Conclusions
In this study, the active components and main targets of DH for UC treatment were initially forecasted, and the potential mechanism was investigated through network pharmacology. These findings offer an experimental foundation for the clinical utilization of DH.
Methods
A network pharmacology approach was used to perform component screening, target prediction, PPI network interaction analysis, GO and KEGG enrichment analysis to initially predict the mechanism of DH treatment for UC. Then, the mechanism was validated with the UC mouse model induced by 3% DSS.
Results
Based on the network pharmacological analysis, a comprehensive of 101 active components were identified, with 19 of them potentially serving as the crucial elements in DH's effectiveness against UC treatment. Additionally, the study revealed 314 potential core therapeutic targets along with the top 5 key targets: SRC, STAT3, AKT1, HSP90AA1, and PIK3CA. In experiments conducted on live mice with UC, DH was found to decrease the levels of IL-6 and TNF-α in the blood, while increasing the levels of IL-10 and TGF-β. This led to notable improvements in colon length, injury severity, and an up-regulation of SRC, STAT3, HSP90AA1, PIK3CA, p-AKT1 and PI3K/AKT signaling pathway expression in the colon tissue. Conclusions: In this study, the active components and main targets of DH for UC treatment were initially forecasted, and the potential mechanism was investigated through network pharmacology. These findings offer an experimental foundation for the clinical utilization of DH.