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

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.

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Longchuan Wu ◽  
Yu Chen ◽  
Jiao Yi ◽  
Yi Zhuang ◽  
Lei Cui ◽  
...  

Objective. To explore the mechanism of action of Bu-Fei-Yi-Shen formula (BFYSF) in treating chronic obstructive pulmonary disease (COPD) based on network pharmacology analysis and molecular docking validation. Methods. First of all, the pharmacologically active ingredients and corresponding targets in BFYSF were mined by the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the analysis platform, and literature review. Subsequently, the COPD-related targets (including the pathogenic targets and known therapeutic targets) were identified through the TTD, CTD, DisGeNet, and GeneCards databases. Thereafter, Cytoscape was employed to construct the candidate component-target network of BFYSF in the treatment of COPD. Moreover, the cytoHubba plug-in was utilized to calculate the topological parameters of nodes in the network; then, the core components and core targets of BFYSF in the treatment of COPD were extracted according to the degree value (greater than or equal to the median degree values for all nodes in the network) to construct the core network. Further, the Autodock vina software was adopted for molecular docking study on the core active ingredients and core targets, so as to verify the above-mentioned network pharmacology analysis results. Finally, the Omicshare database was applied in enrichment analysis of the biological functions of core targets and the involved signaling pathways. Results. In the core component-target network of BFYSF in treating COPD, there were 30 active ingredients and 37 core targets. Enrichment analysis suggested that these 37 core targets were mainly involved in the regulation of biological functions, such as response to biological and chemical stimuli, multiple cellular life processes, immunity, and metabolism. Besides, multiple pathways, including IL-17, Toll-like receptor (TLR), TNF, and HIF-1, played certain roles in the effect of BFYSF on treating COPD. Conclusion. BFYSF can treat COPD through the multicomponent, multitarget, and multipathway synergistic network, which provides basic data for intensively exploring the mechanism of action of BFYSF in treating COPD.


2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ping Yang ◽  
Haifeng He ◽  
Shangfu Xu ◽  
Ping Liu ◽  
Xinyu Bai

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.


2021 ◽  
Author(s):  
Lu Sun ◽  
Zining Wang ◽  
Jian Li ◽  
Li Xu ◽  
Xiaoou Xue

Abstract Background: Primary dysmenorrhea(PD)is the most common gynecologic disorder.Despite the prevalence is high, it is often underdiagnosed,undertreated and normalized even by patients themselves. Guizhi Fuling Formula (GFF) is experientially used for the treatment of PD in a long time. Therefore, the efficiency and potential mechanism are waiting to identify.Methods: We adopted network pharmacology integrated molecular docking strategy in this study.Based on published literatures, the relative compounds of GFF were selected preliminarily. Secondly, the putative targets of PD were obtained by wide-searching DisGeNET, OMIM, Drugbank and GeneCards databases.With protein-protein interaction(PPI) analysis, GO and KEGG pathway enrichment analysis and molecular docking ,we systematically evaluated the relationship of herb ingredients and disease targets.Results: The results showed that 30 ingredients of GFF and 43 hub targets made a difference.Under the further analysis,8 targets(EGFR,AKT1,PTGS2,TNF,ESR1,AHR,CTNNB1,CXCL8) were recognized as key therapeutic targets with excellent binding. The enrichment analyses indicated that the GFF had the potential to influence varieties of biological pathways, especially the pathways in cancer and steroid hormone biosynthesis, which play an important part in the pathogenesis of primary dysmenorrhea.Conclusion: GFF influenced primary dysmenorrhea through the synergistic effect of multiple components, multiple targets, and multiple pathways.This study predictedthe potential mechanism, hope that could made contribution for clinical application and scientific research.


2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiao-Li Chen ◽  
Cheng Tang ◽  
Qing-Ling Xiao ◽  
Zhong-Hua Pang ◽  
Dan-Dan Zhou ◽  
...  

Objective. This study aimed to clarify the mechanism of Fei-Xian formula (FXF) in the treatment of pulmonary fibrosis based on network pharmacology analysis combined with molecular docking validation. Methods. Firstly, ingredients in FXF with pharmacological activities, together with specific targets, were identified based on the BATMA-TCM and TCMSP databases. Then, targets associated with pulmonary fibrosis, which included pathogenic targets as well as those known therapeutic targets, were screened against the CTD, TTD, GeneCards, and DisGeNet databases. Later, Cytoscape was employed to construct a candidate component-target network of FXF for treating pulmonary fibrosis. In addition, for nodes within the as-constructed network, topological parameters were calculated using CytoHubba plug-in, and the degree value (twice as high as the median degree value for all the nodes) was adopted to select core components as well as core targets of FXF for treating pulmonary fibrosis, which were subsequently utilized for constructing the core network. Furthermore, molecular docking study was carried out on those core active ingredients together with the core targets using AutoDock Vina for verifying results of network pharmacology analysis. At last, OmicShare was employed for enrichment analysis of the core targets. Results. Altogether 12 active ingredients along with 13 core targets were identified from our constructed core component-target network of FXF for the treatment of pulmonary fibrosis. As revealed by enrichment analysis, the 13 core targets mostly concentrated in regulating biological functions, like response to external stimulus (from oxidative stress, radiation, UV, chemical substances, and virus infection), apoptosis, cell cycle, aging, immune process, and protein metabolism. In addition, several pathways, like IL-17, AGE-RAGE, TNF, HIF-1, PI3K-AKT, NOD-like receptor, T/B cell receptor, and virus infection-related pathways, exerted vital parts in FXF in the treatment of pulmonary fibrosis. Conclusions. FXF can treat pulmonary fibrosis through a “multicomponent, multitarget, and multipathway” mean. Findings in this work lay foundation for further exploration of the FXF mechanism in the treatment of pulmonary fibrosis.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bin Wang ◽  
Yang Liu ◽  
Jianhui Sun ◽  
Nailin Zhang ◽  
Xiaojia Zheng ◽  
...  

Introduction. Network pharmacology is in line with the holistic characteristics of TCM and can be used to elucidate the complex network of interactions between disease-specific genes and compounds in TCM herbal medicines. Here, we investigate the pharmacological mechanism of Xiaokui Jiedu decoction (XJD) for the treatment of ulcerative colitis (UC). Methods. The Computational Systems Biology Laboratory Platform (TCMSP) database was searched and screened for the active ingredients of all drugs in XJD. The Uniport database was used to retrieve possible gene targets for the therapeutic effects of XJD. GeneCards, PharmGKB, TTD, and OMIM databases were used to retrieve XJD-related gene targets. A herb-compound-protein network and a protein-protein interaction (PPI) network were constructed, and hub genes were screened for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, molecular docking was performed to validate the interrelationship between disease target proteins and active drug components. Results. A total of 135 XJD potential action targets, 5097 UC-related gene targets, and 103 XJD-UC intersection gene targets were screened. The hub gene targets of XJD that exert therapeutic effects on UC are RB1, MAPK1, TP53, JUN, NR3C1, MAPK3, and ESR1. GO enrichment analysis showed 741 biofunctional enrichments, and KEGG enrichment analysis showed 124 related pathway enrichments. Molecular docking showed that the active components of XJD (β-sitosterol, kaempferol, formononetin, quercetin, and luteolin) showed good binding activities to five of the six hub gene targets. Discussion. The active ingredients of XJD (β-sitosterol, kaempferol, formononetin, quercetin, and luteolin) may regulate the inflammatory and oxidative stress-related pathways of colon cells during the course of UC by binding to the hub gene targets. This may be a potential mechanism of XJD in the treatment of UC.


2021 ◽  
Vol 41 (2) ◽  
Author(s):  
Lin Xu ◽  
Jiaqi Zhang ◽  
Yifan Wang ◽  
Zedan Zhang ◽  
Fengyun Wang ◽  
...  

Abstract Background: Ge-Gen-Qin-Lian Decoction (GGQLD), a traditional Chinese medicine (TCM) formula, has been widely used for ulcerative colitis (UC) in China, but the pharmacological mechanisms remain unclear. This research was designed to clarify the underlying pharmacological mechanism of GGQLD against UC. Method: In this research, a GGQLD-compound-target-UC network was constructed based on public databases to clarify the relationship between active compounds in GGQLD and potential targets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were performed to investigate biological functions associated with potential targets. A protein–protein interaction network was constructed to screen and evaluate hub genes and key active ingredients. Molecular docking was used to verify the activities of binding between hub targets and ingredients. Results: Finally, 83 potential therapeutic targets and 118 corresponding active ingredients were obtained by network pharmacology. Quercetin, kaempferol, wogonin, baicalein, and naringenin were identified as potential candidate ingredients. GO and KEGG enrichment analyses revealed that GGQLD had anti-inflammatory, antioxidative, and immunomodulatory effects. The effect of GGQLD on UC might be achieved by regulating the balance of cytokines (e.g., IL-6, TNF, IL-1β, CXCL8, CCL2) in the immune system and inflammation-related pathways, such as the IL-17 pathway and the Th17 cell differentiation pathway. In addition, molecular docking results demonstrated that the main active ingredient, quercetin, exhibited good affinity to hub targets. Conclusion: This research fully reflects the multicomponent and multitarget characteristics of GGQLD in the treatment of UC. Furthermore, the present study provided new insight into the mechanisms of GGQLD against UC.


2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shanshan Ding ◽  
Weihao Wang ◽  
Xujiao Song ◽  
Hao Ma

Background. Huangqi Gegen decoction (HGD), a Chinese herb formula, has been widely used to treat diabetic nephropathy in China, while the pharmacological mechanisms are still unclear. Therefore, the present study aims to explore the underlying mechanism of HGD for treating diabetic nephropathy (DN). Materials and Methods. Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), UniProt, and SwissTargetPrediction databases were used to search the active ingredients and potential targets of HGD. In addition, multiple disease-related databases were used to collect DN-related targets. Common targets of the protein-protein interaction (PPI) network were established using the STRING database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the DAVID database. At last, AutoDockVina was used to conduct molecular docking verification for the core components and targets. Results. A total of 27 active ingredients and 354 putative identified target genes were screened from HGD, of which 99 overlapped with the targets of DN and were considered potential therapeutic targets. Further analysis showed that the HGD activity of quercetin, formononetin, kaempferol, isorhamnetin, and beta-sitosterol ingredients is possible through VEGFA, IL6, TNF, AKT1, and TP53 targets involved in TNF, toll-like receptors, and MAPK-related pathways, which have anti-inflammatory, antiapoptosis, antioxidation, and autophagy effects, relieve renal fibrosis and renal cortex injury, and improve renal function, thus delaying the development of DN. The molecular docking results showed that quercetin, formononetin, kaempferol, isorhamnetin, beta-sitosterol had a good binding activity with VEGFA, IL6, TNF, AKT1, and TP53. Conclusion. This study demonstrated that HGD might take part in the treatment of DN through multicomponent, multitarget, and multichannel combined action.


2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Guilin Ren ◽  
Yu Zhong ◽  
Gang Ke ◽  
Xiaoli Liu ◽  
Huiting Li ◽  
...  

The active component-target network and protein-protein interaction network of Compound Anshen essential oil were constructed. The target functions and related pathways were analyzed to explore the mechanism of Compound Anshen essential oil in the treatment of insomnia. GC-MS was used to detect the chemical composition of Compound Anshen essential oil, and the TCMSP, STITCH, TTD, and DrugBank databases were searched to predict and screen the targets of Compound Anshen essential oil in the treatment of insomnia. Cytoscape software was used to construct the network diagrams of the active component-action target and protein-protein interaction networks, ClueGO software was used to analyze the GO enrichment and KEGG pathway of the target, and the systemsDock website database was used for molecular docking. The analysis of the network results showed that the activity of Compound Anshen essential oil mainly involves biological processes such as the phospholipase C-activating G protein-coupled receptor signaling pathway, response to ammonium ions, calcium ion transport into the cytosol, and chloride transport. The results of molecular docking showed that linalool, caryophyllene, dibutyl phthalate, (-)-4-terpineol, and (-)-α-terpineol have good binding activity with ADRB2, DRD2, ESR1, KCNH2, NR1H4, NR1I2, NR1I3, and TRPV1 targets. This study demonstrates the multicomponent, multitarget, and multichannel characteristics of Compound Anshen essential oil and provides a new therapeutic idea and method for further research on the mechanism of Compound Anshen essential oil in the treatment of insomnia.


2021 ◽  
Vol 7 (5) ◽  
pp. 3927-3933
Author(s):  
Qiong Yan ◽  
Fangwu Ye

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.


2020 ◽  
Author(s):  
Bo Xie ◽  
Haojie Lu ◽  
Jinhui Xu ◽  
Yebei Hu ◽  
Haixin Luo ◽  
...  

Abstract Background Network pharmacology is a new method of bioinformatics in exploring drug targets in recent 3 years. Hydroxychloroquine (HCQ) is a multi-targets drug that are clinically effective in rheumatoid arthritis (RA) but whose mechanism is not well understood. Methods The predicted targets of HCQ and the proteins related to RA were returned from databases. Followed by protein-protein interaction (PPI) network, the intersection of the two group of proteins was conducted. Furthermore, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment was used to analyse these proteins in a macro perspective. Finally, the candidate targets were verified by molecular docking. Results The results suggest that the efficacy of HCQ against RA is mainly associated with 4 targets of smoothened homolog (SMO), sphingosine kinase (SPHK) 1, SPHK2 and gatty-acid amide hydrolase (FAAH), with their related 3316 proteins’ network which regulate ErbB, HIF-1, NF-κB, FoxO, Chemokine, MAPK, PI3K/Akt pathways and so forth. Biological process are mainly concentrated in the regulation of cell activation, myeloid leukocyte activation, regulated exocytosis and so forth. Molecular docking analysis shows that hydrogen bonding and π-π stacking are the main forms of chemical force. Conclusions Our research provides protein targets affected by HCQ in the treatment of RA. SMO, SPHK1, SPHK2 and FAAH involving 3316 proteins become the multi-targets mechanism of HCQ in the treatment of RA. As well, the research also provides a new idea for introducing network pharmacology into the evaluation of the multi-target drugs in internal medicine.


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