Exploring the underlying mechanism of action of a traditional Chinese medicine formula, Youdujing ointment, for cervical cancer treatment

Background: A traditional Chinese medicine formula, Youdujing (YDJ) ointment, is widely used for treatment of human papilloma virus-related diseases, such as cervical cancer. However, the underlying mechanisms by which active compounds of YDJ alleviates cervical cancer are still unclear. Methods: We applied a comprehensive network pharmacology approach to explore the key mechanisms of YDJ by integrating potential target identi fi cation, network analysis, and enrichment analysis into classical molecular docking procedures. First, we used network and enrichment analyses to identify potential therapeutic targets. Second, we performed molecular docking to investigate the potential active compounds of YDJ. Finally, we carried out a network-based analysis to unravel potentially effective drug combinations. Results: Network analysis yielded four potential therapeutic targets: ESR1, NFKB1, TNF, and AKT1. Molecular docking highlighted that these proteins may interact with four potential active compounds of YDJ: E4, Y2, Y20, and Y21. Finally, we found that Y2 or Y21 can act alone or together with E4 to trigger apoptotic cascades via the mitochondrial apoptotic pathway and estrogen receptors. Conclusion: Our study not only explained why YDJ is effective for cervical cancer treatment, but also lays a strong foundation for future clinical studies based on this traditional medicine. summary: mechanisms underlying the effect of remained unclear, so we developed a network pharmacology method to investigate the active compounds and their possible combinations by integrating network and enrichment analyses with molecular docking. In this paper, we found four potential active compounds and four potential therapeutic targets of YDJ. However, these fi ndings should be con fi rmed by further experiments in vitro and in vivo , whose results can be integrated in the present bioinformatic algorithm in order to optimize our method in the future.