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 poorer survival 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.

Automated summary

A novel metabolic-related signature was identified for predicting prognosis in stomach adenocarcinoma patients. Patients in the high-risk group had significantly poorer survival outcomes compared to those in the low-risk group, with the predictive model showing good value. Additionally, gene set enrichment analyses revealed enriched pathways that may help explain the underlying mechanisms.