Investigating the mechanism of Sinisan formula in depression treatment: a comprehensive analysis using GEO datasets, network pharmacology, and molecular docking

Abstract

The herbal formula Sinisan (SNS) is a commonly used treatment for depression; however, its mechanism of action remains unclear. This article uses a combination of the GEO database, network pharmacology and molecular docking technologies to investigate the mechanism of action of SNS. The aim is to provide new insights and methods for future depression treatments. The study aims to extract effective compounds and targets for the treatment of depression from the T CMSP database. Relevant targets were searched using the GEO, Disgenet, Drugbank, PharmGKB and T T D databases, followed by screening of core targets. In addition, GO and KEGG pathway enrichment analyses were performed to explore potential pathways for the treatment of depression. Molecular docking was used to evaluate the potential targets and compounds and to identify the optimal core protein-compound complex. Molecular dynamics was used to further investigate the dynamic variability and stability of the complex. The study identified 118 active SNS components and 208 corresponding targets. Topological analysis of P P I networks identified 11 core targets. GO and KEGG pathway enrichment analyses revealed that the mechanism of action for depression involves genes associated with inflammation, apoptosis, oxidative stress, and the MAP K3 and P I3K-Akt signalling pathways. Molecular docking and dynamics simulations showed a strong binding affinity between these compounds and the screened targets, indicating promising biological activity. The present study investigated the active components, targets and pathways of SNS in the treatment of depression. Through a preliminary investigation, key signalling pathways and compounds were identified. These findings provide new directions and ideas for future research on the therapeutic mechanism of SNS and its clinical application in the treatment of depression.

Communicated by Ramaswamy H. Sarma

Keywords:

• Sinisan
• depression
• GEO datasets
• network pharmacology
• molecular docking
• molecular dynamics
• Traditional Chinese Medicine

Disclosure statement

There are no conflicts of interest declared.

Author’s contributions

X-P. Shi and S-B. Ma proposed and designed this study. M-L. Zheng, X-X. Yang and P. Yuan contributed to writing the manuscript.F-Y. Wang and X-D. Guo performed the analyses.L. Li, J. Wang, and S. Miao revised the manuscript. All authors read and approved the final manuscript.

Data availability statement

All data are included in the main text or supplementary materials. All data generated or analyzed during this study are included in this article (and its Supplementary Information files).

Additional information

Funding

This research is supported by the Key Research and Development Program of Shaanxi Province (No. 2020SF-322,2022SF-205). We are very grateful to the reviewers for reviewing this article.