scholarly journals Identification of the active substances and mechanisms of ginger for the treatment of colon cancer based on network pharmacology and molecular docking

2020 ◽  
Author(s):  
MengMeng Zhang ◽  
Dan Wang ◽  
Feng Lu ◽  
Rong Zhao ◽  
Xun Ye ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Molecular Docking ◽  
Cancer Prevention ◽  
Signaling Pathways ◽  
Cellular Response ◽  
Endocrine Resistance ◽  
Docking Simulation ◽  
Network Pharmacology ◽  
Integrated Network ◽  
Colon Cancer Prevention

Abstract Background and objective: Colon cancer is increasing in people recently and ginger (Zingiber officinale), as a commonly used herbal medicine, has been suggested as a potential agent against colon cancer. This study was aimed to identify the bioactive compounds and potential mechanisms of ginger for colon cancer prevention by an integrated network pharmacology approach.Methods: Putative ingredients of ginger and its related targets were discerned from the TCMSP database. After that, the targets interacting with colon cancer were collected using Genecards, OMIM, and Drugbank databases. KEGG pathway and GO enrichment analysis were performed to explore the signaling pathways related to ginger for colon cancer treatments. The PPI and compound-target-disease networks were constructed using Cytoscape.Results: Six potential active compounds, 285 interacting targets in addition to 1356 disease-related targets were collected, of which 118 intersection targets were obtained. A total of 34 key targets including PIK3CA, SRC, and TP53 were identified. These targets were mainly focused on the biological processes of phosphatidylinositol 3-kinase signaling, cellular response to oxidative stress, and cellular response to peptide hormone stimulus. The KEGG enrichment manifested that three signaling pathways were closely related to colon cancer prevention of ginger, including cancer, endocrine resistance, and hepatitis B pathways. TP53, HSP90AA1, MAPK8, JAK2, CASP3, and ERBB2 were viewed as the most important genes, which were validated by molecular docking simulation.Conclusion: This study demonstrated that ginger produced preventive effects against colon cancer by regulating multi-target and multi-pathway with multi-components. And, the combined data provide novel insight for ginger compounds developed as new drug for anti-colon cancer.

scholarly journals Identification of the active substances and mechanisms of ginger for the treatment of colon cancer based on network pharmacology and molecular docking

2020 ◽  
Author(s):  
MengMeng Zhang ◽  
Dan Wang ◽  
Feng Lu ◽  
Rong Zhao ◽  
Xun Ye ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Cellular Response ◽  
Protein Localization ◽  
Docking Simulation ◽  
Estrogen Signaling ◽  
Receptor Protein ◽  
Network Pharmacology ◽  
Active Compounds

Abstract Background: Colon cancer is increasing recently but the high cost and adverse side effects experienced always leads to treatment drop out. Zingiber officinale, commonly known as ginger, is a popular herbal medicine and this study was aimed to identify the active compounds from ginger and to investigate its anti-cancer mechanisms through network pharmacology construction. Results: Ginger compounds were discerned through the TCMSP, which were filtered by the metrics of oral bioavailability and drug likeness, and its related targets were searched. After that, the targets interacting with colon cancer were collected using Genecards, OMIM, and Drugbank databases. Six potential active compounds, 288 interacting targets in addition to 1356 disease-related targets were collected, of which 114 intersection targets were obtained. The PPI network showed that 32 targets including SRC, PIK3R1, and TP53 were identified as key targets. These targets were mainly associated with the biological processes like transmembrane receptor protein tyrosine kinase signaling pathway, regulation of cellular protein localization, cellular response to oxidative stress. KEGG enrichment manifested that ginger probably produced preventive effects against colon cancer by regulating significant signaling pathway like pathway in cancer, hepatitis B, and estrogen signaling pathway. TP53, HSP90AA1, MAPK8, JAK2, CASP3, and ERBB2 could be viewed as the most potential target proteins, which were validated by molecular docking simulation.Conclusion: This study demonstrated the multi-component, multi-target, and multi-pathway characteristics of ginger, providing novel insight for ginger compounds developed as new drug for anti-colon cancer.

scholarly journals Network Pharmacology Analysis of the Therapeutic Mechanisms Underlying Beimu-Gualou Formula Activity against Bronchiectasis with In Silico Molecular Docking Validation

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Xin Shen ◽  
Hong Li ◽  
Wen-Jun Zou ◽  
Jian-Ming Wu ◽  
Long Wang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Signaling Pathways ◽  
Cellular Response ◽  
Inflammatory Responses ◽  
Interaction Network ◽  
Mechanisms Of Action ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Target Interaction ◽  
In Silico Molecular Docking

Background. The classical Chinese herbal prescription Beimu-Gualou formula (BMGLF) has been diffusely applied to the treatment of respiratory diseases, including bronchiectasis. Although concerning bronchiectasis the effects and mechanisms of action of the BMGLF constituents have been partially elucidated, it remains to be determined how the formula in its entirety exerts therapeutic effects. Methods. In this study, the multitarget mechanisms of BMGLF against bronchiectasis were predicted with network pharmacology analysis. Using prepared data, a drug-target interaction network was established and subsequently the core therapeutic targets of BMGLF were identified. Furthermore, the biological function and pathway enrichment of potential targets were analyzed to evaluate the therapeutic effects and pivotal signaling pathways of BMGLF. Finally, virtual molecular docking was performed to assess the affinities of compounds for the candidate targets. Results. The therapeutic action of BMGLF against bronchiectasis involves 18 core target proteins, including the aforementioned candidates (i.e., ALB, ICAM1, IL10, and MAPK1), which are assumed to be related to biological processes such as drug response, cellular response to lipopolysaccharide, immune response, and positive regulation of NF-κB activity in bronchiectasis. Among the top 20 signaling pathways identified, mechanisms of action appear to be primarily related to Chagas disease, allograft rejection, hepatitis B, and inflammatory bowel disease. Conclusion. In summary, using a network pharmacology approach, we initially predicted the complex regulatory profile of BMGLF against bronchiectasis in which multilink suppression of immune/inflammatory responses plays an essential role. These results may provide a basis for novel pharmacotherapeutic approaches for bronchiectasis.

scholarly journals Modulation of DNA damage prevention and signaling pathways in diet induced colon cancer prevention

2010 ◽  
Vol 4 (S2) ◽  
Author(s):  
Dalila FN Pedro ◽  
Alice A Ramos ◽  
Cristóvão F Lima ◽  
Fátima Baltazar ◽  
Cristina Pereira-Wilson
Keyword(s):  
Colon Cancer ◽  
Dna Damage ◽  
Cancer Prevention ◽  
Signaling Pathways ◽  
Colon Cancer Prevention ◽  
Damage Prevention

scholarly journals Using Network Pharmacology and Molecular Docking to Explore the Mechanism of Shan Ci Gu (Cremastra appendiculata) Against Non-Small Cell Lung Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Yan Wang ◽  
Yunwu Zhang ◽  
Yujia Wang ◽  
Xinyao Shu ◽  
Chaorui Lu ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Signaling Pathways ◽  
Target Genes ◽  
Kinase Inhibitor ◽  
Endocrine Resistance ◽  
Network Pharmacology ◽  
Egfr Tyrosine Kinase Inhibitor ◽  
The Core ◽  
Erbb Signaling ◽  
Inhibitor Resistance

Background: In recent years, the incidence and mortality rates of non-small cell lung cancer (NSCLC) have increased significantly. Shan Ci Gu is commonly used as an anticancer drug in traditional Chinese medicine; however, its specific mechanism against NSCLC has not yet been elucidated. Here, the mechanism was clarified through network pharmacology and molecular docking.Methods: The Traditional Chinese Medicine Systems Pharmacology database was searched for the active ingredients of Shan Ci Gu, and the relevant targets in the Swiss Target Prediction database were obtained according to the structure of the active ingredients. GeneCards were searched for NSCLC-related disease targets. We obtained the cross-target using VENNY to obtain the core targets. The core targets were imported into the Search Tool for the Retrieval of Interacting Genes/Proteins database, and Cytoscape software was used to operate a mesh chart. R software was used to analyze the Gene Ontology biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The core targets and active compounds were molecularly docked through Auto-Dock Vina software to predict the detailed molecular mechanism of Shan Ci Gu for NSCLC treatment. We did a simple survival analysis with hub gene to assess the prognosis of NSCLC patients.Results: Three compounds were screened to obtain 143 target genes and 1,226 targets related to NSCLC, of which 56 genes were related to NSCLC treatment. Shan Ci Gu treatment for NSCLC involved many BPs and acted on main targets including epidermal growth factor receptor (EGFR), ESR1, and SRC through signaling pathways including the endocrine resistance, EGFR tyrosine kinase inhibitor resistance, and ErbB signaling pathways. Shan Ci Gu might be beneficial for treating NSCLC by inhibiting cell proliferation and migration. Molecular docking revealed that the active compounds β-sitosterol, stigmasterol, and 2-methoxy-9,10-dihydrophenanthrene-4,5-diol had good affinity with the core target genes (EGFR, SRC, and ESR1). Core targets included EGFR, SRC, ESR1, ERBB2, MTOR, MCL1, matrix metalloproteinase 2 (MMP2), MMP9, KDR, and JAK2. Key KEGG pathways included endocrine resistance, EGFR tyrosine kinase inhibitor resistance, ErbB signaling, PI3K-Akt signaling, and Rap1 signaling pathways. These core targets and pathways have an inhibitory effect on the proliferation of NSCLC cells.Conclusion: Shan Ci Gu can treat NSCLC through a multi-target, multi-pathway molecular mechanism and effectively improve NSCLC prognosis. This study could serve as a reference for further mechanistic research on wider application of Shan Ci Gu for NSCLC treatment.

scholarly journals Network pharmacology based investigation into the bioactive ingredients and molecular mechanisms of QingFeiPaiDu Decoction treating COVID-19

2020 ◽  
Author(s):  
Yan Liu ◽  
Lewen Xiong ◽  
YanYu Wang ◽  
Mengxiong Luo ◽  
Longfei Zhang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Cellular Response ◽  
Molecular Mechanisms ◽  
Endocrine Resistance ◽  
Hydrolytic Enzyme ◽  
Nitrogen Compound ◽  
Network Pharmacology ◽  
Signal Pathways ◽  
Protein Kinase Activity ◽  
Active Ingredients

Abstract Objective: To study the QingFeiPaiDu Decoction (QFPDD) in the treatment of Corona Virus Disease 2019 (COVID-19) bioactive ingredient and its potential mechanism. Methods: Combined with the clinical symptoms of COVID-19 patients, a "component-target-disease" network model was constructed based on the network pharmacology method, and potential active components, targets and molecular mechanisms of QFPDD for COVID-19 were screened out through topology parameter analysis.Results: We collected 376 active ingredients of QFPDD from the database, and 18833 potential anti-novel coronaviruses (SARS-CoV-2) targets were analyzed and screened. The principal targets involved PIK3CA, PIK3R1, APP, SRC, MAPK1, MAPK3, AKT1, HSP90AA1, EP300, CDK1, etc. We obtained 574 GO entries by Gene Ontology enrichment analysis and obtained 214 signal pathways with P<0.05 by KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis. Among them, the antiviral biological processes of QFPDD included a cellular response to nitrogen compound, protein kinase activity, membrane raft, etc. Pathways involved in the regulation include Pathways in cancer, Endocrine resistance, PI3K-Akt signaling pathway, Proteoglycans in cancer, etc. Molecular docking results showed that the core ingredients of QFPDD have a better affinity to the 2019-nCoV 3CL hydrolytic enzyme (Mpro) and angiotensin-converting enzyme 2 (ACE2). Conclusion: Through network pharmacology research and molecular docking verification, this paper preliminarily explored the potential molecular mechanism and relevant active ingredients of QFPDD playing an anti-SARS-CoV-2 role, providing a reference for the further development and utilization of QFPDD and the development of new specific antiviral drugs.

scholarly journals Prediction of the Active Ingredients, Potential Targets, and Signaling Pathways in ZaoRenDiHuang Capsules for Treatment of Insomnia Based on Network Analysis and Molecular Docking

2020 ◽  
Author(s):  
De Jin ◽  
Jinghua Zhang ◽  
Yuqing Zhang ◽  
Xuedong AN ◽  
Shenghui Zhao ◽  
...  
Keyword(s):  
Data Mining ◽  
Molecular Docking ◽  
Network Analysis ◽  
Signaling Pathways ◽  
Signaling Pathway ◽  
Topological Analysis ◽  
Docking Simulation ◽  
Network Pharmacology ◽  
Receptor Interaction ◽  
Active Ingredients

Abstract Background：Insomnia is a major global public health issue with a high incidence, which presents a significant economic burden. Importantly, insomnia is often accompanied by a myriad of symptoms during the daytime, the most common being insomnia dizziness, headache, malaise, fatigue, anxiety, and even contribute to several diseases. However, the action mode of multi-component and multi-target for Chinese medicine could be a promising therapy for insomnia. According to the previous research, the ZaoRenDiHuang (ZRDH) Capsules showed the noteworthy anti-insomnia effect. Up to now, active ingredients, potential targets, and signaling pathways and mechanism of action are not yet clear. In this study, network pharmacology was employed to elucidate the potential anti-insomnia mechanism of ZRDH.Methods：In this study, an integrated pharmacology approach was implemented, which involved evaluation of absorption, distribution, metabolism and excretion of ZRDH, data mining of the insomnia targets, protein-protein interaction (PPI) network analysis, enrichment analysis, and molecular docking simulation, to predict the bioactive components, potential targets, and molecular mechanism of ZRDH for insomnia.Results：In this work, 44 anti-insomnia components of ZRDH and 65 anti-insomnia targets of insomnia were filtrated through database mining. The Drug-Disease network was constructed andfive key components Jujuboside A, Schizandrin A, Schizandrin C, Schizandrin B, and Spinosin, were further obtained. Sixty-five key targets were identified by topological analysis. Sequential studies turned out, NMURl, CAlCR, GABA, TAER2, ORDS, CYS1TR2, HTR1B, TLR4 were the common key targets. Docking studies indicated that the bioactive compounds could stably bind the pockets of target proteins. The findings of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation suggested that the Neuroactive ligand−receptor interaction, Serotonergic synapse CAMP signaling pathway, HIF−1a signaling pathway, Toll−like receptor signaling pathway, anti-insomnia through data mining and network analysis.Conclusion: In sunmmary, potential mechanisms involved in ZRDH treatment for insomnia involves multiple components and multiple target points as well as multiple pathways. These findings may offer a profile for further investigations of the anti-fibrotic mechanism of ZRDH.

scholarly journals Insights into the molecular mechanisms of Huangqi decoction on liver fibrosis via computational systems pharmacology approaches

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Biting Wang ◽  
Zengrui Wu ◽  
Weihua Li ◽  
Guixia Liu ◽  
Yun Tang
Keyword(s):  
Molecular Docking ◽  
Liver Fibrosis ◽  
Molecular Mechanisms ◽  
Docking Simulation ◽  
Chinese Herbs ◽  
Network Pharmacology ◽  
Bipartite Network ◽  
Metabolomics Data ◽  
Metabolic Products ◽  
Compound Target

Abstract Background The traditional Chinese medicine Huangqi decoction (HQD) consists of Radix Astragali and Radix Glycyrrhizae in a ratio of 6: 1, which has been used for the treatment of liver fibrosis. In this study, we tried to elucidate its action of mechanism (MoA) via a combination of metabolomics data, network pharmacology and molecular docking methods. Methods Firstly, we collected prototype components and metabolic products after administration of HQD from a publication. With known and predicted targets, compound-target interactions were obtained. Then, the global compound-liver fibrosis target bipartite network and the HQD-liver fibrosis protein–protein interaction network were constructed, separately. KEGG pathway analysis was applied to further understand the mechanisms related to the target proteins of HQD. Additionally, molecular docking simulation was performed to determine the binding efficiency of compounds with targets. Finally, considering the concentrations of prototype compounds and metabolites of HQD, the critical compound-liver fibrosis target bipartite network was constructed. Results 68 compounds including 17 prototype components and 51 metabolic products were collected. 540 compound-target interactions were obtained between the 68 compounds and 95 targets. Combining network analysis, molecular docking and concentration of compounds, our final results demonstrated that eight compounds (three prototype compounds and five metabolites) and eight targets (CDK1, MMP9, PPARD, PPARG, PTGS2, SERPINE1, TP53, and HIF1A) might contribute to the effects of HQD on liver fibrosis. These interactions would maintain the balance of ECM, reduce liver damage, inhibit hepatocyte apoptosis, and alleviate liver inflammation through five signaling pathways including p53, PPAR, HIF-1, IL-17, and TNF signaling pathway. Conclusions This study provides a new way to understand the MoA of HQD on liver fibrosis by considering the concentrations of components and metabolites, which might be a model for investigation of MoA of other Chinese herbs.

Sign in / Sign up

Export Citation Format

Share Document