Integrated bioinformatic analysis reveals immune molecular markers and potential drugs for diabetic cardiomyopathy

Abstract

Diabetic cardiomyopathy (DCM) is a pathophysiological condition induced by diabetes mellitus that often causes heart failure (HF). However, their mechanistic relationships remain unclear. This study aimed to identify immune gene signatures and molecular mechanisms of DCM. Microarray data from the Gene Expression Omnibus (GEO) database from patients with DCM were subjected to weighted gene co-expression network analysis (WGCNA) identify co-expression modules. Core expression modules were intersected with the immune gene database. We analyzed and mapped protein-protein interaction (PPI) networks using the STRING database and MCODE and filtering out 17 hub genes using cytoHubba software. Finally, potential transcriptional regulatory factors and therapeutic drugs were identified and molecular docking between gene targets and small molecules was performed. We identified five potential immune biomarkers: proteosome subunit beta type-8 (PSMB8), nuclear factor kappa B1 (NFKB1), albumin (ALB), endothelin 1 (EDN1), and estrogen receptor 1 (ESR1). Their expression levels in animal models were consistent with the changes observed in the datasets. EDN1 showed significant differences in expression in both the dataset and the validation model by real-time quantitative PCR (qPCR) and Western blotting(WB). Subsequently, we confirmed that the potential transcription factors upstream of EDN1 were PRDM5 and KLF4, as its expression was positively correlated with the expression of the two transcription factors. To repurpose known therapeutic drugs, a connectivity map (CMap) database was retrieved, and nine candidate compounds were identified. Finally, molecular docking simulations of the proteins encoded by the five genes with small-molecule drugs were performed. Our data suggest that EDN1 may play a key role in the development of DCM and is a potential DCM biomarker.

Keywords: bioinformatics; biomarker; diabetes mellitus; diabetic cardiomyopathy; molecular docking; potential drugs.

Figure 1

Study workflow. DCM, Diabetic cardiomyopathy; GEO, Gene Expression Omnibus; GO, Gene Ontology; PPI, protein-protein interaction; cMAP, Connectivity Map; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, Gene Set Enrichment Analysis; TF, transcription factors; DEGs, differentially expressed genes; WGCNA, Weight gene co-expression network analysis; GEPIA, Gene Expression Profiling Interactive Analysis; QPCR, quantitative real-time PCR; WB, Western blotting.

Figure 2

Gene co-expression modules. (A) Analysis of network topology for soft-thresholding powers. (B) Hierarchical cluster dendrogram of DCM-related genes based on one dissimilarity measure. (C) Module-phenotype associations.

Figure 3

Immune infiltration analysis and intersection dataset. (A) Boxplot diagram of proportions of 22 types of immune cells. (B) Venn diagram of intersection of black modules and immune genes. * means p value <0.05; ** means p value <0.01; *** means p value <0.001.

Figure 4

Density plots of the dataset GSE4745, showing a smooth distribution of the points along the numeric axis. The peaks of the density plot are at the locations with the highest concentration of points. (A) density plot of sample dataset. (B–F) Density plots of hub genes.

Figure 5

DCM-related gene set enrichment analysis. (A) Cell component. (B) Molecular function. (C) KEGG pathway. (D) Reactome.

Figure 6

Gene expression and validation. (A) Dataset gene expression; (B) Murine DCM gene expression; (C) ROC for gene expression in dataset; (D) ROC curve for murine DCM gene expression.

Figure 7

Binding sites of transcription factors and expression correlation verification. (A) Binding site of Prdm5; (B) Binding site of Gata4; (C) Binding site of Klf4; (D) Correlation between Prdm5 and Edn1 gene expression in myocardial tissue; (E) Correlation between Gata4 and Edn1 gene expression in myocardial tissue; (F) Correlation between Klf4 and Edn1 gene expression in myocardial tissue.

Figure 8

Protein level expression of hub genes. Ns means p value >0.05;* means p value <0.05; ** means p value <0.01; *** means p value <0.001.

Figure 9

Molecular docking simulations. (A) EDN1 with telomerase inhibitor IX. (B) EDN1 with isoliquiritigenin. (C) EDN1 with medrysone. (D) EDN1 with benzoic acid. (E) EDN1 with oxymetholone. (F) EDN1 with sulforaphane. (G) EDN1 with epoxycholesterol. (H) 2-chloro-6-(1-piperazinyl)pyrazine.

Figure 10

ROC curves of the virtual filter for (A) isoliquiritigenin; (B) medrysone; (C) benzoic acid; (D) oxymetholone; (E) sulforaphane; (F) pevonedistat; (G) 2-chloro-6-(1-piperazinyl)pyrazine.