Differentially expressed (DE)-MTGs were screened from The Cancer Genome Atlas (TCGA) LUAD cohorts. Univariate Cox regression analysis was performed on these DE-MTGs to identify genes significantly correlated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression was performed on the resulting genes to establish an optimal risk model. Survival analysis was used to assess the prognostic ability of the model. The prognostic value of the gene signature was further validated in independent Gene Expression Omnibus (GEO) datasets. A gene signature with 13 metabolic genes was identified as an independent prognostic factor. Kaplan-Meier survival analysis demonstrated the good performance of the risk model in both TCGA training and GEO validation cohorts. Finally, a nomogram incorporating clinical parameters and the metabolic gene signature was constructed to help individualize outcome predictions. The calibration curves showed excellent agreement between the actual and predicted survival.