Identification of key genes and biological pathways associated with vascular aging in diabetes based on bioinformatics and machine learning.

ABSTRACT: Vascular aging exacerbates diabetes-associated vascular damage, a major cause of microvascular and macrovascular complications. This study aimed to elucidate key genes and pathways underlying vascular aging in diabetes using integrated bioinformatics and machine learning approaches. Gene expression datasets related to vascular smooth muscle cell (VSMC) senescence and diabetic vascular aging were analyzed. Differential expression analysis identified 428 genes associated with VSMC senescence. Functional enrichment revealed their involvement in cellular senescence, ECM-receptor interaction, PI3K-Akt and AGE-RAGE signaling pathways. Further analysis of diabetic vascular aging datasets revealed 52 differentially expressed genes, enriched in AMPK signaling, AGE-RAGE signaling, cellular senescenc

SUBMITTER: Wang S 

PROVIDER: S-EPMC11210242 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature