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Table of Contents
Year : 2021  |  Volume : 7  |  Issue : 3  |  Page : 370-376

Network pharmacology study of yuebi plus banxia decoction in treating asthma

Chinese Medical College, Tianjin University of Traditional Chinese Medicine, Tianjin, China

Date of Submission29-Jun-2020
Date of Acceptance14-Sep-2020
Date of Web Publication9-Aug-2021

Correspondence Address:
Prof. Zheng Hao
Tianjin University of Traditional Chinese Medicine, Tianjin, 301617
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/wjtcm.wjtcm_18_21

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Objective: The objective of the study was to study the potential biological mechanism of Yuebi plus Banxia Decoction (YBD) in the treatment of asthma based on network pharmacology. Materials and Methods: Literature databases were used to collect information on the chemical components and pharmacokinetics of YBD as comprehensively as possible. According to pharmacokinetic information and effective ingredient screening criteria, effective chemical components of YBD were screened, and the target genes corresponding to the effective components were collected by the ligand prediction method. At the same time, literature databases including five disease target gene databases were used to collect asthma disease target genes. Then, the effective component target network of YBD and the asthma disease target network can be constructed using Cytoscape 3.2.1 software. The core targets of YBD for the treatment of asthma were screened according to topological analysis based on degree parameters. Through the analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway, the main mechanisms of YBD in treating asthma were found out. Results: A161 active ingredients of YBD and 136 core targets for the treatment of asthma were screened out. The effective signaling pathways of YBD in the treatment of asthma were mainly focused on ErbB, MAPK, Estrogen, PI3K-Akt, Neurotrophin, Hippo, HIF-1, TGF-, FoxO, Wnt, Chemoking, Toll-like receptor, vascular endothelial growth factor (VEGF), GnRH, and Notch. Conclusion: YBD has the characteristics of multiple targets and multiple pathways in the treatment of asthma. Its main biological mechanism is related to ErbB, MAPK, Estrogen, PI3K-Akt, neurotrophin, Hippo, HIF-1, TGF-, FoxO, Wnt, Chemoking, Toll-like receptor, VEGF, GnRH, and Notch.

Keywords: Asthma, network pharmacology, signaling pathway, Yuebi plus Banxia decoction

How to cite this article:
Song WJ, Hao Z. Network pharmacology study of yuebi plus banxia decoction in treating asthma. World J Tradit Chin Med 2021;7:370-6

How to cite this URL:
Song WJ, Hao Z. Network pharmacology study of yuebi plus banxia decoction in treating asthma. World J Tradit Chin Med [serial online] 2021 [cited 2022 Aug 8];7:370-6. Available from: https://www.wjtcm.net/text.asp?2021/7/3/370/323494

  Introduction Top

Asthma is a common chronic respiratory disease, which easily forms a vicious circle of “bronchitis/pneumonia-asthma,” resulting in irreversible changes in the airway and complications such as chronic obstructive pulmonary disease, pulmonary heart disease, and multiple organ failure.[1] Therefore, asthma has become a disease that seriously endangers human health. Epidemiological studies show that, in 2016, there were about 339 million patients with asthma worldwide, and about 1000 people died from asthma every day. Asthma is one of the top 20 reasons that make healthy people continue to live with unhealthy bodies for many years after being disabled, and asthma has become a global disease with a high burden of death and disability.[2] In China, the number of asthma cases in 2017 was about 30 million (ranking first in the world), and the number of deaths was 129,300 (ranking third in the world).[3] Therefore, reducing the mortality rate of asthma and improving the quality of life of patients is the current medical priority.

Modern medical treatment of asthma often uses antibiotics combined with bronchodilators and glucocorticoids. However, long-term large-scale use of these drugs can aggravate the pulmonary and intestinal microbiota disorder, increase the risk of infection, and induce or aggravate asthma, showing the limitations of modern medical treatment. Traditional Chinese medicine in China has the characteristics of multiple components, multiple targets, and multiple pathways of action. It can effectively regulate pulmonary and intestinal microbiota disorders, reduce airway inflammation, delay airway remodeling, improve lung function, reduce asthma recurrence, reduce mortality, and treat asthma.[4],[5],[6] Therefore, looking for ways to treat asthma from the perspective of traditional Chinese medicine, combined Chinese and Western medicine to treat asthma is the current medical development direction.

