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

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.


Enrichment analysis of overlapping DEGs
To analyze the biological functions and pathways involved in 299 common DEGs, GO and KEGG pathway enrichment analyses were performed.After screening with the threshold of an adjusted p value < 0.05, we select the top four significantly enriched GO terms and the top four KEGG terms.In terms of biological processes, DEGs were principally associated with response to xenobiotic stimulus, positive regulation of angiogenesis, response to hypoxia and cellular response to extracellular stimulus.The analysis of cell component indicated that DEGs significantly enriched in extracellular region, extracellular space, extracellular matrix and Z disc.The results of molecular function showed that DEGs were mainly enriched in calcium ion binding, heparin binding, growth factor activity and steroid hormone receptor activity (Fig. 2F, Supplementary Table S2).KEGG pathway analysis of DEGs showed that the top four significant pathways were enriched in HIF-1 signaling pathway, PPAR signaling pathway, glycolysis/gluconeogenesis and steroid biosynthesis (Fig. 2G, Supplementary Table S3).

The expression of hub genes
In order to identify the genes necessary for the co-occurrence of DCM and DFU, we verified the expression of the hub genes of DCM (GSE 197,850, Fig. 4A) and DFU (GSE 134,431, Fig. 4B) from GEO database.According to our results, compared to the control group, three hub genes were significantly enhanced in the DCM and DFU samples: PPARG, CD4 and SCARB1.At the same time, three hub genes were significantly weakened: JUN, SLC2A1 and SERPINE1.

Functional enrichment analysis of proteins interacting with hub genes
In order to clarify the biological functions of each hub gene, functional enrichment analysis of proteins interacting with 6 hub genes was performed.PPARG and its interacting proteins were significantly enriched in the regulation of lipid metabolic biological process (Fig. 5A).JUN-interacting proteins were significantly enriched in cellular response to metal ion biological process and inflammatory response signaling pathway such as IL-17 and human T-cell leukemia pathways (Fig. 5B).SLC2A1-interacting proteins were significantly enriched in response to hypoxia biological process and HIF-1 signaling pathway (Fig. 5C).CD4 and its interacting proteins  www.nature.com/scientificreports/were widely involved in various T cell functions to influence immunity (Fig. 5D).SCARB1-interacting proteins were significantly enriched in lipid metabolism biological process and inflammatory response signaling pathway (Fig. 5E).SERPINE1-interacting proteins were significantly enriched in negative regulation of blood coagulation and wound healing biological process (Fig. 5F).Therefore, these hub genes were widely involved in lipid metabolic, response to metal ion, inflammatory response, hypoxia, blood coagulation and wound healing biological process and related signaling pathways.

Differential expression analysis of immune cell infiltration
Inflammation plays an important role in the occurrence and progression of DCM and DFU 16,17 .To assess the extent of inflammation, we employed the CIBERSORT algorithm to estimate the degree of immune cell infiltration in patients diagnosed with DCM and DFU.The relative abundance analysis of different immune cell subsets in DCM showed that T cells were the main subsets of infiltrating immune cells (Fig. 6A).In DFU, T cell subsets and macrophage subsets are the main subsets of infiltrating immune cells (Fig. 6B).In the DCM T cells cohort, SLC2A1 and CD4 were significantly negatively correlated with CD8 T cells, while SERPINE1 was significantly positively correlated.There was a significant negative correlation between JUN and SCARB1 in CD4 naive T cells.In CD4 memory activated T cells, JUN, SCARB1 and SERPINE1 were significantly positively correlated, while CD4 was significantly negatively correlated.JUN and SCARB1 were significantly positively correlated with T cells regulatory (Tregs, Fig. 6C).In the DFU T cells cohort, CD4 and SCARB1 were positively correlated with CD8 T cells.PPARG was positively correlated with CD4 memory resting T cells, but negatively correlated with T cells follicular helper.SCARB1 and SERPINE1 were positively correlated with Tregs.In the DFU macrophage cohort, CD4 was positively correlated with M1-type macrophages (Fig. 6D).
In DCM and DFU, correlation coefficient analysis revealed a significant correlation between the hub genes and the level of immune cell infiltration (Fig. 6C,D).This showed that DCM and DFU were associated with immune infiltration.

