Your browser doesn't support javascript.
loading
Network Pharmacology-Based Prediction of Mechanism of Shenzhuo Formula for Application to DKD.
Wang, Xinmiao; Yang, Haoyu; Zhang, Lili; Han, Lin; Di, Sha; Wei, Xiuxiu; Wu, Haoran; Zhang, Haiyu; Zhao, Linhua; Tong, Xiaolin.
Afiliación
  • Wang X; Department of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
  • Yang H; Department of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
  • Zhang L; Graduate College, Beijing University of Traditional Chinese Medicine, Beijing 100029, China.
  • Han L; Department of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
  • Di S; Department of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
  • Wei X; Department of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
  • Wu H; Department of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
  • Zhang H; Graduate College, Beijing University of Traditional Chinese Medicine, Beijing 100029, China.
  • Zhao L; Department of Endocrinology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
  • Tong X; Graduate College, Beijing University of Traditional Chinese Medicine, Beijing 100029, China.
Article en En | MEDLINE | ID: mdl-33968154
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.

Texto completo: 1 Bases de datos: MEDLINE Medicinas Tradicionales: Medicinas_tradicionales_de_asia / Medicina_china Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Evid Based Complement Alternat Med Año: 2021 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Bases de datos: MEDLINE Medicinas Tradicionales: Medicinas_tradicionales_de_asia / Medicina_china Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Evid Based Complement Alternat Med Año: 2021 Tipo del documento: Article País de afiliación: China