Dataset Information

Drug metabolism-related eight-gene signature can predict the prognosis of gastric adenocarcinoma.

ABSTRACT:

Background

Metabolic abnormalities in patients with gastric adenocarcinoma lead to drug resistance and poor prognosis. Therefore, this study aimed to explore biomarkers that can predict the prognostic risk of gastric adenocarcinoma by analyzing drug metabolism-related genes.

Methods

The RNA-seq and clinical information on gastric adenocarcinoma were downloaded from the UCSC and gene expression omnibus databases. Univariate and least absolute shrinkage and selection operator regression analyses were used to identify the prognostic gene signature of gastric adenocarcinoma. The relationships between gastric adenocarcinoma prognostic risk and tumor microenvironment were assessed using CIBERSORT, EPIC, QUANTISEQ, MCPCounter, xCell, and TIMER algorithms. The potential drugs that could target the gene signatures were predicted in WebGestalt, and molecular docking analysis verified their binding stabilities.

Results

Combined with clinical information, an eight-gene signature, including GPX3, ABCA1, NNMT, NOS3, SLCO4A1, ADH4, DHRS7, and TAP1, was identified from the drug metabolism-related gene set. Based on their expressions, risk scores were calculated, and patients were divided into high- and low-risk groups, which had significant differences in survival status and immune infiltrations. Risk group was also identified as an independent prognostic factor of gastric adenocarcinoma, and the established prognostic and nomogram models exhibited excellent capacities for predicting prognosis. Finally, miconazole and niacin were predicted as potential therapeutic drugs for gastric adenocarcinoma that bond stably with NOS3 and NNMT through hydrogen interactions.

Conclusions

This study proposed a drug metabolism-related eight-gene signature as a potential biomarker to predict the gastric adenocarcinoma prognosis risks.

SUBMITTER: Yin HM

PROVIDER: S-EPMC8649372 | biostudies-literature |

REPOSITORIES: biostudies-literature

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