Integrated Strategy of UHPLC-Q-TOF-MS and Molecular Networking for Identification of Diterpenoids from Steud. and Prediction of the Anti-Breast-Cancer Mechanism by the Network Pharmacological Method.

Breast cancer is one of the most common malignancies in women worldwide. Traditional Chinese medicine has been used as adjunctive or complementary therapy for breast cancer. Diterpenoids from Steud. have been demonstrated to possess anti-breast-cancer activity. This research was aimed to systematically explore the diterpenoids from and study the multiple mechanisms on breast cancer. The structures of diterpenoids were identified by the integrated strategy of UHPLC-Q-TOF-MS and molecular networking. A total of 177 diterpenoids belonging to 13 types were collected. In silico ADME analysis was performed on these compounds. It indicated that 130 of 177 diterpenoids completely adjusted to Lipinski's rule. The targets of compounds were obtained from PharmMapper. The targets of breast cancer were collected from GeneCards. Then, 197 compounds-related targets and 544 breast cancer-related targets were identified. After the intersection process, 58 overlapping targets between compounds-related targets and breast cancer-related targets were acquired. The STRING database was applied to predict the protein-protein interactions. The GO and KEGG pathway enrichment analysis were performed by using the KOBAS database. It indicated that these predicted pathways were closely related to breast cancer. The treatment effect of on breast cancer might be performed through signaling pathways, such as IL-17 signaling pathway, MAPK signaling pathway, and PI3K-Akt signaling pathway. The predicted top genes such as EGFR, ESR, MAPK, SRC, CASP3, CDK2, and KDR were involved in cell proliferation, gene transcription, apoptosis, signal transduction, DNA damage and repair, tumor differentiation, metastasis, and cell cycle, which indicated that might treat breast cancer comprehensively. A compounds-KEGG pathways-related targets network was built by using cytoHubba to analyze the hub compounds and targets. It concluded that treated breast cancer not only by the main components but also by the microconstituents, which reflected the overall regulatory role of multicomponents treating breast cancer. To estimate the binding affinities, binding sites, and binding postures, molecular docking simulations between 177 diterpenoids and top 19 targets were carried out. The results are basically in line with expectations. In conclusion, these results can serve as references for researchers studying potential targets of diterpenoids from on breast cancer in the future.