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      Network Pharmacology-Oriented Identification of Key Proteins and Signaling Pathways Targeted by Xihuang Pill in the Treatment of Breast Cancer

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          Abstract

          Purpose

          The compound traditional Chinese medicine Xihuang pill (XHP) has been adopted to treat breast cancer (BC) for centuries, but its specific mechanism of action is unclear.

          Materials and Methods

          The active ingredients and related targets of XHP were screened using the TCMSP and TCMID databases. GSE139038 was downloaded from the GEO database, and differentially expressed genes (DEGs) were analyzed. The intersection of targets and DEGs were chosen to build an ingredients–target genes network. Protein–protein interaction network construction and functional enrichment analysis of target genes were conducted.

          Results

          A PPI network of 37 targets was constructed, and seven core nodes (FOS, MYC, JUN, PPARG, MMP9, PTGS2, SERPINE1) were identified. Functional enrichment analysis revealed that the aforementioned targets were mainly enriched in the IL-17, toll-like receptor, and tumor necrosis factor signaling pathways, which are deeply involved in the inflammatory microenvironment of tumors.

          Conclusion

          This network pharmacology study indicated that XHP can inhibit the development of BC by targeting a variety of proteins and signaling pathways involved in the inflammatory microenvironment.

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          Most cited references51

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              cytoHubba: identifying hub objects and sub-networks from complex interactome

              Background Network is a useful way for presenting many types of biological data including protein-protein interactions, gene regulations, cellular pathways, and signal transductions. We can measure nodes by their network features to infer their importance in the network, and it can help us identify central elements of biological networks. Results We introduce a novel Cytoscape plugin cytoHubba for ranking nodes in a network by their network features. CytoHubba provides 11 topological analysis methods including Degree, Edge Percolated Component, Maximum Neighborhood Component, Density of Maximum Neighborhood Component, Maximal Clique Centrality and six centralities (Bottleneck, EcCentricity, Closeness, Radiality, Betweenness, and Stress) based on shortest paths. Among the eleven methods, the new proposed method, MCC, has a better performance on the precision of predicting essential proteins from the yeast PPI network. Conclusions CytoHubba provide a user-friendly interface to explore important nodes in biological networks. It computes all eleven methods in one stop shopping way. Besides, researchers are able to combine cytoHubba with and other plugins into a novel analysis scheme. The network and sub-networks caught by this topological analysis strategy will lead to new insights on essential regulatory networks and protein drug targets for experimental biologists. According to cytoscape plugin download statistics, the accumulated number of cytoHubba is around 6,700 times since 2010.
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                Author and article information

                Journal
                Breast Cancer (Dove Med Press)
                Breast Cancer (Dove Med Press)
                bctt
                bctt
                Breast Cancer : Targets and Therapy
                Dove
                1179-1314
                08 December 2020
                2020
                : 12
                : 267-277
                Affiliations
                [1 ]School of Food and Bioengineering, Henan University of Science and Technology , Luoyang, Henan, People’s Republic of China
                [2 ]Henan Engineering Research Center of Food Microbiology, Henan University of Science and Technology , Luoyang, Henan, People’s Republic of China
                [3 ]The First Affiliated Hospital, Henan University of Science and Technology , Luoyang, Henan, People’s Republic of China
                [4 ]School of Medicine, Henan Polytechnic University , Jiaozuo, Henan, People’s Republic of China
                Author notes
                Correspondence: Jiafa Wu School of Food and Bioengineering, Henan University of Science and Technology , Luoyang471023, Henan, People’s Republic of China Email wujiafa@haust.edu.cn
                Article
                284076
                10.2147/BCTT.S284076
                7733446
                27cf5cfe-2c38-40a6-8d41-bc19d8c392c9
                © 2020 Wu et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                Page count
                Figures: 10, Tables: 3, References: 51, Pages: 11
                Funding
                Funded by: the Natural Science Foundation of Henan Province;
                Funded by: Henan University of Science and Technology, open-funder-registry 10.13039/501100003172;
                Funded by: National Natural Science Foundation of China, open-funder-registry 10.13039/501100001809;
                This research was funded by the Natural Science Foundation of Henan Province (No. 162300410099), Doctor Scientific Research start-up Fund from Henan University of Science and Technology (No. 13480064), and National Natural Science Foundation of China (No. 81700775).
                Categories
                Original Research

                traditional chinese medicine,xihuang pill,breast cancer,network pharmacology

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