Yuebi plus Banxia Decoction (YBD) comes from “The Synopsis of the Golden Chamber,” which is the representative prescription of the medical sage Zhang Zhongjing for the treatment of the lung syndrome caused by drinking heat. The article says: “Coughing and breathing is lung swelling, and the person's clinical manifestations are wheezing, prolapsed eyes, and floating pulse. At this time, YBD is used as the main treatment prescription,” which points out that YBD can treat the typical manifestations of an acute attack of asthma such as severe coughing and wheezing, difficulty breathing, hyperventilation leading to extreme hypoxia, and bulging eyes like “frog eyes.” YBD is composed of six traditional Chinese medicines, namely, mahuang, shengjiang, banxia, shigao, gancao, and dazao, among which mahuang and shengjiang can release the lungs and relieves the surface, banxia can reduce phlegm and relieve asthma, shigao can clear lung heat and relieve stagnant heat, and gancao and dazao can invigorate the spleen and inhibit water drinking. The whole prescription can promote lung qi, eliminate asthma, clear away heat and reduce phlegm, and invigorate the spleen and stomach, which can effectively alleviate asthma symptoms, alleviate immune inflammation, and improve lung function.[5],[7],[8]

However, the mechanism of YBD in treating asthma cannot be fully explained. Combining the characteristics of traditional Chinese medicine with multiple components, multiple targets, and multiple ways of action, it is of great significance to study the mechanism of traditional Chinese medicine from the perspective of overall regulation of biological networks. Therefore, this study adopts the method of network pharmacology to explore the potential biological mechanism of YBD in the treatment of asthma.

  Materials and Methods Top

Screening of bioactive components in Yuebi plus Banxia decoction

Component data of the six Chinese medicines in YBD were obtained from recognized natural product databases including the Pharmacopoeia of the People's Republic of China (2015 edition), the TCMSP database (http://ibts.hkbu.edu.hk/LSP/tcmsp.php), National Population and Health Science Data Sharing Platform (http://cowork.cintcm.com/engine/search?channelid=58730), Taiwan Traditional Chinese Medicine Information Database (http://tcm.cmu.edu.tw/zh-tw/review-result.php?herbid=5114), and Pubchem database (https://pubchem.ncbi.nlm.nih.gov/), ZINC database (http://zinc.docking.org/). The component data included all drugs, chemical components, and pharmacokinetic information of the chemical components (namely, absorption, distribution, metabolism, and excretion, referred to as ADME). In addition, modern biomedical literature databases such as CNKI, Wanfang Database, VIP Database (VIP), and PubMed were also used to supplement other missing data. Finally, 821 components from YBD were collected including mahuang 230, gancao 280, shengjiang 64, dazao 126, banxia 116, and shigao 5.

Oral is the most common way of taking Chinese herbal medicine. Based on drug action pathways and ADME-related models, bioavailability (OB) and drug similarity (DL) are selected for the screening of bioactive component in YBD, which is widely recognized and plays an important role in pharmacodynamic research. The screening criteria are: OB ≥20% and DL ≥0.18.[9] Moreover, the missing biologically bioactive components were also manually entered if there are indeed literature reports. In this study, 199 bioactive components of YBD were screened including mahuang 8, gancao 114, shengjiang 4, dazao 47, banxia 21, and shigao 5.

Target prediction of bioactive components in Yuebi plus Banxia decoction

According to different drug-target prediction principles, target prediction technologies and methods can be divided into four types: (i) ligand-based prediction (chemical similarity search and pharmacophore model), (ii) receptor-based prediction (molecular docking), (iii) machine learning prediction (the database must have a clear correspondence between the drug and the target, and the target name must be standardized), and (iv) combined application prediction.[10] Based on our previous experience and considering the limitations of the experimental conditions for molecular docking and machine learning prediction, we chose the method based on ligand prediction in this study.[11],[12]

According to the method of ligand prediction, commonly used databases of Batman-TCM database (http://bionet.ncpsb.org/batman-tcm/), TCMSP database, Swiss Target Prediction (http://www.swisstargetprediction.ch/), STITCH database (http://stitch.embl.de/), Target-Prediction database (http://prediction.charite.de/index.php?site=chem-doodle_search_target), and PharmMapper database (http://lilab.ecust.edu.cn/pharmmapper/index.php) were adopted to collect target protein corresponding to the bioactive component. By applying Uniprot database (http://www.uniprot.org/), the target protein name could be converted into target gene name with the species limited into “Homo sapiens,”.

Target mining of asthma disease

Asthma disease target genes are obtained from the following five databases: (i) OMIM database (http://omim.org/, updated on January 3, 2018), (ii) TTD database (ttp://systemsdock.unit.osit.jp/iddp/home/index, updated on September 15, 2017h), (iii) GAD database (https://geneticassociationdb.nih.gov, updated on September 1, 2014), (iv) PharmGkb database (https://www.pharmgkb.org/, updated on December 28, 2017), and (v) Drugbank database (https://www.drugbank.ca, updated on December 20, 2017). In total, 542 asthma target genes were obtained.

PPI network construction

Proteins usually work with other molecules such as lipids, nucleic acids, and other proteins.[13] The PPI network is a network structure that physically connects different proteins[14] and can be analyzed using Bisogenet, a Cytoscape plugin. Bisogenet data sources come from six main PPI databases, namely, the Interacting Protein Database, Interaction Dataset Biology General Knowledge Base (BioGRID), Human Protein Reference Database, IntAct Molecular Interaction Database (IntAct), The Molecular Interaction Database, and the Biomolecular Interaction Network Database, which could ensure the accuracy of PPI network analysis.[15] Therefore, in this study, Bisogenet was used to construct the PPI network of YBD and asthma.

Topological analysis

Topological analysis is an important method to deeply study the internal physical relationships of big data networks and screen valuable nodes. Each node in the big data network is evaluated with eight typical central attributes: betweenness centrality (BC), closeness centrality (CC), degree centrality (DC), eigenvector centrality (EC), local average connectivity-based method (LAC), network centrality (NC), subgraph centrality (SC), and information centrality (IC).[16] Only six typical central attributes can be used for data calculation, namely, BC, CC, DC, EC, LAC, and NC. Based on previous research experience,[17],[18] this study selected CytoNCA (a Cytoscape plugin) for analysis and used “DC≥2 ×DC median” as the main screening criterion to screen the “shortest average distance (CC), shortest path (BC), and highest eigenvector score (EC), the largest average local connectivity (LAC), and the largest aggregation coefficient (NC)” core node, and finally obtain the core target.

Enrichment analysis of KEGG pathway

Enrichment analysis of KEGG signaling pathway is a common method to describe the mechanism of target action.[19] We used the DAVID database (https://david.ncifcrf.gov/) to analyze the KEGG signal pathway of the core target and set the condition for displaying the KEGG signal pathway to pV ≤ 0.05.[20] Finally, the results are visualized with Omicshare (http://www.omicshare.com)[19].

  Results Top

Yuebi plus Banxia decoction bioactive components-target genes network

This study found that 821 chemical components of YBD were obtained through literature search. 199 active ingredients were screened by OB value and DL value. After deleting duplicates, a total of 196 target genes were obtained based on ligand prediction methods including 101 mahuang target genes, 139 gancao target genes, 50 shengjiang target genes, 136 dazao target genes, 84 banxia target genes, and 36 shigao target genes. Since some bioactive components have no related target genes, the number of bioactive components that contain targets are finally included as mahuang 8, gancao 93, shengjiang 2, dazao 38, banxia 15, and shigao 5. It can be seen that the number of bioactive components included in network pharmacology studies is relatively small, as shown in [Figure 1].
Figure 1: Numbers of Yuebi plus Banxia Decoction components

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In order to further study the YBD bioactive components-target genes network from a holistic perspective, we constructed a visual network model through the application of Cytoscape 3.2.1 software Cytoscape 3.2.1 software (http://www.cytoscape.org, National Resource for Network Biology, USA). In the network, the green diamonds, purple diamonds, red diamonds, orange diamonds, blue diamonds, and sky blue diamonds represent the effective components of traditional Chinese medicine mahuang, gancao, shengjiang, dazao, banxia, and shigao, respectively, and the yellow diamonds represent target genes. The network includes 348 nodes and 3261 edges. It is pointed out that different drugs can share several identical targets, which may be a synergistic mechanism of traditional Chinese medicine, as shown in [Figure 2].
Figure 2: Yuebi plus Banxia Decoction bioactive components-target genes network

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Asthma-target genes network

Asthma is a polygenic genetic predisposition disease. Research on asthma-target genes network helps to further explore the mechanism of herb in treating asthma. In this study, a total of 542 target genes for asthma were obtained. And the Asthma-target genes network was constructed through Cytoscape 3.2.1 software. In the network, the yellow diamonds represent asthma diseases, and the blue diamonds represent target genes. The network includes 542 nodes and 541 edges, as shown in [Figure 3].
Figure 3: “Asthma-target genes” network

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Yuebi plus Banxia decoction-asthma PPI network and topological analysis

The PPI network helps to understand the interaction between various proteins. Therefore, we first used the PPI database in Bisogenet to construct YBD PPI network [Figure 4]a and asthma-related PPI network [Figure 4]b and then merged the two PPI networks to get the interactive PPI network of YBD and asthma [Figure 4]c.
Figure 4: YBD-asthma PPI network and topological analysis. (a) Interactive PPI network of YBD and asthma with 4880 nodes and 131 477 edges. (b) First central network evaluation with a core subset of 1176 nodes and 53 244 edges based on the median degree of 33. (c) Second central network evaluation with a core subset of 136 nodes and 3 107 edges based on “'BC' > 475.278, 'CC' > 0.443, 'DC' > 214.000, 'EC' > 0.018, 'LAC' > 17.035, and 'NC' > 148.457”. Blue diamonds, pink diamonds, and yellow diamonds represent other human proteins, component targets, and selected targets, respectively

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Second, in order to further study the core pharmacological mechanism of YBD in the treatment of asthma, we applied CytoNCA to perform topological analysis for the core PPI network screening, and the topological analysis screening criterion was “DC≥2×median DC” [Figure 4]d. In the end, 136 core targets for YBD treatment of asthma were obtained, which meet six screening criteria at the same time [Figure 4]e.

Enrichment analysis of the 136 core targets

In order to clarify the core mechanism of YBD in treating asthma, KEGG pathway enrichment analysis was performed on 136 core targets. Based on the analysis of the research results, we believe that key signaling pathways, such as ErbB, MAPK, Estrogen, PI3K-Akt, Neurotrophin, Hippo, HIF-1, TGF-, FoxO, Wnt, Chemoking, Toll-like receptor, vascular endothelial growth factor (VEGF), GnRH, and Notch, are the first 15 KEGG pathways, which may be the core pharmacological mechanism of YBD in the treatment of asthma and provide inspirations for the development of asthma treatment, as shown in [Figure 5].
Figure 5: Enrichment analysis of the 136 core targets

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  Discussion Top

Asthma is a common chronic disease, and the morbidity and mortality are increasing year by year. At present, the effect of simple Western medicine treatment is limited, and integrated Chinese and Western medicine treatment has become the trend of asthma treatment. Traditional Chinese medicine compound has the characteristics of multiple components, multiple targets, and multiple pathways, but most of the current research is still limited to the traditional research method of “single drug-single action pathway-single target,”[21] lacking the overall perspective of traditional Chinese medicine research on the overall system. Since 2007, Li[22] put forward the viewpoint of applying biological networks to study the theoretical framework of traditional Chinese medicine prescriptions. In the same year, Hopkins[23] first proposed the concept of “network pharmacology” and believed that network pharmacology would become the next step is to research and develop the main forms of drugs.[24] At present, network pharmacology is based on the big data theory by establishing a “disease-target-drug” network to systematically observe the systematic biological methods of drug intervention on disease network.[25] The “multicomponent, multitarget, multitarget-multi-level research strategy” of network pharmacology is consistent with the concept of the overall concept of Chinese herbal compound, which is widely used in scientific research in the field of Chinese medicine and has made significant progress. By applying the method of network pharmacology, the author conducts a systematic network analysis of YBD from an overall perspective and explores the mechanism of YBD in the treatment of asthma, in order to guide the next research.

With Traditional Chinese Medicine theory, asthma is often caused by spleen loss of healthy transportation. The fluid is turned into sputum, and the accumulation of water changes into sputum. Phlegm turbidity is blocked in the lungs, which leads to the malfunction of lung and then triggers asthma. Because of the current improvement in living standards and increased social pressure, people often addicted to alcohol, taste, anger, depression, and anger, which could lead to stagnation of phlegm drinking and heat syndrome. YBD comes from the medical sage Zhang Zhongjing's “The Golden Chamber,” which is a representative prescription for the treatment of asthma and heat syndrome. The article clearly describes the typical manifestations of asthma attacks and ends with “main treatment prescription,” indicating the importance and effectiveness of YBD in clinical application. Mahuang and shigao in the prescription can clear the lungs and clear the heat, banxia can reduce the phlegm and relieve the asthma, and gancao and dazao can strengthen the spleen, which could effectively reduce asthma symptoms, reduce immune inflammation, and improve lung function.[5],[8]

The author applied the network pharmacology method, with bioactive component screening, drug target prediction, PPT network analysis, and gene annotation enrichment analysis and found the main signal pathways of YBD in treating asthma such as ErbB, MAPK, Estrogen, PI3K-Akt, Neurotrophin, Hippo, HIF-1, TGF-, FoxO, Wnt, Chemoking, Toll-like receptor, VEGF, GnRH, and Notch. Studies have found that activation of the MAPK signaling pathway increases lung inflammation in asthma patients, and regulation of the MAPK signaling pathway can suppress airway inflammation and treat asthma.[26] ErbB signaling pathway promotes epithelial repair in asthma patients.[27] PI3K-Akt signaling pathway activation can increase airway epithelial–mesenchymal transition in early asthma patients,[28],[29] promote airway smooth muscle cell proliferation and hypertrophy,[30] and regulate PI3K-Akt signaling pathway to inhibit epithelial–mesenchymal transition and reduce airway remodeling may aid asthma treatment.[31] Wnt signaling pathway is the initial step to transduce extracellular signals into the intracellular response,[32] participate in lung development and repair processes,[33] and affect asthma airway remodeling.[34] VEGF signaling pathway is closely related to airway inflammation, airway remodeling, and asthma severity.[35] Regulating the expression of VEGF signaling pathway helps reduce the pathological changes of asthma.[36] GnRH analogs have a certain effect in the treatment of premenstrual asthma.[37] Notch signaling pathway plays an important role in lung homeostasis, injury, and repair, and regulating the expression of Notch signaling pathway can effectively treat asthma.[38] In summary, YBD may be able to treat asthma through ErbB, MAPK, Estrogen, PI3K-Akt, Neurotrophin, Hippo, HIF-1, TGF-, FoxO, Wnt, Chemoking, Toll-like receptor, VEGF, GnRH, Notch, and other signaling pathways, which is consistent with the results of this study.

However, there are still some limitations in this study. For example, the collection of active ingredients and targets of drugs is not comprehensive enough, and the results of network pharmacology research are lack of verification in animal experiments and clinical trials. Follow-up research can be improved using new detection methods such as liquid chromatography, mass spectrometry, two-dimensional liquid chromatography, or quadrupole time mass spectrometry. Moreover, efforts should be made to verify the main signaling pathways of YBD in the treatment of asthma from animal experiments and clinical trials, in order to provide scientific basis and ideas for better treatment of asthma with integrated Chinese and Western medicine.

  Conclusion Top

YBD has the characteristics of multiple targets and multiple pathways in the treatment of asthma. Its main biological mechanism is related to ErbB, MAPK, Estrogen, PI3K-Akt, neurotrophin, Hippo, HIF-1, TGF-, FoxO, Wnt, Chemoking, Toll-like receptor, VEGF, GnRH, and Notch.

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Conflicts of interest

There are no conflicts of interest.

  References Top

Zhang Jiujin, Chen Xianyuan. Complications of bronchial asthma[J]. Clinical Highlights. 1999:471-3.  Back to cited text no. 1
To T, Stanojevic S, Moores G, Moores G, Gershon AS, Bateman ED, et al. Global asthma prevalence in adults: Findings from the cross-sectional world health survey. BMC Public Health 2012;12:204.  Back to cited text no. 2
Ruixian Y. Creation Report of “Heavy Asthma”. China: Zhejiang Media College; 2019.  Back to cited text no. 3
Yang Guifang, Ren Jiayu, Liu Huan, Hu Songyan, Wang Yan, Pu Xiaojun, et al. Study on the effect of Yuemao plus Banxia decoction on serum IgE and IL-4 in lung tissue of allergic asthma mice [J]. Guiyang Traditional Chinese Medicine Journal of College. 2016;38:32-5+40.  Back to cited text no. 4
Guifang Y, Jiayu R, Huan L, Songyan H, Yan W, Xiaojun P, et al. Study on the effect of Yuemai plus Banxia Tang on serum IgE and IL-4 in lung tissue of mice with allergic asthma. Guiyang Tradit Chin Med Coll J 2016;38:32-5.  Back to cited text no. 5
Wen F, Xinglong L, Longqiong Z, Yongxiang G. Effects of three methods on intestinal flora in rats with imbalance of intestinal flora and allergic asthma. Chongqing Med J 2017;46:3477-9.  Back to cited text no. 6
Jiayu R, Guifang Y, Songyan H, Huan L, Yan W, Xiaojun P, et al. Yuemao plus Banxia Tang reduced serum IgE and increased SOD activity in allergic asthma mice. China Comp Med J 2015;25:18-21.  Back to cited text no. 7
Fei C. Yue Mai and Ban Xia Tang in the treatment of 35 cases of bronchial asthma with acute asthma syndrome. Mod Distance Educ Chin Med 2010;8:25-6.  Back to cited text no. 8
Li J, Zhao P, Li Y, Tian Y, Wang Y. Systems pharmacology-based dissection of mechanisms of Chinese medicinal formula Bufei Yishen as an effective treatment for chronic obstructive pulmonary disease. Sci Rep 2015;15:5.  Back to cited text no. 9
Chunwei W, Li L, Shengwang L, Chao C, Shumei W. Application of drug-target prediction technology in network pharmacology of traditional Chinese medicine. Zhongguo Zhong Yao Za Zhi 2016,41:377-82.  Back to cited text no. 10
Wang S, Wang H, Lu Y. Tianfoshen oral liquid: A CFDA approved clinical traditional Chinese medicine, normalizes major cellular pathways disordered during colorectal carcinogenesis. Oncotarget 2017;8:14549-69.  Back to cited text no. 11
Yu H, Chen J, Xu X, Li Y, Zhao H, Fang Y, et al. A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data. PLoS One 2012;7:e37608.  Back to cited text no. 12
Bosque G, Folch-Fortuny A, Picó J, Ferrer A, Elena SF. Topology analysis and visualization of Potyvirus protein-protein interaction network. BMC Syst Biol 2014;8:129.  Back to cited text no. 13
De Las Rivas J, Fontanillo C. Protein-protein interactions essentials: Key concepts to building and analyzing interactome networks. PLoS Comput Biol 2010;6:e1000807.  Back to cited text no. 14
Martin A, Ochagavia ME, Rabasa LC, Miranda J, Fernandez-de-Cossio J, Bringas R. BisoGenet: A new tool for gene network building, visualization and analysis. BMC Bioinformatics 2010;11:91.  Back to cited text no. 15
Tang Y, Li M, Wang J, Pan Y, Wu FX. CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems 2015;127:67-72.  Back to cited text no. 16
Zhang Y, Li Z, Yang M, Wang D, Yu L, Guo C, et al. Identification of GRB2 and GAB1 coexpression as an unfavorable prognostic factor for hepatocellular carcinoma by a combination of expression profile and network analysis. PLoS One 2013;8:e85170.  Back to cited text no. 17
Zheng RQ, Li M. A WEB-based platform for predicting essential proteins. Chin J Bioinform 2014;12:84-9.  Back to cited text no. 18
Chen J, Li C, Zhu Y, Sun L, Sun H, Liu Y, et al. Integrating GO and KEGG terms to characterize and predict acute myeloid leukemia-related genes. Hematology 2015;20:336-42.  Back to cited text no. 19
Lyu M, Yan CL, Liu HX, Wang TY, Shi XH, Liu JP, et al. Network pharmacology exploration reveals endothelial inflammation as a common mechanism for stroke and coronary artery disease treatment of Danhong injection. Sci Rep 2017;7:15427.  Back to cited text no. 20
Zeng L, Yang K. Exploring the pharmacological mechanism of Yanghe decoction on HER2-positive breast cancer by a network pharmacology approach. J Ethnopharmacol 2017;199:68-85.  Back to cited text no. 21
Li S. Framework and practice of network-based studies for Chinese herbal formula. Zhong Xi Yi Jie He Xue Bao 2007;5:489-93.  Back to cited text no. 22
Hopkins AL. Network pharmacology. Nat Biotechnol 2007;25:1110-1.  Back to cited text no. 23
Hopkins AL. Network pharmacology: The next paradigm in drug discovery. Nat Chem Biol 2008;4:682-90.  Back to cited text no. 24
Li ZY. Some progress in network pharmacology and modern research of traditional Chinese medicine. Chin J Pharmacol Toxicol 2015;29:883-92.  Back to cited text no. 25
Bao A, Yang H, Ji J, Chen Y, Bao W, Li F, et al. Involvements of p38 MAPK and oxidative stress in the ozone-induced enhancement of AHR and pulmonary inflammation in an allergic asthma model. Respir Res 2017;18:216.  Back to cited text no. 26
Inoue H, Hattori T, Zhou X, Etling EB, Modena BD, Trudeau JB, et al. Dysfunctional ErbB2, an EGF receptor family member, hinders repair of airway epithelial cells from asthmatic patients. J Allergy Clin Immunol 2019;143:2075-85.  Back to cited text no. 27
Higaki M, Shimokado K. Phosphatidylinositol 3-kinase is required for growth factor-induced amino acid uptake by vascular smooth muscle cells. Arterioscler Thromb Vasc Biol 1999;19:2127-32.  Back to cited text no. 28
Krymskaya VP, Hoffman R, Eszterhas A, Ciocca V, Panettieri RA Jr. TGF-beta 1 modulates EGF-stimulated phosphatidylinositol 3-kinase activity in human airway smooth muscle cells. Am J Physiol 1997;273:L1220-7.  Back to cited text no. 29
Ma L, Brown M, Kogut P, Serban K, Li X, McConville J, et al. Akt activation induces hypertrophy without contractile phenotypic maturation in airway smooth muscle. Am J Physiol Lung Cell Mol Physiol 2011;300:L701-9.  Back to cited text no. 30
Yang N, Zhang H, Cai X, Shang Y. Epigallocatechin-3-gallate inhibits inflammation and epithelial-mesenchymal transition through the PI3K/AKT pathway via upregulation of PTEN in asthma. Int J Mol Med 2018;41:818-28.  Back to cited text no. 31
Skronska-Wasek W, Gosens R, Königshoff M, Baarsma HA. WNT receptor signalling in lung physiology and pathology. Pharmacol Ther 2018;187:150-66.  Back to cited text no. 32
Kim HA, Yin W, Nakamichi YA, Panza P, Grohmann B, Buettner C, et al. WNT/RYK signaling restricts goblet cell differentiation during lung development and repair. Proc Natl Acad Sci USA 2019;116:25697-706.  Back to cited text no. 33
Jia XX, Zhu TT, Huang Y, Zeng XX, Zhang H, Zhang WX. Wnt/β-catenin signaling pathway regulates asthma airway remodeling by influencing the expression of c-Myc and cyclin D1 via the p38 MAPK-dependent pathway. Exp Ther Med 2019;18:3431-8.  Back to cited text no. 34
Sharma S, Murphy AJ, Soto-Quiros ME, Avila L, Klanderman BJ, Sylvia JS, et al. Association of VEGF polymorphisms with childhood asthma, lung function and airway responsiveness. Eur Respir J 2009;33:1287-94.  Back to cited text no. 35
Zhao X, Yu FQ, Huang XJ, Xu BY, Li YL, Zhao XY, et al. Azithromycin influences airway remodeling in asthma via the PI3K/Akt/MTOR/HIF-1α/VEGF pathway. J Biol Regul Homeost Agents 2018;32:1079-88.  Back to cited text no. 36
Redmond AM, James AW, Nolan SH, Self TH. Premenstrual asthma: Emphasis on drug therapy options. J Asthma 2004;41:687-93.  Back to cited text no. 37
Lee CC, Lin CL, Leu SJ, Lee YL. Overexpression of Notch ligand Delta-like-1 by dendritic cells enhances their immunoregulatory capacity and exerts antiallergic effects on Th2-mediated allergic asthma in mice. Clin Immunol 2018;187:58-67.  Back to cited text no. 38


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