Construct transcription factor (TF)-gene and miRNA regulatory network
Next, we used Networkanalyst to predict the association between TF-genes, miRNAs and hub genes.We combined the TF-Knock v2.0 database with the miRTarBase screening subnetwork to validate our predicted TF-genes + miRNAs network.We screened the potential transcription factors of hub genes "PPARG", "JUN", "SLC2A1", "CD4", "SCARB1" and "SERPINE1" from the TF-Knock database.In the miRTarBase database, we screened potential miRNAs that were experimentally confirmed by western blot, clip-seq, micro-array, etc.Our network reveals 38 nodes, 106 edges, and 5 seed genes (Fig. 7A).Transcription factors SP7, REST, miR-30a and miR-181d may play key regulatory roles in the regulation of hub genes.

Identification of candidate drugs and targeted chemical interactions in DCM and DFU
Chemical-protein interaction networks are important research tools for understanding protein function and advancing drug discovery.The hub genes of DCM and DFU were used to identify drug candidates based on DSigDB database enrichment.The top 10 drug molecules were selected based on their P-values as potential drug candidates.The 10 drug candidates included Fenofibrate, Efavirenz, Gemfibrozil, Alitretinoin, Einecs 250-892-2, Phencyclidine, Nicotinic acid, PD 98,059, Simvastatin and 17-Ethynyl estradiol (Fig. 8A).We summarized the drug candidates (top 10) adjusted P-value by gene-drug interaction enrichment analysis in Supplementary Table S4.These drug candidates reacted with the hub gene, indicating that they might be suitable for treating both diseases.
Of all the drug candidates, fenofibrate had the highest combined score (Fig. 8A).In addition, molecular docking predicted the binding of fenofibrate to hub genes: PPARG, JUN, SLC2A1, CD4, SCARB1 and SERPINE1.Molecular docking results are shown in Fig. 8B-G.Interestingly, fenofibrate had low stabilization energy at its binding sites with six target proteins and formed stable structures.The binding energy, number and location of hydrogen bond formation of fenofibrate and 6 hub genes were evaluated by AutoDock calculation, as shown in Supplementary Table S5.Therefore, fenofibrate might have the potential to treat DCM combined with DFU.

Discussion
In our study, through the screening of DEGs and the construction of PPI network, we found 6 hub genes that may have significant differences in the pathogenesis and development of DCM and DFU: PPARG, JUN, SLC2A1, CD4, SCARB1 and SERPINE1.
PPARG encodes PPAR-γ, a member of the nuclear receptor peroxisome proliferator activating receptor (PPAR) subfamily, which is a regulator of adipocyte differentiation.PPARG directs cardiac energy metabolism in cardiomyocytes, thereby affecting pathological heart failure and diabetic cardiomyopathy 18 .PPARG activation effectively improved the foot withdrawal threshold, protected the sciatic nerve myelin structure, and improved the intraepidermal nerve fiber density of foot skin 19 .These studies show that PPARG has a regulatory role in both DCM and DFU, which is consistent with the results predicted by our biological analysis.
JUN is a protein that is highly similar to a viral protein and interacts directly with specific target DNA sequences to regulate gene expression.Although rarely reported in DCM and DFU.However, our analysis results showed that JUN was significantly correlated with DCM and DFU.This may be related to JUN's regulation of inflammation and lipid metabolism 20,21 .SLC2A1 promotes glucose transporters, which are responsible for constitutive or basal glucose uptake 22,23 .It has a very broad substrate specificity and can transport a variety of aldoses, including pentose and hexose 24 .Our results showed that SCL2A1 was significantly down-regulated in both DCM and DFU patients.It may be a key therapeutic target for DCM combined with DFU.
CD4 encodes the CD4 membrane glycoprotein of T lymphocytes, which plays a crucial role in the immune response 25 .Previous studies have shown that T lymphocyte depletion improves cardiac fibrosis in streptozotocin induced diabetic cardiomyopathy 26 .CD4 + T cells affect DFU healing by regulating FOXP3, providing a new direction for future treatment of DFU 27 .We found that CD4 was significantly elevated in DCM and DFU, and CD4 was found to promote T cell activation through functional enrichment.The regulation of T cell activation by CD4 may have important significance for the treatment of DCM combined with DFU.The protein encoded by SCARB1 is a plasma membrane receptor for high-density lipoprotein cholesterol (HDL) 28 .SCARB1 mediates the transfer between cholesterol and high-density lipoprotein plays an important role in lipid homeostasis in type 2 diabetes 28 .And genetic variants of SCARB1 can lead to severe early-onset coronary artery disease 29 .In our study, SCARB1, as a hub gene, was significantly increased in both DCM and DFU.This suggests that SCARB1 may be involved in diabetes-related complications by regulating lipid homeostasis.
SERPINE1 encodes plasminogen activator inhibitor-1 (PAI-1), a downstream target of heart-transforming growth factor β, which is associated with cardiac fibrosis 30 .Studies showed that a hyperosmotic, autosomal recessive cardiac fibrosis phenotype was found in young adults with homozygous migration variants of SER-PINE1, suggesting that cardiac homeostasis requires an optimal range of levels of PAI-1 30 .PAI-1 is elevated in obese people with type 2 diabetes and may contribute to increased cardiovascular disease risk independently of traditional factors 31 .We screened SERPINE1 as a key target gene that was significantly elevated in both DCM and DFU.These results suggest that SERPINE1 may be involved in the fibrotic process that leads to multiple complications of diabetes.
We used GSEA to reveal 6 identified central genes widely associated with adaptive immune responses.Patients with DCM and DFU may benefit from anti-inflammatory therapy in the microenvironment 32,33 .However, these genes were not work together on the same immune cell type.Therefore, interventions targeting single-cell immune responses may not be effective against DCM combined with DFU.We also used Networkanalyst to predict the association between TF-genes, miRNAs and hub genes.Transcription factors SP7, REST, miR-30a and miR-181d may be the key regulatory targets of hub gene, providing a new perspective for the treatment of DCM patients with DFU.
In recent years, the combined analysis of metabolomics, lipidomics and proteomics has been widely used in the analysis of potential drug targets [34][35][36] .Sodium-glucose cotransporter 2 (SGLT 2) inhibitors are the gold standard in the treatment of type 2 diabetes.Among them, empagliflozin (EMPA) has been shown to have a beneficial effect on heart failure 34 .Multi-omics analysis showed that EMPA could regulate and partially restore the levels of multiple metabolites related to stress in cardiomyocytes under high glucose environment, alleviating lipid toxicity 34 .Enhanced diabetic cardiac fatty acid (FA) metabolism and perturbations in the biosynthesis of unsaturated fatty acid and arachidonic acid metabolism are potential drivers of the cardiovascular benefits of EMPA 35 .Animal experiments have also demonstrated that fatty acid oxidation is one of the targets of EMPA therapy in db/db mouse hearts 36 .These results suggest that improving lipid metabolism may be an effective treatment strategy to alleviate multiple complications of diabetes.Fenofibrate is primarily used as a lipid-lowering drug to reduce cholesterol levels in patients at risk of cardiovascular disease 37 .Fenofibrate reduces low density lipoprotein (LDL), very low-density lipoprotein (VLDL) and www.nature.com/scientificreports/triglyceride (TG) levels, and increases high density lipoprotein (HDL) levels 38 .In people with high triglyceride levels, fenofibrate plus statin treatment was associated with lower all-cause death and cardiovascular disease 37 .In a multi-country randomized controlled trial involving 9795 people aged 50-75 years with type 2 diabetes, fenofibrate reduced total cardiovascular events 39 .Fenofibrate rescues diabetes-associated ischemia-mediated angiogenesis impairment by mercaptopurine independent regulation of thioredoxin interactions 40 .Our study shows that fenofibrate reacts with hub genes.Molecular docking also showed that fenofibrate and 6 hub genes could form hydrogen bonds to form stable structures.This suggests that fenofibrate may be a potential therapeutic agent for advanced DCM combined with DFU.Recent experimental studies have demonstrated that fenofibrate, as a peroxisome proliferator-activated receptor (PPAR) agonist, protects corneal nerves against diabetic eye disease (DR) 41 .Fenofibrate up-regulated Nrf2 inhibits diabetes-related ferroptosis and delays the progression of diabetic nephropathy (DN) 42 .These results suggest that fenofibrate may have a broad therapeutic effect in patients with multiple complications of diabetes.
Our review of the published literature showed that there was limited research on common therapeutic targets and drugs between DCM and DFU, especially bioinformatic analyses.Here, we screened overlapping DEGs, hub genes, and TFs of DCM and DFU to elucidate the common targets of DCM and DFU.However, our study has some limitations.First, our work needs to be further verified by experiments in vivo and in vitro.Secondly, the function of hub genes needs further experimental exploration, which is the focus of our upcoming research.
In summary, our study offers compelling evidence suggesting that specific hub genes may mediate the common pathogenesis of DCM and DFU.The identification of six hub genes-PPARG, JUN, SLC2A1, CD4, SCARB1, and SERPINE1-highlights their potential involvement in the pathophysiology of these conditions.Furthermore, our findings indicate that fenofibrate, known for its lipid-lowering effects, may hold therapeutic promise for patients with DCM and concurrent DFU.Our findings provided a possible direction to explore common therapeutic targets and drugs for DCM and DFU.Despite the need for further validation and exploration, these findings offer valuable insights that could potentially pave the way for the development of novel treatment strategies targeting both conditions simultaneously.

Dataset download
Use the search term "diabetic cardiomyopathy" and "diabetic foot ulcer", we screened the whole Gene Expression Omnibus (GEO) database (http:// www.ncbi.nlm.nih.gov/ geo/) 43 .The following data sets were incorporated into the analysis: First, the archival information should include case and control groups.Second, the genome was sequenced.Third, these datasets must provide raw data that can be further analyzed.Based on the GPL11154 Illumina HiSeq 2000 (Homo sapiens) platform and the GPL18573 Illumina NextSeq 500 (Homo sapiens) platform collected two gene datasets (GSE 197,850, GSE 134,431) for DCM, DFU and control group from the GEO database.The GSE 197,850 dataset included gene expression profiles from 4 DCM samples and 4 normal controls.The GSE 134,431 dataset included 12 DFU patients and 9 controls.

Screening of differentially expressed genes (DEGs)
Using GEO 2R online analytical tools (https:// www.ncbi.nlm.nih.gov/ geo/ geo2r/) extraction and analysis of DEGs respectively, the tool is GEO database contained in the web application based on R language.The p values were corrected by Benjamini and Hochberg method to reduce the false positive rate.The threshold of DEGs screening was |log 2 FC|≥ 1 and adjust p < 0.05.Use the R packages "complexheatmap" and "ggplot 2" to visualize the DEG obtained from the two datasets to generate heat maps and volcano maps, respectively.In addition, the overlapping DEGs between DCM and DFU were delineated using the Venn diagrams with the Venn online platform (http:// bioin forma tics.psb.ugent.be/ webto ols/ Venn/).These overlapping DEGs are used for subsequent analysis.

GO and KEGG analyses of DEGs
The above overlapping DEGs were analyzed for Gene ontology (GO) functional enrichment, consisting of biological processes (BP), cell components (CC), and molecular functions (MF), as well as Kyoto encyclopedia of genes and genomes (KEGG) signaling pathway enrichment using the R package "clusterProfiler" 44 .Significance was considered when the adjusted p < 0.05 [45][46][47] .

Protein-protein interaction establishment and identification of hub genes
To further explore the interactions between the common genes obtained above, a PPI network was constructed using the search tool for interacting genes (STRING) (http:// string-db.org/) 48 .A minimum interaction score of above 0.4 was considered to be significant.The PPI network was then visualized using Cytoscape software.Then, we use the Cytoscape plugin Minimal Common Oncology Data Elements (MCODE, http:// apps.cytos cape.org/ apps/mcode) to select key protein expression.Then, four algorithms were applied to screen the hub genes with high connectivity in PPI network.

Verification of hub gene expression
We confirmed the transcriptional levels of the above hub genes in GSE197850 (DCM) and GSE134431 (DFU).We used t-test for inter-group assessment P < 0.05.

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 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 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).