Exploration of the clinicopathological and prognostic significance of BRCA1 in gastric cancer.

ABSTRACT: Gastric cancer (GC) is one of the most common malignancies and is a highly heterogeneous disease; it is also a leading cause of cancer-related death. Owing to the complexity and late-stage diagnosis of GC, the prognosis remains poor. To explore potential biomarkers for GC, GC patient transcriptome data were subjected to a comprehensive approach involving machine learning, binary nomogram prediction model construction, the topological algorithm of CytoHubba, and Kaplan-Meier and Mendelian randomization (MR) analyses. First, gene expression data for normal and GC tissues were assessed via machine learning and the topological algorithm of CytoHubba, and a total of 792 differentially expressed genes (DEGs) and nine core genes were identified. Kaplan-Meier analysis and analysis of a nomogram bi

SUBMITTER: Zhang H 

PROVIDER: S-EPMC11933547 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature