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ABSTRACT: Background
Gastric cancer (GC) is one of the leading causes of cancer-related mortality worldwide, with limited therapeutic options and a poor prognosis, particularly in advanced stages. Glycometabolism, a hallmark of cancer, plays a critical role in tumor progression, immune evasion, and response to therapy. However, the specific roles of glycometabolism-related genes and their prognostic and therapeutic implications in GC remain inadequately understood.Methods
Transcriptomic and clinical data from GC patients were retrieved from TCGA and GEO databases. Glycometabolism-related genes were identified and analyzed using machine learning algorithms to construct a prognostic model. Functional assays, immune profiling, and pathway enrichment analyses were performed to explore th
SUBMITTER: Ye W
PROVIDER: S-EPMC11998276 | biostudies-literature | 2025 Apr
REPOSITORIES: biostudies-literature