Development and validation of a prognosis prediction model for overall survival in correlation between butyrate metabolism and gastric cancer prognosis: Mendelian randomization and transcriptomics analysis.

Abstract

Background: Gastric cancer (GC) remains a leading cause of cancer-related mortality due to its late diagnosis and poor prognosis. Butyrate metabolism (BM) has demonstrated significant roles in tumor biology, but its prognostic implications in GC remain unexplored. We aimed to investigate the effect of butyrate metabolic biomarkers on the prognosis of GC.

Methods: We acquired datasets from The Cancer Genome Atlas and Gene Expression Omnibus. Differential BM-related genes (BMGs) were identified using weighted gene co-expression network analysis (WGCNA). Patients were stratified into subtypes, and a prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) regression. Mendelian randomization (MR) analysis was conducted using genetic variants as instrumental variables to establish causal links between BM and GC prognosis.

Results: Our model demonstrated robust prognostic accuracy with an area under the receiver operating characteristic (ROC) curve of 0.716. Transcriptomic analysis identified two key BMGs, SMC2 and HSPB1, with significant implications for GC survival. However, MR analysis provided no evidence of a causal association between BM and GC.

Conclusions: We identified two butyrate metabolic prognostic genes, namely, structural maintenance of chromosome 2 and heat shock protein beta-1, as the prognostic markers for GC. Furthermore, MR indicated no causal association between the butyrate metabolic pathway and GC.