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. 2024 Jul 3;14(1):15324.
doi: 10.1038/s41598-024-65773-z.

Identification of hub genes associated with diabetic cardiomyopathy using integrated bioinformatics analysis

Hailong Cui #  1   2 , Die Hu #  3 , Jing Xu  3 , Shuiying Zhao  1 , Yi Song  1   2 , Guijun Qin  4 , Yanling Liu  5
Affiliations

Identification of hub genes associated with diabetic cardiomyopathy using integrated bioinformatics analysis

Hailong Cui et al. Sci Rep. .

Abstract

Diabetic cardiomyopathy (DCM) is a common cardiovascular complication of diabetes, which may threaten the quality of life and shorten life expectancy in the diabetic population. However, the molecular mechanisms underlying the diabetes cardiomyopathy are not fully elucidated. We analyzed two datasets from Gene Expression Omnibus (GEO). Differentially expressed and weighted gene correlation network analysis (WGCNA) was used to screen key genes and molecules. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) network analysis were constructed to identify hub genes. The diagnostic value of the hub gene was evaluated using the receiver operating characteristic (ROC). Quantitative real-time PCR (RT-qPCR) was used to validate the hub genes. A total of 13 differentially co-expressed modules were selected by WGCNA and differential expression analysis. KEGG and GO analysis showed these DEGs were mainly enriched in lipid metabolism and myocardial hypertrophy pathway, cytomembrane, and mitochondrion. As a result, six genes were identified as hub genes. Finally, five genes (Pdk4, Lipe, Serpine1, Igf1r, and Bcl2l1) were found significantly changed in both the validation dataset and experimental mice with DCM. In conclusion, the present study identified five genes that may help provide novel targets for diagnosing and treating DCM.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Flow diagram of the preparation, processing, analysis, and validation dates.
Figure 2
Figure 2
Datasets merging. (A,B) The UMAP and box plots before and after the removal of the inter-batch effect.
Figure 3
Figure 3
Identification of modules associated with diabetic cardiomyopathy using the weighted gene co-expression network analysis (WGCNA). (A) Analysis of the scale-free index for various soft-threshold powers (β). (B) Analysis of the mean connectivity for various soft-threshold powers. (C) Dendrogram of all differentially expressed genes clustered based on the measurement of dissimilarity (1-TOM). The color band shows the results obtained from the automatic single-block analysis. (D) Eigengene dendrogram and eigengene adjacency plot. (E) Heatmap of the correlation between the module eigengenes and clinical traits of diabetes. We selected the ME turquoise-grade block for subsequent analysis. TOM topological overlap matrix, ME module eigengene.
Figure 4
Figure 4
Differentially expressed genes (DEGs) in the merged datasets. (A) Volcano plot. (B) Heatmap plot of top 100 DEGs. (C) The Venn diagram of genes from the DEGs and turquoise module.
Figure 5
Figure 5
Functional annotation of the 89 overlapping genes. (A) Biological processes (BP). (B) Cell composition (CC). (C) Molecular function (MF). (D) KEGG analysis.
Figure 6
Figure 6
PPI network and identification of hub genes. (A) PPI network of the genes between DEG lists and turquoise module constructed by STRING. The nodes represent the genes. Edges indicate interaction associations between nodes. (B) Identification of 6 candidates for hub genes by four algorithms. (C) PPI network of the hub genes. PPI, protein–protein interaction; DEG, differentially expressed genes; STRING, Search Tool for the Retrieval of Interacting Genes.
Figure 7
Figure 7
Establishment of diabetic cardiomyopathy mouse model and validation of hub genes and ROC analysis. Schematic illustration of diabetic cardiomyopathy mouse model. (B,C) Body weight, and random blood glucose, at 7 weeks after STZ injection (n = 6 each). (D) ROC curve analysis of Pdk4, Igf1r, Lipe, Serpine1 and Bcl2l1 in the merged dataset. (E) Gene expression in a mouse model of DCM and Con. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05. (F) ROC curve analysis of Pdk4, Igf1r, Lipe, Serpine1 and Bcl2l1 in the validating dataset GSE5606. (G) ROC curve analysis of Pdk4, Igf1r, Lipe, Serpine1 and Bcl2l1 in the validating dataset GSE36875.
Figure 8
Figure 8
Relationship analysis of the five potential hub genes with cardiovascular and diseases in CTD. Five potential hub genes (Pdk4, Igf1r, Lipe, Serpine1 and Bcl2l1.) targeted multiple cardiovascular diseases, including (A) cardiomyopathies, (B) cardiovascular disease, (C) heart disease, (D) ventricular dysfunction.
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