Discussion on the Mechanism of Lithospermum erythrorhizon in the Treatment of Cervical Cancer Based on Network Pharmacology and Molecular Docking Technology

2021 ◽  
Vol 7 (5) ◽  
pp. 3927-3933
Author(s):  
Qiong Yan ◽  
Fangwu Ye
Keyword(s):  
Cervical Cancer ◽  
Molecular Docking ◽  
Drug Targets ◽  
Target Genes ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Drug Candidate ◽  
Pathway Enrichment Analysis ◽  
Active Ingredients ◽  
Stable Conformation

Objective To explore the “multi-component, multi-target, multi-pathway” mechanism of Lithospermum erythrorhizonagairtst cervical cancer. Methods The active ingredients and corresponding targets were screened through TCMSP, PubChem and SwissTargetPrediction databases. The GeneCarts platform was used to collect cervical cancer-related genes, and the intersection of drug targets and cervical cancer targets was analyzed. Use STRING to analyze protein interaction network, use Cytoscape software to construct component-target and core target interaction network, perform KEGG pathway enrichment analysis on core target genes, and conduct molecular docking verification.Results After screening, 12 main active ingredients of comfrey (including Shikonin A, 1-methoxyacetylshikonin, Shikonin B, etc.) and 35 key targets related to comfrey and cervical cancer were obtained (including ESR1, SRC, MMP9, PTGS2, etc.). And these genes were mainly enriched in 39 signaling pathways such as PI3K-Akt and estrogen. Molecular docking reminder that Lithospermum A has a higher affinity with ESR1, and Lithospermum B can form a stable conformation with SRC, MMP9, and PTGS2. Conclusion Lithospermum erythrorhizon is a potential drug candidate for the treatment of cervical cancer. It can treat cervical cancer through multi-component, multi-target, and multi-channel action.

scholarly journals Network Pharmacology and Molecular Docking Suggest the Mechanism for Biological Activity of Rosmarinic Acid

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Minglong Guan ◽  
Lan Guo ◽  
Hengli Ma ◽  
Huimei Wu ◽  
Xiaoyun Fan
Keyword(s):  
Biological Activity ◽  
Molecular Docking ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Rosmarinic Acid ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis

Rosmarinic acid (RosA) is a natural phenolic acid compound, which is mainly extracted from Labiatae and Arnebia. At present, there is no systematic analysis of its mechanism. Therefore, we used the method of network pharmacology to analyze the mechanism of RosA. In our study, PubChem database was used to search for the chemical formula and the Chemical Abstracts Service (CAS) number of RosA. Then, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to evaluate the pharmacodynamics of RosA, and the Comparative Toxicogenomics Database (CTD) was used to identify the potential target genes of RosA. In addition, the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of target genes were carried out by using the web-based gene set analysis toolkit (WebGestalt). At the same time, we uploaded the targets to the STRING database to obtain the protein interaction network. Then, we carried out a molecular docking about targets and RosA. Finally, we used Cytoscape to establish a visual protein-protein interaction network and drug-target-pathway network and analyze these networks. Our data showed that RosA has good biological activity and drug utilization. There are 55 target genes that have been identified. Then, the bioinformatics analysis and network analysis found that these target genes are closely related to inflammatory response, tumor occurrence and development, and other biological processes. These results demonstrated that RosA can act on a variety of proteins and pathways to form a systematic pharmacological network, which has good value in drug development and utilization.

scholarly journals Potential Molecular Target Prediction and Docking Verification of Hua-Feng-Dan in Stroke Based on Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ping Yang ◽  
Haifeng He ◽  
Shangfu Xu ◽  
Ping Liu ◽  
Xinyu Bai
Keyword(s):  
Molecular Docking ◽  
Signaling Pathways ◽  
Target Genes ◽  
Target Prediction ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Core Network ◽  
Active Ingredients ◽  
Protein Protein Interaction ◽  
Highly Correlated

Objective. Hua-Feng-Dan (HFD) is a Chinese medicine for stroke. This study is to predict and verify potential molecular targets and pathways of HFD against stroke using network pharmacology. Methods. The TCMSP database and TCMID were used to search for the active ingredients of HFD, and GeneCards and DrugBank databases were used to search for stroke-related target genes to construct the “component-target-disease” by Cytoscape 3.7.1, which was further filtered by MCODE to build a core network. The STRING database was used to obtain interrelationships by topology and to construct a protein-protein interaction network. GO and KEGG were carried out through DAVID Bioinformatics. Autodock 4.2 was used for molecular docking. BaseSpace was used to correlate target genes with the GEO database. Results. Based on OB ≥ 30% and DL ≥ 0.18, 42 active ingredients were extracted from HFD, and 107 associated targets were obtained. PPI network and Cytoscape analysis identified 22 key targets. GO analysis suggested 51 cellular biological processes, and KEGG suggested that 60 pathways were related to the antistroke mechanism of HFD, with p53, PI3K-Akt, and apoptosis signaling pathways being most important for HFD effects. Molecular docking verified interactions between the core target (CASP8, CASP9, MDM2, CYCS, RELA, and CCND1) and the active ingredients (beta-sitosterol, luteolin, baicalein, and wogonin). The identified gene targets were highly correlated with the GEO biosets, and the stroke-protection effects of Xuesaitong in the database were verified by identified targets. Conclusion. HFD could regulate the symptoms of stroke through signaling pathways with core targets. This work provided a bioinformatic method to clarify the antistroke mechanism of HFD, and the identified core targets could be valuable to evaluate the antistroke effects of traditional Chinese medicines.

scholarly journals Molecular mechanism underlying the hypolipidemic effect of Shanmei Capsule based on network pharmacology and molecular docking

2021 ◽  
Vol 29 ◽  
pp. 239-256
Author(s):  
Qian Wang ◽  
Lijing Du ◽  
Jiana Hong ◽  
Zhenlin Chen ◽  
Huijian Liu ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Signaling Pathway ◽  
Molecular Mechanism ◽  
Kegg Pathway ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Signal Pathways ◽  
Hypolipidemic Effect ◽  
Active Ingredients ◽  
Pathway Enrichment

BACKGROUND: Shanmei Capsule is a famous preparation in China. However, the related mechanism of Shanmei Capsule against hyperlipidemia has yet to be revealed. OBJECTIVE: To elucidate underlying mechanism of Shanmei Capsule against hyperlipidemia through network pharmacology approach and molecular docking. METHODS: Active ingredients, targets of Shanmei Capsule as well as targets for hyperlipidemia were screened based on database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed via Database for Annotation, Visualization, and Integrated Discovery (DAVID) 6.8 database. Ingredient-target-disease-pathway network was visualized utilizing Cytoscape software and molecular docking was performed by Autodock Vina. RESULTS: Seventeen active ingredients in Shanmei Capsule were screened out with a closely connection with 34 hyperlipidemia-related targets. GO analysis revealed 40 biological processes, 5 cellular components and 29 molecular functions. A total of 15 signal pathways were enriched by KEGG pathway enrichment analysis. The docking results indicated that the binding activities of key ingredients for PPAR-α are equivalent to that of the positive drug lifibrate. CONCLUSIONS: The possible molecular mechanism mainly involved PPAR signaling pathway, Bile secretion and TNF signaling pathway via acting on MAPK8, PPARγ, MMP9, PPARα, FABP4 and NOS2 targets.

scholarly journals Elucidating the Mechanisms of Hugan Buzure Granule in the Treatment of Liver Fibrosis via Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yi Zhu ◽  
Ming Qiao ◽  
Jianhua Yang ◽  
Junping Hu
Keyword(s):  
Liver Fibrosis ◽  
Rna Polymerase Ii ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Docking Simulation ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Transcription Activator ◽  
Active Ingredients ◽  
Oxidoreductase Activity

Objective. To holistically explore the latent active ingredients, targets, and related mechanisms of Hugan buzure granule (HBG) in the treatment of liver fibrosis (LF) via network pharmacology. Methods. First, we collected the ingredients of HBG by referring the TCMSP server and literature and filtered the active ingredients though the criteria of oral bioavailability ≥30% and drug-likeness index ≥0.18. Second, herb-associated targets were predicted and screened based on the BATMAN-TCM and SwissTargetPrediction platforms. Candidate targets related to LF were collected from the GeneCards and OMIM databases. Furthermore, the overlapping target genes were used to construct the protein-protein interaction network and “drug-compound-target-disease” network. Third, GO and KEGG pathway analyses were carried out to illustrate the latent mechanisms of HBG in the treatment of LF. Finally, the combining activities of hub targets with active ingredients were further verified based on software AutoDock Vina. Results. A total of 25 active ingredients and 115 overlapping target genes of HBG and LF were collected. Besides, GO enrichment analysis exhibited that the overlapping target genes were involved in DNA-binding transcription activator activity, RNA polymerase II-specific, and oxidoreductase activity. Simultaneously, the key molecular mechanisms of HBG against LF were mainly involved in PI3K-AKT, MAPK, HIF-1, and NF-κB signaling pathways. Also, molecular docking simulation demonstrated that the key targets of HBG for antiliver fibrosis were IL6, CASP3, EGFR, VEGF, and MAPK. Conclusion. This work validated and predicted the underlying mechanisms of multicomponent and multitarget about HBG in treating LF and provided a scientific foundation for further research.

scholarly journals Integrating Network Pharmacology with Molecular Docking to Unravel the Active Compounds and Potential Mechanism of Simiao Pill Treating Rheumatoid Arthritis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Mengshi Tang ◽  
Xi Xie ◽  
Pengji Yi ◽  
Jin Kang ◽  
Jiafen Liao ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Molecular Docking ◽  
Signaling Pathway ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Binding Activity ◽  
New Combination ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Potential Mechanism

Objective. To explore the main components and unravel the potential mechanism of simiao pill (SM) on rheumatoid arthritis (RA) based on network pharmacological analysis and molecular docking. Methods. Related compounds were obtained from TCMSP and BATMAN-TCM database. Oral bioavailability and drug-likeness were then screened by using absorption, distribution, metabolism, and excretion (ADME) criteria. Additionally, target genes related to RA were acquired from GeneCards and OMIM database. Correlations about SM-RA, compounds-targets, and pathways-targets-compounds were visualized through Cytoscape 3.7.1. The protein-protein interaction (PPI) network was constructed by STRING. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed via R packages. Molecular docking analysis was constructed by the Molecular Operating Environment (MOE). Results. A total of 72 potential compounds and 77 associated targets of SM were identified. The compounds-targets network analysis indicated that the 6 compounds, including quercetin, kaempferol, baicalein, wogonin, beta-sitosterol, and eugenol, were linked to ≥10 target genes, and the 10 target genes (PTGS1, ESR1, AR, PGR, CHRM3, PPARG, CHRM2, BCL2, CASP3, and RELA) were core target genes in the network. Enrichment analysis indicated that PI3K-Akt, TNF, and IL-17 signaling pathway may be a critical signaling pathway in the network pharmacology. Molecular docking showed that quercetin, kaempferol, baicalein, and wogonin have good binding activity with IL6, VEGFA, EGFR, and NFKBIA targets. Conclusion. The integrative investigation based on bioinformatics/network topology strategy may elaborate on the multicomponent synergy mechanisms of SM against RA and provide the way out to develop new combination medicines for RA.

scholarly journals A Network Pharmacology-Based Study on the Anti-Lung Cancer Effect of Dipsaci Radix

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Jiayan Wu ◽  
Shengkun Hong ◽  
Xiankuan Xie ◽  
Wangmi Liu
Keyword(s):  
Lung Cancer ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Active Compounds

Objective. Dipsaci Radix (DR) has been used to treat fracture and osteoporosis. Recent reports have shown that myeloid cells from bone marrow can promote the proliferation of lung cancer. However, the action and mechanism of DR has not been well defined in lung cancer. The aim of the present study was to define molecular mechanisms of DR as a potential therapeutic approach to treat lung cancer. Methods. Active compounds of DR with oral bioavailability ≥30% and drug-likeness index ≥0.18 were obtained from the traditional Chinese medicine systems pharmacology database and analysis platform. The potential target genes of the active compounds and bone were identified by PharmMapper and GeneCards, respectively. The compound-target network and protein-protein interaction network were built by Cytoscape software and Search Tool for the Retrieval of Interacting Genes webserver, respectively. GO analysis and pathway enrichment analysis were performed using R software. Results. Our study demonstrated that DR had 6 active compounds, including gentisin, sitosterol, Sylvestroside III, 3,5-Di-O-caffeoylquinic acid, cauloside A, and japonine. There were 254 target genes related to these active compounds as well as to bone. SRC, AKT1, and GRB2 were the top 3 hub genes. Metabolisms and signaling pathways associated with these hub genes were significantly enriched. Conclusions. This study indicated that DR could exhibit the anti-lung cancer effect by affecting multiple targets and multiple pathways. It reflects the traditional Chinese medicine characterized by multicomponents and multitargets. DR could be considered as a candidate for clinical anticancer therapy by regulating bone physiological functions.

scholarly journals Network Pharmacology-Based Study on the Mechanism of Aloe Vera for Treating Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jing Xie ◽  
Jun Wu ◽  
Sihui Yang ◽  
Huaijun Zhou
Keyword(s):  
Molecular Docking ◽  
Drug Targets ◽  
Aloe Vera ◽  
Beta Carotene ◽  
Network Pharmacology ◽  
Potential Mechanism ◽  
Active Ingredients ◽  
Protein Protein Interaction ◽  
Target Network ◽  
Molecule Docking

Background. Aloe vera has long been considered an anticancer herb in different parts of the world. Objective. To explore the potential mechanism of aloe vera in the treatment of cancer using network pharmacology and molecule docking approaches. Methods. The active ingredients and corresponding protein targets of aloe vera were identified from the TCMSP database. Targets related to cancer were obtained from GeneCards and OMIM databases. The anticancer targets of aloe vera were obtained by intersecting the drug targets with the disease targets, and the process was presented in the form of a Venn plot. These targets were uploaded to the String database for protein-protein interaction (PPI) analysis, and the result was visualized by Cytoscape software. Go and KEGG enrichment were used to analyze the biological process of the target proteins. Molecular docking was used to verify the relationship between the active ingredients of aloe vera and predicted targets. Results. By screening and analyzing, 8 active ingredients and 174 anticancer targets of aloe vera were obtained. The active ingredient-anticancer target network constructed by Cytoscape software indicated that quercetin, arachidonic acid, aloe-emodin, and beta-carotene, which have more than 4 gene targets, may play crucial roles. In the PPI network, AKT1, TP53, and VEGFA have the top 3 highest values. The anticancer targets of aloe vera were mainly involved in pathways in cancer, prostate cancer, bladder cancer, pancreatic cancer, and non-small-cell lung cancer and the TNF signaling pathway. The results of molecular docking suggested that the binding ability between TP53 and quercetin was the strongest. Conclusion. This study revealed the active ingredients of aloe vera and the potential mechanism underlying its anticancer effect based on network pharmacology and provided ideas for further research.

scholarly journals Exploration of the mechanism of Ziyin Tongluo Formula on preventing and treating postmenopausal osteoporosis based on the network pharmacology and molecular docking

2020 ◽  
Author(s):  
Rong-Bin Chen ◽  
Ying-Dong Yang ◽  
Kai Sun ◽  
Shan Liu ◽  
Wei Guo ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Postmenopausal Osteoporosis ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Active Ingredients ◽  
Active Compounds ◽  
Core Proteins ◽  
Key Genes

Abstract Background: Postmenopausal osteoporosis (PMOP) is a global chronic and metabolic bone disease, which poses huge challenges to individuals and society. Ziyin Tongluo Formula (ZYTLF) has been proved effective in the treatment of PMOP. However, the material basis and mechanism of ZYLTF against PMOP have not been thoroughly elucidated.Methods: Online databases were used to identify the active ingredients of ZYTLF and corresponding putative targets. Genes associated with PMOP were mined, and then mapped with the putative targets to obtain overlapping genes. Multiple networks were constructed and analyzed, from which the key genes were selected. The key genes were imported to the DAVID database to performs GO and KEGG pathway enrichment analysis. Finally, AutoDock Tools and other software were used for molecular docking of core compounds and key proteins. Results: Ninety-two active compounds of ZYTLF corresponded to 243 targets, with 129 target genes interacting with PMOP, and 50 key genes were selected. Network analysis showed the top 5 active ingredients including quercetin, kaempferol, luteolin, scutellarein, and formononetin., and the top 50 key genes such as VEGFA, MAPK8, AKT1, TNF, ESR1. Enrichment analysis uncovered two significant types of KEGG pathways in PMOP, hormone-related signaling pathways (estrogen , prolactin, and thyroid hormone signaling pathway) and inflammation-related pathways (TNF, PI3K-Akt, and MAPK signaling pathway). Moreover, molecular docking analysis verified that the main active compounds were tightly bound to the core proteins, further confirming the anti-PMOP effects. Conclusions: Based on network pharmacology and molecular docking technology, this study initially revealed the mechanisms of ZYTLF on PMOP, which involves multiple targets and multiple pathways.

scholarly journals Mechanism of YuPingFeng in the Treatment of COPD Based on Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yunhong Yin ◽  
Jianyu Liu ◽  
Mengyu Zhang ◽  
Rui Li ◽  
Xiao Liu ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Signalling Pathways ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Herbal Formula ◽  
Pathway Enrichment ◽  
Topological Parameters

YuPingFeng (YPF) granules are a classic herbal formula extensively used in clinical practice in China for the treatment of COPD. However, the pathological mechanisms of YPF in COPD remain undefined. In the present research, a network pharmacology-based strategy was implemented to elucidate the underlying multicomponent, multitarget, and multipathway modes of action of YPF against COPD. First, we identified putative YPF targets based on TCMSP databases and constructed a network containing interactions between putative YPF targets and known therapeutic targets of COPD. Next, two topological parameters, “degree” and “closeness,” were calculated to identify target genes in the network. The major hubs were imported to the MetaCore database for pathway enrichment analysis. In total, 23 YPF active ingredients and 83 target genes associated with COPD were identified. Through protein interaction network analysis, 26 genes were identified as major hubs due to their topological importance. GO and KEGG enrichment analysis results revealed YPF to be mainly associated with the response to glucocorticoids and steroid hormones, with apoptotic and HIF-1 signalling pathways being dominant and correlative pathways. The promising utility of YPF in the treatment of COPD has been demonstrated by a network pharmacology approach.

scholarly journals Exploring the Possible Mechanism and Drug Targets of Huang-Qi-Gui-Zhi-Wu-Wu Decoction for the Treatment of Chemotherapy-Induced Peripheral Neuropathy on Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jia-lin Gu ◽  
Guo-li Wei ◽  
Yu-zhu Ma ◽  
Jin-zhi Zhang ◽  
Yi Ji ◽  
...  
Keyword(s):  
Peripheral Neuropathy ◽  
Inflammatory Response ◽  
Nerve Injury ◽  
Drug Targets ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Common Side Effect ◽  
Pathway Enrichment Analysis ◽  
Herbal Formula ◽  
Active Components

Chemotherapy-induced peripheral neuropathy (CIPN) is a common side effect of anticancer treatment, which may influence its successful completion. The Huang-Qi-Gui-Zhi-Wu-Wu decoction (HQGZWWD) has been widely used to treat CIPN in China although the pharmacological mechanisms involved have not been clarified. Using the network pharmacology approach, this study investigated the potential pathogenesis of CIPN and the therapeutic mechanisms exerted by the HQGZWWD herbal formula in CIPN. The targets of HQGZWWD were identified using traditional Chinese medicine (TCM) databases (TCMSP and ETCM) and prediction platforms (PharmMapper and TargetNet), and the genes of CIPN were collected by DisGeNET, GeneCards, and literature search. The common target interaction network between herbal formula and diseases was constructed by using Cytoscape. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to reveal the mechanism and efficacy of HQGZWWD in the treatment of CIPN. A total of 153 CIPN-related genes were screened, and a protein-protein interaction (PPI) network with 96 nodes and 424 edges was constructed. Sixty-three active components were retrieved from HQGZWWD, with a herb-composite compound-target network including 748 nodes and 5448 edges. Forty-one targets belong to the above two networks. The analysis of network results and literature review shows that the main pathological processes of CIPN may be the inflammatory response and nerve injury, and HQGZWWD plays a therapeutic role in CIPN by regulating inflammatory response and repairing nerve injury, thus verifying the reliable efficacy of this herbal formula. In addition, we found two new potential therapeutic targets (CDK7 and GSTM2) warranting further investigation. This study fully illustrates that TCM has the characteristics of a multicompound, multitarget, and multipathway treatment, which is of great significance to study the curative effect of herbal formulations.

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