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

• Published in: Evidence-based complementary and alternative medicine 2021, Vol.2021, p.6623010-13
• Main Authors: Wang, Xinmiao, Yang, Haoyu, Zhang, Lili, Han, Lin, Di, Sha, Wei, Xiuxiu, Wu, Haoran, Zhang, Haiyu, Zhao, Linhua, Tong, Xiaolin
• Format: Article
• Language: English
• Subjects: 1-Phosphatidylinositol 3-kinase; AKT protein; Androgens; Chinese medicine; Cytochrome; Dehydrogenases; Diabetes; Diabetes mellitus; DNA-directed RNA polymerase; Epidermal growth factor; Epidermal growth factor receptors; Estrogens; Gelatinase B; Gene regulation; Genes; Glycoproteins; Inflammation; Insulin-like growth factors; Kidney diseases; Kinases; Lipopolysaccharides; Pathogenesis; Pharmacology; Phosphatase; Proteins; Signal transduction; Stat3 protein; Traditional Chinese medicine; Transcription; Tumor necrosis factor; Vascular endothelial growth factor
• ISSN: 1741-427X; 1741-4288
• DOI: 10.1155/2021/6623010

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.