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. 2021 Apr 17:2021:6623010.
doi: 10.1155/2021/6623010. eCollection 2021.

Network Pharmacology-Based Prediction of Mechanism of Shenzhuo Formula for Application to DKD

Xinmiao Wang  1 , Haoyu Yang  1   2 , Lili Zhang  1 , Lin Han  1 , Sha Di  1 , Xiuxiu Wei  1   2 , Haoran Wu  1   2 , Haiyu Zhang  1   2 , Linhua Zhao  3 , Xiaolin Tong  4
Affiliations

Network Pharmacology-Based Prediction of Mechanism of Shenzhuo Formula for Application to DKD

Xinmiao Wang et al. Evid Based Complement Alternat Med. .

Abstract

Background: Shenzhuo formula (SZF) is a traditional Chinese medicine (TCM) prescription which has significant therapeutic effects on diabetic kidney disease (DKD). However, its mechanism remains unknown. Therefore, this study aimed to explore the underlying anti-DKD mechanism of SZF.

Methods: The active ingredients and targets of SZF were obtained by searching TCMSP, TCMID, SwissTargetPrediction, HIT, and literature. The DKD target was identified from TTD, DrugBank, and DisGeNet. The potential targets were obtained and PPI network were built after mapping SZF targets and DKD targets. The key targets were screened out by network topology and the "SZF-key targets-DKD" network was constructed by Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed by using DAVID, and the results were visualized by Omicshare Tools.

Results: We obtained 182 potential targets and 30 key targets. Furthermore, a "SZF-key targets-DKD" network topological analysis showed that active ingredients like M51, M21, M5, M71, and M28 and targets like EGFR, MMP9, MAPK8, PIK3CA, and STAT3 might play important roles in the process of SZF treating in DKD. GO analysis results showed that targets were mainly involved in positive regulation of transcription from RNA polymerase II promoter, inflammatory response, lipopolysaccharide-mediated signaling pathway, and other biological processes. KEGG showed that DKD-related pathways like TNF signaling pathway and PI3K-Akt signaling pathway were at the top of the list.

Conclusion: This research reveals the potential pharmacological targets of SZF in the treatment of DKD through network pharmacology and lays a foundation for further studies.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
PPI network of the 182 common targets.
Figure 2
Figure 2
“SZF-key targets-DKD” network. The nodes were visualized with degree. The larger and the redder the node, the higher the degree it was. M1-75 stand for the active ingredients whose full names are shown in Table 4.
Figure 3
Figure 3
Top 20 enrichments in GO analysis.
Figure 4
Figure 4
Top 20 KEGG pathway enrichments.
See this image and copyright information in PMC

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