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 binary prediction model for the core genes revealed that the expression level of BRCA1 was closely and significantly correlated with the survival time of GC patients, suggesting that BRCA1 may be considered a valuable biomarker for GC diagnosis. Furthermore, MR analysis revealed that BRCA1 promotes the transformation of normal cells into GC cells by regulating NADPH levels, leading to a continuous increase in oxidative stress. This is one of the initial comprehensive analysis involving MR and multidimensional approaches; it revealed the significant role of BRCA1 in GC, providing new ideas on drugs and targets for GC clinical treatment.