Identification of Potential Therapeutic Drugs for Diabetic Cardiomyopathy

Abstract

This study focused on identifying potential therapeutic drugs and mechanisms of action for diabetic cardiomyopathy (DCM). Using gene expression profiles from the GSE197850 dataset, we applied Weighted Correlation Network Analysis, Limma, and Gene Set Variation Analysis (GSVA) to uncover DCM-related gene sets and pathways. Subsequently, we conducted protein interaction network analysis with String and identified 10 hub genes through Cytoscape: ACTN2, ITGA1, CASP3, PXN, PCNA, CAV1, GAPDH, FEN1, PTPN11, and ESR1. In vitro validation using Rat H9C2 cardiomyocytes showed upregulation of FEN1, PCNA, PTPN11, CAV1, GAPDH, CASP3, PXN, and ACTN2, and downregulation of ESR1 and ITGA11 in high-glucose conditions. We further performed immune infiltration analysis with CIBERSORT and explored potential therapeutic agents through molecular docking with Autodock Vina. Our findings identified estradiol, valproic acid, acetaminophen, and resveratrol as potential drugs for DCM. Among these, resveratrol showed promise by promoting autophagy. This study leveraged comprehensive bioinformatic and experimental methods to pinpoint DCM-related genes, elucidate key hub genes, and propose resveratrol as a latent drug for DCM.