Integrative Multi-Omics and Machine Learning Reveal Shared Biomarkers in Type 2 Diabetes and Atherosclerosis.

ABSTRACT: Atherosclerosis (AS) is a leading cause of death and disability in type 2 diabetes mellitus (T2DM). However, the shared molecular mechanisms linking T2DM and atherosclerosis have not been fully elucidated. We analyzed AS- and T2DM-related gene expression profiles from the Gene Expression Omnibus (GEO) database to identify overlapping differentially expressed genes and co-expression signatures. Functional enrichment (Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG)) and protein-protein interaction (PPI) network analyses were then used to describe the pathways and interaction modules associated with these shared signatures, We next applied the cytoHubba algorithm together with several machine learning methods to prioritize hub genes and evaluate their diagnostic potential an

SUBMITTER: Wu Q 

PROVIDER: S-EPMC12786049 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature