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 senescence, and VEGF signaling pathways. Machine learning algorithms, including LASSO regression and SVM-RFE, pinpointed six key genes: TFB1M, FOXRED2, LY75, DALRD3, PI4K2B, and NDOR1. Immune cell infiltration analysis demonstrated correlations between diabetic vascular aging, the identified key genes, and infiltration levels of plasma cells, M1 macrophages, CD8+ T cells, eosinophils, and regulatory T cells. In conclusion, this study identified six pivotal genes (TFB1M, FOXRED2, LY75, DALRD3, PI4K2B, and NDOR1) closely associated with diabetic vascular aging through integrative bioinformatics and machine learning approaches. These genes are linked to alterations in the immune microenvironment during diabetic vascular aging. This study provides a reference and basis for molecular mechanism research, biomarker mining, and diagnosis and treatment evaluation of diabetes-related vascular aging.