Identification of a metabolic-related gene signature predicting the overall survival for patients with stomach adenocarcinoma

BACKGROUND: The reprogramming of energy metabolism and consistently altered metabolic genes are new features of cancer, and their prognostic roles remain to be further studied in stomach adenocarcinoma (STAD). METHODS: Messenger RNA (mRNA) expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) and the GSE84437 databases from the Gene Expression Omnibus (GEO) database. A univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression model established a novel metabolic signature based on TCGA. The area under the receiver operating characteristic (ROC) curve (AUROC) and a nomogram were calculated to assess the predictive accuracy. RESULTS: A novel metabolic-related signature (including acylphosphatase 1, RNA polymerase I subunit A, retinol dehydrogenase 12, 5-oxoprolinase, ATP-hydrolyzing, malic enzyme 1, nicotinamide N-methyltransferase, gamma-glutamyl transferase 5, deoxycytidine kinase, galactosidase alpha, DNA polymerase delta 3, glutathione S-transferase alpha 2, N-acyl sphingosine amidohydrolase 1, and N-acyl sphingosine amidohydrolase 1) was identified. In both TCGA and GSE84437, patients in the high-risk group showed significantly poorersurvival than the patients in the low-risk group. A good predictive value was shown by the AUROC and nomogram. Furthermore, gene set enrichment analyses (GSEAs) revealed several significantly enriched pathways, which may help in explaining the underlying mechanisms. CONCLUSIONS: A novel robust metabolic-related signature for STAD prognosis prediction was conducted. The signature may reflect the dysregulated metabolic microenvironment and can provided potential biomarkers for metabolic therapy in STAD.