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      Mechanisms of Compound Kushen Injection for the Treatment of Lung Cancer Based on Network Pharmacology

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          Abstract

          Background

          Compound Kushen Injection (CKI) is a Chinese patent drug that shows good efficacy in treating lung cancer (LC). However, its underlying mechanisms need to be further clarified.

          Methods

          In this study, we adopted a network pharmacology method to gather compounds, predict targets, construct networks, and analyze biological functions and pathways. Moreover, molecular docking simulation was employed to assess the binding potential of selected target-compound pairs.

          Results

          Four networks were established, including the compound-putative target network, protein-protein interaction (PPI) network of LC targets, compound-LC target network, and herb-compound-target-pathway network. Network analysis showed that 8 targets (CHRNA3, DRD2, PRKCA, CDK1, CDK2, CHRNA5, MMP1, and MMP9) may be the therapeutic targets of CKI in LC. In addition, molecular docking simulation indicated that CHRNA3, DRD2, PRKCA, CDK1, CDK2, MMP1, and MMP9 had good binding activity with the corresponding compounds. Furthermore, enrichment analysis indicated that CKI might exert a therapeutic role in LC by regulating some important pathways, namely, pathways in cancer, proteoglycans in cancer, PI3K-Akt signaling pathway, non-small-cell lung cancer, and small cell lung cancer.

          Conclusions

          This study validated and predicted the mechanism of CKI in treating LC. Additionally, this study provides a good foundation for further experimental studies and promotes the reasonable application of CKI in the clinical treatment of LC.

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          Most cited references88

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          Cytoscape: software for visualization and analysis of biological networks.

          Substantial progress has been made in the field of "omics" research (e.g., Genomics, Transcriptomics, Proteomics, and Metabolomics), leading to a vast amount of biological data. In order to represent large biological data sets in an easily interpretable manner, this information is frequently visualized as graphs, i.e., a set of nodes and edges. Nodes are representations of biological molecules and edges connect the nodes depicting some kind of relationship. Obviously, there is a high demand for computer-based assistance for both visualization and analysis of biological data, which are often heterogeneous and retrieved from different sources. This chapter focuses on software tools that assist in visual exploration and analysis of biological networks. Global requirements for such programs are discussed. Utilization of visualization software is exemplified using the widely used Cytoscape tool. Additional information about the use of Cytoscape is provided in the Notes section. Furthermore, special features of alternative software tools are highlighted in order to assist researchers in the choice of an adequate program for their specific requirements.
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            Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1.

            To identify risk variants for lung cancer, we conducted a multistage genome-wide association study. In the discovery phase, we analyzed 315,450 tagging SNPs in 1,154 current and former (ever) smoking cases of European ancestry and 1,137 frequency-matched, ever-smoking controls from Houston, Texas. For replication, we evaluated the ten SNPs most significantly associated with lung cancer in an additional 711 cases and 632 controls from Texas and 2,013 cases and 3,062 controls from the UK. Two SNPs, rs1051730 and rs8034191, mapping to a region of strong linkage disequilibrium within 15q25.1 containing PSMA4 and the nicotinic acetylcholine receptor subunit genes CHRNA3 and CHRNA5, were significantly associated with risk in both replication sets. Combined analysis yielded odds ratios of 1.32 (P < 1 x 10(-17)) for both SNPs. Haplotype analysis was consistent with there being a single risk variant in this region. We conclude that variation in a region of 15q25.1 containing nicotinic acetylcholine receptors genes contributes to lung cancer risk.
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              The RCSB protein data bank: integrative view of protein, gene and 3D structural information

              The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, http://rcsb.org), the US data center for the global PDB archive, makes PDB data freely available to all users, from structural biologists to computational biologists and beyond. New tools and resources have been added to the RCSB PDB web portal in support of a ‘Structural View of Biology.’ Recent developments have improved the User experience, including the high-speed NGL Viewer that provides 3D molecular visualization in any web browser, improved support for data file download and enhanced organization of website pages for query, reporting and individual structure exploration. Structure validation information is now visible for all archival entries. PDB data have been integrated with external biological resources, including chromosomal position within the human genome; protein modifications; and metabolic pathways. PDB-101 educational materials have been reorganized into a searchable website and expanded to include new features such as the Geis Digital Archive.
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                Author and article information

                Contributors
                Journal
                Evid Based Complement Alternat Med
                Evid Based Complement Alternat Med
                ECAM
                Evidence-based Complementary and Alternative Medicine : eCAM
                Hindawi
                1741-427X
                1741-4288
                2019
                28 May 2019
                28 May 2019
                : 2019
                : 4637839
                Affiliations
                1Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100102, China
                2Department of Academic Management, China Academy of Chinese Medicine Sciences, Beijing 100700, China
                Author notes

                Academic Editor: Laura De Martino

                Author information
                http://orcid.org/0000-0002-1617-6110
                Article
                10.1155/2019/4637839
                6558614
                22331b25-3a42-48f1-82c4-6e3f441d2cd8
                Copyright © 2019 Ziqi Meng et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 January 2019
                : 23 April 2019
                : 20 May 2019
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 81473547
                Award ID: 81673829
                Categories
                Research Article

                Complementary & Alternative medicine
                Complementary & Alternative medicine

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