Abstract
As a popular Chinese herbal medicine (CHM), polygonum cuspidatum is widely used to treat various diseases in China. However, its biological function and action mechanism have yet to be systematically explored. In the present study, we first identified 14 potential active ingredients of polygonum cuspidatum using the TCMSP server and then conducted an in silico target prediction for these ingredients using PharmMapper. The subsequent KEGG pathway enrichment analysis of the 57 identified potential targets revealed that they were closely associated with cancer and gynecological disorders. Furthermore, a protein-protein interaction network of these targets was constructed using STRING and Cytoscape, through which 11 core targets were excavated according to degree, a key topological parameter. Meanwhile, we developed a novel formula, in which the "R value" is determined by average shortest path length and closeness centrality, two other key topological parameters, to evaluate the reliability of these predicted core targets. Intriguingly, among the top 10 core targets excavated using this new formula, 7 overlapped with the former 11 core targets, showing a good consistency in these core targets between the different prediction algorithms. Next, 7 ingredients were identified/validated from the crude extract of polygonum cuspidatum using UPLC-MS/MS. Noteworthy, 6 potential targets predicted for these 7 ingredients overlapped with the 7 core targets excavated from the previous in silico analyses. Further molecular docking and druggability analyses suggested that polydatin may play a pivotal role in manifesting the therapeutic effects of polygonum cuspidatum. Finally, we carried out a series of cell functional assays, which validated the anti-proliferative effects of polygonum cuspidatum on gynecological cancer cells, thus demonstrating our network pharmacology approach is reliable and powerful enough to guide the CHM mechanism study.