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      Network pharmacology based virtual screening of active constituents of Prunella vulgaris L. and the molecular mechanism against breast cancer

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          Abstract

          Prunella vulgaris L, a perennial herb widely used in Asia in the treatment of various diseases including cancer. In vitro studies have demonstrated the therapeutic effect of Prunella vulgaris L. against breast cancer through multiple pathways. However, the nature of the biological mechanisms remains unclear. In this study, a Network pharmacology based approach was used to explore active constituents and potential molecular mechanisms of Prunella vulgaris L. for the treatment of breast cancer. The methods adopted included active constituents prescreening, target prediction, GO and KEGG pathway enrichment analysis. Molecular docking experiments were used to further validate network pharmacology results. The predicted results showed that there were 19 active ingredients in Prunella vulgaris L. and 31 potential gene targets including AKT1, EGFR, MYC, and VEGFA. Further, analysis of the potential biological mechanisms of Prunella vulgaris L. against breast cancer was performed by investigating the relationship between the active constituents, target genes and pathways. Network analysis showed that Prunella vulgaris L. exerted a promising preventive effect on breast cancer by acting on tumor-associated signaling pathways. This provides a basis to understand the mechanism of the anti-breast cancer activity of Prunella vulgaris L.

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          Network pharmacology.

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            Network analyses in systems pharmacology.

            Systems pharmacology is an emerging area of pharmacology which utilizes network analysis of drug action as one of its approaches. By considering drug actions and side effects in the context of the regulatory networks within which the drug targets and disease gene products function, network analysis promises to greatly increase our knowledge of the mechanisms underlying the multiple actions of drugs. Systems pharmacology can provide new approaches for drug discovery for complex diseases. The integrated approach used in systems pharmacology can allow for drug action to be considered in the context of the whole genome. Network-based studies are becoming an increasingly important tool in understanding the relationships between drug action and disease susceptibility genes. This review discusses how analysis of biological networks has contributed to the genesis of systems pharmacology and how these studies have improved global understanding of drug targets, suggested new targets and approaches for therapeutics, and provided a deeper understanding of the effects of drugs. Taken together, these types of analyses can lead to new therapeutic options while improving the safety and efficacy of existing medications.
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              On the Treatment of Inoperable Cases of Carcinoma of the Mamma: Suggestions for a New Method of Treatment, with Illustrative Cases

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                Author and article information

                Contributors
                shentaotcm@aliyun.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                25 September 2020
                25 September 2020
                2020
                : 10
                : 15730
                Affiliations
                [1 ]GRID grid.411304.3, ISNI 0000 0001 0376 205X, School of Basic Medicine, , Chengdu University of Traditional Chinese Medicine, ; Chengdu, 611137 China
                [2 ]Academic Department, Zhuhai Ebang Pharmaceutical Co., Ltd, Zhuhai, 519040 China
                [3 ]GRID grid.411304.3, ISNI 0000 0001 0376 205X, College of Information Engineering, , Chengdu University of Traditional Chinese Medicine, ; Chengdu, 611137 China
                Article
                72797
                10.1038/s41598-020-72797-8
                7519149
                32978480
                f1b3ce3c-5fed-41be-8405-4502df93e6b2
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 November 2019
                : 4 September 2020
                Funding
                Funded by: TaoShen
                Award ID: 81973743
                Funded by: XinZhou
                Award ID: 81703844
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                cancer,computational biology and bioinformatics
                Uncategorized
                cancer, computational biology and bioinformatics

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