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. 2024 Jan 2;14(1):230.
doi: 10.1038/s41598-023-50954-z.

Exploring shared therapeutic targets in diabetic cardiomyopathy and diabetic foot ulcers through bioinformatics analysis

Hanlin Wu  1   2 , Zheming Yang  2 , Jing Wang  2 , Yuxin Bu  2 , Yani Wang  2 , Kai Xu  2 , Jing Li  2 , Chenghui Yan  2 , Dan Liu  3 , Yaling Han  4
Affiliations

Exploring shared therapeutic targets in diabetic cardiomyopathy and diabetic foot ulcers through bioinformatics analysis

Hanlin Wu et al. Sci Rep. .

Abstract

Advanced diabetic cardiomyopathy (DCM) patients are often accompanied by severe peripheral artery disease. For patients with DCM combined with diabetic foot ulcer (DFU), there are currently no good therapeutic targets and drugs. Here, we investigated the underlying network of molecular actions associated with the occurrence of these two complications. The datasets were downloaded from the Gene Expression Omnibus (GEO) database. We performed enrichment and protein-protein interaction analyses, and screened for hub genes. Construct transcription factors (TFs) and microRNAs regulatory networks for validated hub genes. Finally, drug prediction and molecular docking verification were performed. We identified 299 common differentially expressed genes (DEGs), many of which were involved in inflammation and lipid metabolism. 6 DEGs were identified as hub genes (PPARG, JUN, SLC2A1, CD4, SCARB1 and SERPINE1). These 6 hub genes were associated with inflammation and immune response. We identified 31 common TFs and 2 key miRNAs closely related to hub genes. Interestingly, our study suggested that fenofibrate, a lipid-lowering medication, holds promise as a potential treatment for DCM combined with DFU due to its stable binding to the identified hub genes. Here, we revealed a network involves a common target for DCM and DFU. Understanding these networks and hub genes is pivotal for advancing our comprehension of the multifaceted complications of diabetes and facilitating the development of future therapeutic interventions.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Research design flow chart.
Figure 2
Figure 2
Differentially expressed genes (DEGs) and functional enrichment analysis in DCM and DFU datasets. (A,B) Volcano plot of DEGs in GSE197850 and GSE134431 datasets. Red color indicated up-regulated genes and blue color indicated downregulated genes. (C) Venn diagram of overlapping DEGs among two GEO datasets. (D,E) Heat maps of overlapping DEGs in GSE197850 and GSE134431 datasets. (F) Gene ontology (GO) enrichment analysis of 299 overlapping DEGs. (G) Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis of 299 overlapping DEGs.
Figure 3
Figure 3
Identification of Hub genes in overlapping DEGs among two GEO datasets. (A) Protein–protein interaction of the overlapping DEGs. (B–E) The closeness ranking method, the maximal neighborhood component (MNC) ranking method, the edge percolated component (EPC) ranking method, and the betweenness ranking method for hub genes identification. (F) Venn diagram for identifying hub genes among different ranking methods.
Figure 4
Figure 4
The expression of hub genes in two GEO datasets. (A) The mRNA expression of hub genes in CSE197850 datasets. (B) The mRNA expression of hub genes in CSE134431 datasets. Data are presented as mean ± SEM.
Figure 5
Figure 5
Functional enrichment analysis of proteins interacting with six genes. (A) Protein interaction network of PPARG. BP and KEGG enrichment analysis of protein interaction network of PPARG. (B) Protein interaction network of JUN. BP and KEGG enrichment analysis of protein interaction network of JUN. (C) Protein interaction network of SLC2A1. BP and KEGG enrichment analysis of protein interaction network of SLC2A1. (D) Protein interaction network of CD4. BP and KEGG enrichment analysis of protein interaction network of CD4. (E) Protein interaction network of SCARB1. BP and KEGG enrichment analysis of protein interaction network of SCARB1. (F) Protein interaction network of SERPINE1. BP and KEGG enrichment analysis of protein interaction network of SERPINE1.
Figure 6
Figure 6
DCM and DFU are associated with immune infiltration. (A) The relative abundance of distinct immune cells subsets in DCM. (B) The relative abundance of distinct immune cells subsets in DFU. (C) The correlation between immune cell infiltration and the expression of PPARG, JUN, SLC2A1, CD4, SCARB1 and SERPINE1 in DCM cohort. (D) The correlation between immune cell infiltration and the expression of PPARG, JUN, SLC2A1, CD4, SCARB1 and SERPINE1 in DFU cohort.
Figure 7
Figure 7
Construct transcription factor (TF)-gene and miRNA regulatory network. (A) TFs and microRNAs regulatory networks for validated hub genes. Red: hub genes, green: TF genes, wathet blue: key TF genes, blue: miRNAs.
Figure 8
Figure 8
Identification of candidate drugs and molecular docking patterns. (A) The combined score of interactions between known molecules and hub genes in the DSigDB database. (B-G) Molecular docking mode of fenofibrate and PPARG (B), JUN (C), SLC2A1 (D), CD4 (E), SCARB1 (F) and SERPINE1 (G).
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