Molecular clustering and prognostic features based on integrated databases predict survival and immune status in patients with gastric cancer.

ABSTRACT:

Background

Gastric cancer (GC) remains one of the most common malignancies worldwide with high mortality rates despite advances in treatment approaches. Patients frequently develop drug resistance to current therapies, highlighting the critical need for novel prognostic biomarkers that can enhance survival rates and guide immunotherapy decisions in patients with GC.

Methods

We conducted a comprehensive bioinformatics analysis using integrated clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. GC cases were categorized into two prognostic-related gene (PRG) clusters, and differentially expressed genes were identified. We established a prognostic model based on 11 key genes, stratified patients into high-risk and low-risk groups, and

SUBMITTER: Shi Y 

PROVIDER: S-EPMC12444767 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature