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Journal of Diabetes Research logoLink to Journal of Diabetes Research
. 2021 Feb 5;2021:8891093. doi: 10.1155/2021/8891093

Based on Network Pharmacology Tools to Investigate the Molecular Mechanism of Cordyceps sinensis on the Treatment of Diabetic Nephropathy

Yan Li 1, Lei Wang 2, Bojun Xu 1, Liangbin Zhao 1, Li Li 1, Keyang Xu 3, Anqi Tang 1, Shasha Zhou 1, Lu Song 1, Xiao Zhang 1, Huakui Zhan 1,✉
PMCID: PMC7884116  PMID: 33628839

Abstract

Background

Diabetic nephropathy (DN) is one of the most common complications of diabetes mellitus and is a major cause of end-stage kidney disease. Cordyceps sinensis (Cordyceps, Dong Chong Xia Cao) is a widely applied ingredient for treating patients with DN in China, while the molecular mechanisms remain unclear. This study is aimed at revealing the therapeutic mechanisms of Cordyceps in DN by undertaking a network pharmacology analysis.

Materials and Methods

In this study, active ingredients and associated target proteins of Cordyceps sinensis were obtained via Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and Swiss Target Prediction platform, then reconfirmed by using PubChem databases. The collection of DN-related target genes was based on DisGeNET and GeneCards databases. A DN-Cordyceps common target interaction network was carried out via the STRING database, and the results were integrated and visualized by utilizing Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to determine the molecular mechanisms and therapeutic effects of Cordyceps on the treatment of DN.

Results

Seven active ingredients were screened from Cordyceps, 293 putative target genes were identified, and 85 overlapping targets matched with DN were considered potential therapeutic targets, such as TNF, MAPK1, EGFR, ACE, and CASP3. The results of GO and KEGG analyses revealed that hub targets mainly participated in the AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, PI3K-Akt signaling pathway, and IL-17 signaling pathway. These targets were correlated with inflammatory response, apoptosis, oxidative stress, insulin resistance, and other biological processes.

Conclusions

Our study showed that Cordyceps is characterized as multicomponent, multitarget, and multichannel. Cordyceps may play a crucial role in the treatment of DN by targeting TNF, MAPK1, EGFR, ACE, and CASP3 signaling and involved in the inflammatory response, apoptosis, oxidative stress, and insulin resistance.

1. Introduction

Diabetic nephropathy (DN) is featured as hyperglycemia, hyperfiltration, proteinuria, and progressive renal function decline, which can cause end-stage kidney disease [1]. It accounts for about 40% of chronic kidney disease worldwide and is undoubtedly a medical challenge worldwide [2], in terms of high incidence, multifactorial pathogenesis, and the absence of practical methods in the diagnosis and treatment [3]. Traditional Chinese medicine (TCM) guided by the unique theory provides an effective treatment of complex chronic diseases via multicomponent, multitarget, and multipathway [4]. In recent years, Chinese herb medicine has been commonly utilized to alleviate and reverse diabetes and its complications, such as DN in clinical practice and scientific researches, which has been considered a beneficial supplement of the drug therapy for DN [4]. Cordyceps, a traditional Chinese herbal medicine, is reported to have multiple health-promoting characteristics, including anti-inflammatory, anticancer, antidiabetic, analgesic, antioxidant, antiallergic, and antiobesity [5]. Also, Cordyceps has been reported to have broad pharmacological effects on DN by inhibiting the epithelial-mesenchymal transition, alleviating oxidative stress, repressing inflammation, modulating gut microbiota dysbiosis, and activating autophagy [6–9]. However, the pharmacological mechanisms of Cordyceps associated with DN only focus on a single chemical molecule. Accordingly, a comprehensive and systematic evaluation of the molecular mechanisms of Cordyceps on DN is indispensable.

This study is aimed at analyzing the pharmacological mechanisms of active ingredients of Cordyceps involved in the progression of DN via using the network pharmacology databases and biological analysis methods. It laid a stable foundation for further research on exploring pharmacological mechanisms of Cordyceps in treating DN. The study framework is showed in Figure 1.

Figure 1.

Figure 1

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A flow chart based on an integration strategy of network pharmacology.

2. Materials and Methods

2.1. Establishment of Active Ingredients and Correlated Target Database

TCMSP (http://lsp.nwu.edu.cn/tcmsp.php), a systematic pharmacological platform that contains the relationships among herbal compounds, related targets, and diseases [10], was applied to identify the chemical constituents of Cordyceps. The active components were selected based on oral bioavailability (OB) and drug likeness (DL) values, and the ingredients were captured when the OB was ≥30% and the DL ≥ 0.18 (a screening threshold of TCMSP database) [10]. The chemical formulas of components were reconfirmed by PubChem (https://www.ncbi.nlm.nih.gov/pccompound) to double-check the final compounds of Cordyceps [11]. The targets associated with Cordyceps components were further identified based on the TCMSP database and Swiss Target Prediction (http://www.swisstargetprediction.ch.), a webserver to accurately identify the targets of active molecules [12]. The genes corresponding to the protein targets were obtained from the UniProt database.

2.2. Network Construction of Active Components-Potential Targets

A comprehensive network was constructed via using Cytoscape software to reflect the intricate relationship between active compounds and putative targets [13]. Nodes represent the components and targets, while edges reveal the interactions between components and targets (Figure 2).

Figure 2.

Figure 2

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Compound-target network of putative targets and active Cordyceps ingredients. The green nodes represent the active components of Cordyceps, and nodes in red represent the corresponding targets of the ingredients.

2.3. Selection of Potential DN Targets

The keyword “diabetic nephropathy” was inputted in the GeneCards (https://www.genecards.org/), a human gene compendium with information about genomics, proteomics, and transcriptomics [14], and DisGeNET (https://www.disgenet.org/home/), a comprehensive platform including one of the largest publicly accessible collections of genes, to search for DN-associated targets [15].

2.4. Screening Compound-Disease Overlapping Targets

The screened compound targets and disease targets were imported into Funrich, a software used mainly for functional enrichment and interaction network analysis of genes and proteins for analysis [16]. The common targets of compound-disease were obtained as the potential targets for further analysis (Figure 3).

Figure 3.

Figure 3

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Overlapping target genes between DN and Cordyceps.

2.5. Network Construction of Compound-Disease Common Targets

A protein-protein interaction (PPI) network was obtained based on the STRING platform (https://string-db.org/), which covers nearly all functional interactions among the expressed proteins [17]. Target interaction information derived from the STRING database was imported into the Cytoscape (version 3.7.1; https://www.cytoscape.org/) software where the interaction information was integrated and analyzed.

2.6. GO and KEGG Pathway Enrichment Analyses

GO is the most comprehensive and widely used knowledgebase for the classification of gene functions, including the biological process (BP), molecular function (MF), and cell component (CC) [18]. KEGG (http://www.kegg.jp/) is an encyclopedia of genes and genomes; connecting genomic information to higher-order functional information to capture significantly enriched biological pathways is the major objective of the KEGG knowledgebase [19].

In our study, GO functional annotation and KEGG pathway enrichment analysis were carried out via using the ClusterProfiler package of R software, and P < 0.05 was employed as a screening threshold.

3. Results

3.1. Active Ingredients of Cordyceps

The active chemical components of Cordyceps were selected via the TCMSP databases, and 38 compounds were collected with the thresholds of OB ≥ 30% and DL ≥ 0.18 properties. Finally, 7 candidate ingredients were screened out for further study (Table 1).

Table 1.

Essential information about Cordyceps active components.

Mol ID Molecule name OB (%) DL
MOL001645 Linoleyl acetate 42.1 0.2
MOL001439 Arachidonic acid 45.57 0.2
MOL008999 Cholesteryl palmitate 31.05 0.45
MOL000953 CLR 37.87 0.68
MOL000358 Beta-sitosterol 36.91 0.75
MOL008998 Cerevisterol 39.52 0.77
MOL011169 Peroxyergosterol 44.39 0.82
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3.2. Compound-Target Network Construction

In order to visualize the interaction relationship between the Cordyceps ingredients and corresponding targets, we established a compound-target network (Figure 2). By mapping 7 components to 293 potential targets, the network comprises 300 nodes and 500 edges, in which the red circles correspond to the putative targets and Cordyceps ingredients are in green. Chemical compound arachidonic acid, cerevisterol, beta-sitosterol, linoleyl acetate, cholesteryl palmitate, CLR, and peroxyergosterol correspond to 133, 100, 77, 57, 53, 49, and 31 targets, respectively. The results suggest that these 7 components probably serve significant therapeutic roles in DN.

3.3. Predicting DN-Related Targets

By retrieving the DisGeNET and GeneCards databases, results were integrated to obtain the DN-associated targets. As shown in Figure 3, the potential target genes in Cordyceps were matched to the DN-associated target gene by using the Funrich platform and was shown as a Venn diagram. Finally, 85 putative targets were selected according to the intersection of component targets and DN-related targets; one target was excluded because it had no interaction with other targets (Table 2).

Table 2.

Information of putative targets and topological attributes.

No. UniProt ID Protein names Gene names Degree
1 P01375 Tumor necrosis factor TNF 51
2 P28482 Mitogen-activated protein kinase 1 MAPK1 45
3 P01133 Proepidermal growth factor EGF 45
4 P00533 Epidermal growth factor receptor EGFR 45
5 P42574 Caspase-3 CASP3 45
6 P04637 Cellular tumor antigen p53 TP53 44
7 P35354 Prostaglandin G/H synthase 2 PTGS2 41
8 P14780 Matrix metalloproteinase-9 MMP9 40
9 P29474 Nitric oxide synthase, endothelial NOS3 40
10 P05412 Transcription factor AP-1 JUN 40
11 P37231 Peroxisome proliferator-activated receptor gamma PPARG 37
12 P60484 Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN PTEN 37
13 P45983 Mitogen-activated protein kinase 8 MAPK8 36
14 Q04206 Transcription factor p65 RELA 35
15 P35968 Vascular endothelial growth factor receptor 2 KDR 33
16 Q16539 Mitogen-activated protein kinase 14 MAPK14 33
17 P08253 72 kDa type IV collagenase MMP2 32
18 P42345 Serine/threonine-protein kinase mTOR MTOR 32
19 P03372 Estrogen receptor ESR1 31
20 P12821 Angiotensin-converting enzyme ACE 28
21 P01137 Transforming growth factor beta-1 proprotein TGFB1 27
22 P16284 Platelet endothelial cell adhesion molecule PECAM1 27
23 P42336 Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform PIK3CA 26
24 P08254 Stromelysin-1 MMP3 25
25 Q14790 Caspase-8 CASP8 25
26 P09619 Platelet-derived growth factor receptor beta PDGFRB 23
27 Q00987 E3 ubiquitin-protein ligase Mdm2 MDM2 23
28 P19438 Tumor necrosis factor receptor superfamily member 1A TNFRSF1A 23
29 P55211 Caspase-9 CASP9 22
30 Q06124 Tyrosine-protein phosphatase nonreceptor type 11 PTPN11 22
31 P41235 Hepatocyte nuclear factor 4-alpha HNF4A 20
32 P30556 Type-1 angiotensin II receptor AGTR1 20
33 Q07869 Peroxisome proliferator-activated receptor alpha PPARA 20
34 P11511 Aromatase CYP19A1 16
35 P35228 Nitric oxide synthase, inducible NOS2 16
36 P49327 Fatty acid synthase FASN 16
37 O14920 Inhibitor of nuclear factor kappa-B kinase subunit beta IKBKB 16
38 P25116 Proteinase-activated receptor 1 F2R 15
39 P17252 Protein kinase C alpha type PRKCA 15
40 P07148 Fatty acid-binding protein, liver FABP1 15
41 P15090 Fatty acid-binding protein, adipocyte FABP4 15
42 P16109 P-selectin SELP 14
43 P11473 Vitamin D3 receptor VDR 14
44 P29350 Tyrosine-protein phosphatase nonreceptor type 6 PTPN6 14
45 O95477 Phospholipid-transporting ATPase ABCA1 ABCA1 14
46 P25101 Endothelin-1 receptor EDNRA 13
47 P55851 Mitochondrial uncoupling protein 2 UCP2 12
48 P48736 Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma isoform PIK3CG 12
49 P05771 Protein kinase C beta type PRKCB 12
50 Q13464 Rho-associated protein kinase 1 ROCK1 12
51 P20333 Tumor necrosis factor receptor superfamily member 1B TNFRSF1B 12
52 P15121 Aldo-keto reductase family 1 member B1 AKR1B1 11
53 P21554 Cannabinoid receptor 1 CNR1 11
54 P04629 High affinity nerve growth factor receptor NTRK1 11
55 P22894 Neutrophil collagenase MMP8 11
56 Q99814 Endothelial PAS domain-containing protein 1 EPAS1 10
57 P10415 Apoptosis regulator Bcl-2 BCL2 10
58 P32246 C-C chemokine receptor type 1 CCR1 8
59 P11413 Glucose-6-phosphate 1-dehydrogenase G6PD 8
60 P08123 Collagen alpha-2 COL1A2 8
61 P23219 Prostaglandin G/H synthase 1 PTGS1 8
62 Q13133 Oxysterols receptor LXR-alpha NR1H3 8
63 O75116 Rho-associated protein kinase 2 ROCK2 8
64 Q03181 Peroxisome proliferator-activated receptor delta PPARD 7
65 P25105 Platelet-activating factor receptor PTAFR 7
66 P27169 Serum paraoxonase/arylesterase 1 PON1 7
67 P17706 Tyrosine-protein phosphatase nonreceptor type 2 PTPN2 7
68 O75469 Nuclear receptor subfamily 1 group I member 2 NR1I2 6
69 P28223 5-Hydroxytryptamine receptor 2A HTR2A 6
70 P12104 Fatty acid-binding protein, intestinal FABP2 6
71 P02753 Retinol-binding protein 4 RBP4 5
72 P39900 Macrophage metalloelastase MMP12 5
73 P08235 Mineralocorticoid receptor NR3C2 5
74 P11597 Cholesteryl ester transfer protein CETP 5
75 P07477 Trypsin-1 PRSS1 4
76 Q07973 1,25-Dihydroxyvitamin D CYP24A1 4
77 P30542 Adenosine receptor A1 ADORA1 4
78 P18054 Polyunsaturated fatty acid lipoxygenase ALOX12 ALOX12 4
79 O00763 Acetyl-CoA carboxylase 2 ACACB 4
80 P04278 Sex hormone-binding globulin SHBG 3
81 P80365 Corticosteroid 11-beta-dehydrogenase isozyme 2 HSD11B2 3
82 P13866 Sodium/glucose cotransporter 1 SLC5A1 2
83 P35610 Sterol O-acyltransferase 1 SOAT1 1
84 P05091 Aldehyde dehydrogenase, mitochondrial ALDH2 1
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3.4. Common Target Network

85 putative genes correlated with DN were imported to the STRING database for analysis and network establishment. The interaction network was based on the selected targets with a medium confidence score of 0.400 (Figure 4). A total of 84 nodes and 767 edges were embodied, and the average node degree is 18.3 after analysis. The results were imported to the Cytoscape software for further analysis and network construction (Figure 5). In this figure, the edges represent the interaction between a pair of potential targets, while the nodes represent the targets, and the degree value indicates the intensity of target interaction.

Figure 4.

Figure 4

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PPI network of overlapping targets between DN and Cordyceps. Each circular node represents a protein target, and the 3D structure in the circular nodes shows the protein spatial structure. The lines among different nodes represent the association among potential protein targets, while the width of lines was according to the action intensity.

Figure 5.

Figure 5

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PPI network integration and analysis. The red nodes display the most potential targets in treating DN, which are greater than the average degree. Also, the nodes in purple show the potential targets under the average degree. Furthermore, the density of lines among inner nodes indicates the interaction relationship between different protein targets.

3.5. GO and KEGG Pathway Enrichment Analyses

After using the ClusterProfiler package for pathway analysis, a total of 1843 biological processes were enriched. The top 10 remarkably enriched BP terms were selected for analysis, including regulation of inflammatory response, response to lipopolysaccharide, response to molecule of bacterial origin, response to nutrient levels, muscle cell proliferation, response to oxygen levels, cellular response to chemical stress, regulation of lipid metabolic process, and lipid localization. Besides, 32 cell components were enriched, and the top 10 entries were screened, consisting of membrane raft, membrane microdomain, membrane region, RNA polymerase II transcription regulator complex, transcription regulator complex, caveola, vesicle lumen, plasma membrane raft, mitochondrial outer membrane, and region of cytosol. Furthermore, a total of 118 molecular functions were enriched; the top 10 entries were selected, containing steroid hormone receptor activity, monocarboxylic acid-binding, carboxylic acid-binding, fatty acid-binding, organic acid-binding, steroid binding, nuclear receptor activity, ligand-activated transcription factor activity, nuclear receptor transcription coactivator activity, and long-chain fatty acid-binding. These processes are of great significance to further understand the curative mechanism of Cordyceps on the treatment of DN. The results of the GO analysis are illustrated in Figure 6.

Figure 6.

Figure 6

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Enriched GO terms for BP, MF, and CC of putative targets of active components of Cordyceps. The color of the bar is displayed in a gradient from red to blue according to the ascending order of the P value, while the length of the bar is arranged according to the ascending order of the number of gene counts.

In terms of KEGG analysis, a total of 118 pathways were obtained. The top 20 significantly enriched pathways were screened out based on the threshold of P < 0.05 (Figure 7). The results indicated that these genes were mainly associated with the inflammatory signaling pathway, apoptosis, oxidative stress, and insulin signaling pathway, including AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, apoptosis, IL-17 signaling pathway, PI3K-Akt signaling pathway, and insulin resistance. The results prove that Cordyceps may alleviate DN by regulating insulin resistance, apoptosis, and inflammatory reaction.

Figure 7.

Figure 7

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KEGG pathway analysis of potential targets of Cordyceps. The top 20 pathways are selected following the criteria of P < 0.05. The longitudinal axis represents the name of different pathways, and the transverse axis shows the number of enriched genes. Besides, the dot size represents the proportion of the number of enriched genes to the total number of genes, which is according to the descending proportion value. And the color of the dot is displayed in a gradient from red to blue according to the ascending order of the P value.

To more intuitively demonstrate which pathway each target is involved in, the target pathway data captured from KEGG analysis was uploaded into Cytoscape software for constructing a network graph of target and pathway (Figure 8).

Figure 8.

Figure 8

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Pathway-target interaction network. Red circles indicate pathways which have interactions with potential targets, while the yellow circles represent putative targets.

4. Discussion

DN, featured as high incidence, multifactorial pathogenesis, and absence of practical methods for diagnosis and treatment, is undoubtedly a medical challenge worldwide. The etiology of DN is multifactorial; with hyperglycemia, oxidative stress, and advanced glycation end products (AGE) as the leading factors, chronic inflammation and infiltrated immune cells in renal tissue are considered to be the common pathological consequences [20, 21]. Despite current improving therapies, there is still a considerable residual risk of DN onset and progression [22]. Some relevant studies highlight new perspectives of TCM for delaying DN progression and strengthen the therapeutic rationale for TCM on the treatment of DN [23, 24]. Cordyceps contains several active ingredients which affect multiple targets and pathways in the progression of DN and has been used as an adjuvant on the treatment of DN in China for a long time [7, 25]. In this study, we selected 7 active ingredients from Cordyceps based on the OB and DL, including linoleyl acetate, arachidonic acid, cholesteryl palmitate, CLR, beta-sitosterol, cerevisterol, and peroxyergosterol. Some compounds of Cordyceps were reported to have the effect on ameliorating endocrine and metabolic disorders during the development of DN [26–29]. For instance, it was reported that beta-sitosterol protects the expression of insulin signaling molecules through activating insulin receptor and glucose transporter 4 in the adipose tissue with a high-fat diet, which can slow the development of DN [29]. Arachidonic acid is a strong inducer of insulin secretion and it can attenuate DN by inhibiting the TGF-β/Smad signaling pathway [26, 30]. In addition, arachidonic acid also can facilitate the production of anti-inflammatory lipoxins which were reported to improve insulin sensitivity and may prevent the development of diabetes [27]. Besides, several arachidonic acid metabolites, including PGE2, PGI2, and LXA4, PGE2 and PGI2 can alleviate insulin resistance and improve insulin sensitivity of pancreatic cells [28]; LXA4 can inhibit the production of IL-6, TNF-α, and ROS, thus alleviating inflammation, and has an antidiabetic effect [31, 32].

We found that many targets can be regulated by multiple compounds from the compound-target network, such as CYP19A1, NOS2, NR1H3, SHBG, and PTGS2. When it comes to core targets, TNF (degree = 51), MAPK1 (degree = 51), EGFR (degree = 45), and CASP3 (degree = 45) played an important role in the process of Cordyceps in DN treatment. TNF and MAPK1 are correlated with inflammation response and deterioration of renal function [33, 34]. CASP3 is known to have an important role in the promotion of apoptotic cell death [35]. AG1478 can block EGFR signaling and inhibit oxidative stress and endoplasmic reticulum stress markers in diabetic mice [36]. In addition, inhibition of EGFR activation is associated with improved DN and insulin resistance in type 2 diabetes mouse models [37]. Therefore, the candidate targets are mainly enriched for oxidative stress, insulin resistance, apoptosis, and inflammation.

We also selected 85 common targets between the components of Cordyceps and DN for performing GO enrichment, consisting of biological processes, molecular function, and cellular components, which is aimed at predicting the mechanism of Cordyceps in treating DN. We found that the candidate targets are involved in multiple biological processes, such as regulation of inflammatory response, response to lipopolysaccharide, response to nutrient levels, response to oxygen levels, cellular response to chemical stress, regulation of lipid metabolic process, and lipid localization. The active targets such as TNF, PPARG, MAPK1, EGFR, and TGF-β1 mainly participate in the biological processes of inflammatory response, oxidative stress, and lipid metabolic process [36, 38, 39]. Thus, the molecular processes of several targets are relatively consistent with the pathogenesis and mechanism of clinical DN. In addition, cellular components constitute membrane raft, membrane microdomain, membrane region, RNA polymerase II transcription regulator complex, and transcription regulator complex. It indirectly illustrates the complexity of the pathogenesis of DN and its damage to several cellular components, and Cordyceps may have a function in regulating these cellular components, eventually improving DN. Besides, molecular functions are mainly enriched in steroid hormone-related activity and steroid-binding, and many studies reported that steroid hormones were closely related to DN in patients with diabetes [40, 41]. It reveals that Cordyceps might target steroid hormone in DN treatment.

Using network pharmacological analysis and performing KEGG enrichment, we found that these Cordyceps components may relieve the symptoms of DN through the action of targets in various signaling pathways and multiple biological processes, including AGE-RAGE signaling pathway, TNF signaling pathway, apoptosis, IL-17 signaling pathway, PI3K-Akt signaling pathway, and insulin resistance. Activation of the receptor for AGEs or RAGE (receptor for advanced glycation end-products) is associated with the development of DN [42], which evokes oxidative stress and chronic inflammation in renal tissues, ending up in losses in kidney function by activating various intracellular signalings like PI3K/Akt/mTOR, NF-κB, MAPK/ERK, and TGF-β/Smad [43–45]. Additionally, it is believed that reactive oxygen species (ROS) can regulate PI3K/Akt/mTOR signaling and play an essential role in the development of DN, including epithelial-mesenchymal transition (EMT). During EMT, epithelial cells lose their primary epithelial properties, such as epithelial- (E-) cadherin, while acquiring characteristics typical of mesenchymal cells such as α-SMA, ending up with renal interstitial fibrosis [46]. Moreover, PI3K/Akt/mTOR signaling can promote high glucose-induced podocyte apoptosis, which contributes to the pathogenesis of DN [47].

TNF signaling is characterized as a well-known inflammatory cytokine associated with renal injury [48]. Once stimulated by TNF-α, NF-κB moves from the cytoplasm to the nucleus and activates the transcription of VCAM-1, ICAM-1, IL-6, and IL-8, which will result in endothelial inflammatory and DN pathological process acceleration [49]. Furthermore, NF-κB can be activated by several cytokines, which in turn induces the production of TNF-α, resulting in diabetic renal damage [50].

Insulin resistance is a critical process and one of the main symptoms in the initiation and progression of DN, which is closely related to microalbuminuria [51, 52]. High levels of insulin can cause insulin receptor degradation and drive early podocyte insulin resistance, and both the insulin receptor and nephrin are needed for full insulin sensitivity of podocytes. Thus, it explains why individuals with nephropathy caused by type 2 diabetes are commonly hyperinsulinaemic in the early stage of their disease [53]. Moreover, there are lots of mediators of insulin resistance that participate in driving renal function decline, including TGF-β1, blood pressure, inflammation, TNF-α, IL-6, and oxidative stress [54–56].

Therefore, these results of network pharmacological analyses not only verify that our screened targets are consistent with previous literature reports but also indicate that Cordyceps play a significant therapeutic role in DN by regulating several signaling pathways, including inflammatory response, insulin resistance, oxidative stress, apoptosis, and other pathways with unclear mechanisms. It will provide a novel methodology for further study of the therapeutic mechanism of Cordyceps in alleviating DN.

5. Conclusion

In summary, our network pharmacological analyses have shown that Cordyceps play an indispensable supplementary role in the treatment of DN, which is consistent with previous studies. Moreover, the biological functions of active chemical molecules and their corresponding targets of Cordyceps analyzed by network pharmacological methods provide a unique and innovative path for the study of TCM and further reveal the molecular biological mechanism of Cordyceps in treating DN. However, in vivo and in vitro experiments should be undertaken to validate the relationship between key targets and pathways of Cordyceps for the treatment of DN. Despite the limitations of this study, the results of this study provide new evidence and theoretical basis which will be used in subsequent theoretical and clinical research studies of Cordyceps.

Acknowledgments

This work was supported by the Science and Technology Department of Sichuan Province (grant number: 20YYJC4065).

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no competing interests.

Authors' Contributions

Yan Li and Lei Wang contributed equally to this work. All authors have contributed to this study and approve its submission.

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Data Availability Statement

The data used to support the findings of this study are included within the article.

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