Identifying the Antiproliferative Effect of Astragalus Polysaccharides on Breast Cancer: Coupling Network Pharmacology With Targetable Screening From the Cancer Genome Atlas - Liu, Cun; Li, Huayao; Wang, Kejia; Zhuang, Jing; Chu, Fuhao; Gao, Chundi; Liu, Lijuan; Feng, Fubin; Zhou, Chao; Zhang, Wenfeng; Sun, Changgang (Frontiers in Oncology, 2019)
Background: Astragalus polysaccharides (APS), natural plant compounds, have recently emerged as a promising strategy for cancer treatment, but little is known concerning their effects on breast cancer (BC) tumourigenesis. Methods: We obtained breast cancer genetic data from The Cancer Genome Atlas (TCGA) database, network pharmacology to further clarify its biological properties. Survival analysis and molecular docking techniques were implemented for the final screening to obtain key target information. Our experiments focused on the detection of intervention effects of APS on BC cells (MCF-7 and MDA-MB-231), and quantitative RT-PCR (qRT-PCR) was used to assess the expression of key targets. Results: A total of 1,439 differentially expressed genes (DEGs) were identified by TCGA and used to build disease networks. Module analysis, gene ontology and pathway analysis revealed characteristic of the DEGs network. Topological properties were used to identify key targets, survival analysis and molecular docking finally found that the targets of APS regulation of BC cells may be CCNB1, CDC6 and p53. Through cell viability, migration and invasion assays, we found that APS interferes with the development of breast cancer in MCF7 and MDA-MB-231 cells in a dose-dependent manner. Furthermore, qRT-PCR verification suggested that the expression of CCNB1 and CDC6 in breast cancer cells was significantly downregulated in response to APS, while expression of the tumour suppressor gene P53 was significantly increased. Conclusion: Results of this study suggest therapeutic potential for APS in BC treatment, possibly through interventions with CCNB1, CDC6 and P53. Furthermore, these findings illustrate the feasibility of using network pharmacology to connect large-scale target data as a way to discover the mechanism of natural products interfering with disease.