Identification of a novel prognostic model for gastric cancer utilizing glutamine-related genes.

ABSTRACT:

Background

Glutamine metabolism presents a promising avenue for cancer prevention and treatment, but the underlying mechanisms in gastric cancer (GC) progression remain elusive.

Methods

The TCGA-STAD and GEO GSE62254 datasets, containing gene expression, clinical information, and survival outcomes of GC, were meticulously examined. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to excavate a key module (MEturquoise), which was used to intersect with glutamine metabolism-related genes (GMRGs) and differentially expressed genes (DEGs) to identify differentially expressed GMRGs (DE-GMRGs). LASSO and Cox Univariate analyses were implemented to determine risk model genes. Correlation of the risk model with clinical paramete

SUBMITTER: Li W 

PROVIDER: S-EPMC11462029 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature