Sphingolipid metabolism-related genes for the diagnosis of metabolic syndrome by integrated bioinformatics analysis and Mendelian randomization identification.

ABSTRACT:

Background

The rising global incidence of metabolic syndrome (MetS) highlights the need for more effective diagnostic and therapeutic tools. Sphingolipid metabolites are crucial in MetS pathogenesis, and identifying related biomarkers could improve treatment strategies.

Methods

Differentially expressed genes (DEGs) were extracted from the GSE181646 dataset and compared with sphingolipid metabolism-related genes (SMRGs) to identify differentially expressed SMRGs (DE-SMRGs). Key module genes were obtained via Weighted Gene Co-expression Network Analysis (WGCNA). Machine learning and receiver operating characteristic (ROC) curve validation were used to screen biomarkers, followed by Gene Set Enrichment Analysis (GSEA) and immune cell infiltration analysis. Mendelian randomizati

SUBMITTER: Li W 

PROVIDER: S-EPMC12178068 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature