Network Pharmacology-Based Prediction of Mechanism of Shenzhuo Formula for Application to DKD

Background. Shenzhuo formula (SZF) is a traditional Chinese medicine (TCM) prescription which has significant therapeutic effects on diabetic kidney disease (DKD). However, its mechanism remains unknown. Therefore, this study aimed to explore the underlying anti-DKD mechanism of SZF. Methods. The active ingredients and targets of SZF were obtained by searching TCMSP, TCMID, SwissTargetPrediction, HIT, and literature. The DKD target was identified from TTD, DrugBank, and DisGeNet. The potential targets were obtained and PPI network were built after mapping SZF targets and DKD targets. The key targets were screened out by network topology and the “SZF-key targets-DKD” network was constructed by Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed by using DAVID, and the results were visualized by Omicshare Tools. Results. We obtained 182 potential targets and 30 key targets. Furthermore, a “SZF-key targets-DKD” network topological analysis showed that active ingredients like M51, M21, M5, M71, and M28 and targets like EGFR, MMP9, MAPK8, PIK3CA, and STAT3 might play important roles in the process of SZF treating in DKD. GO analysis results showed that targets were mainly involved in positive regulation of transcription from RNA polymerase II promoter, inflammatory response, lipopolysaccharide-mediated signaling pathway, and other biological processes. KEGG showed that DKD-related pathways like TNF signaling pathway and PI3K-Akt signaling pathway were at the top of the list. Conclusion. This research reveals the potential pharmacological targets of SZF in the treatment of DKD through network pharmacology and lays a foundation for further studies.

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Network Pharmacology-based Prediction of Mechanism of Shenzhuo Formula for Application to DN

10.21203/rs.3.rs-53219/v1 ◽

2020 ◽

Author(s):

Xin-miao Wang ◽

Lin Han ◽

Li-li Zhang ◽

Sha Di ◽

Xiu-xiu Wei ◽

...

Keyword(s):

Rna Polymerase Ii ◽

Signaling Pathway ◽

Drug Targets ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Regulation Of Transcription ◽

Therapeutic Effects ◽

Go Analysis ◽

Pathway Enrichment

Abstract Background: Shenzhuo formula is a traditional Chinese medicine (TCM) prescription which has significant therapeutic effects on diabetic nephropathy (DN). However, its mechanism remains unknown. Therefore, this study aimed to explore the underlying anti-DN mechanism of shenzhuo formula.Methods: The active ingredients and targets of shenzhuo formula were obtained by searching TCMSP, TCMID, SwissTargetPrediction and HIT. The DN target was identified from TTD, DrugBank and DisGeNet. The potential targets were obtained and PPI network were built after mapping the disease and drug targets. The key targets were screened out by network topology and the “drugs - DN - key targets” network was constructed by Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed using DAVID, and the results were visualized using the Omicshare Tools.Results: We obtained 182 potential targets and 30 key targets. Ulteriorly, “drugs - DN - key targets” network were constructed, and results showed that nodes like M51, M21, M5, M71, M28, EGFR, MMP9, MAPK8, PIK3CA and STAT3 had a higher degree. GO analysis results mainly involved in positive regulation of transcription from RNA polymerase II promoter, inflammatory response, lipopolysaccharide-mediated signalingpathway and other biological processes. The results of KEGG showed that DN-related pathways like TNF signaling pathway, PI3K-Akt signaling pathway were at the top of the list.Conclusion: This article reveals the possible mechanism of shenzhuo formula in the treatment of DN through network pharmacology research, and lays a foundation for further studies.

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Systematic Investigation of Quercetin for Treating Cardiovascular Disease Based on Network Pharmacology

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666190717124507 ◽

2019 ◽

Vol 22 (6) ◽

pp. 411-420 ◽

Cited By ~ 5

Author(s):

Xian-Jun Wu ◽

Xin-Bin Zhou ◽

Chen Chen ◽

Wei Mao

Keyword(s):

Cardiovascular Disease ◽

Cardiovascular Diseases ◽

Signaling Pathway ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Therapeutic Effects ◽

Pathway Enrichment ◽

Compound Target

Aim and Objective: Cardiovascular disease is a serious threat to human health because of its high mortality and morbidity rates. At present, there is no effective treatment. In Southeast Asia, traditional Chinese medicine is widely used in the treatment of cardiovascular diseases. Quercetin is a flavonoid extract of Ginkgo biloba leaves. Basic experiments and clinical studies have shown that quercetin has a significant effect on the treatment of cardiovascular diseases. However, its precise mechanism is still unclear. Therefore, it is necessary to exploit the network pharmacological potential effects of quercetin on cardiovascular disease. Materials and Methods: In the present study, a novel network pharmacology strategy based on pharmacokinetic filtering, target fishing, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, compound-target-pathway network structured was performed to explore the anti- cardiovascular disease mechanism of quercetin. Results:: The outcomes showed that quercetin possesses favorable pharmacokinetic profiles, which have interactions with 47 cardiovascular disease-related targets and 12 KEGG signaling pathways to provide potential synergistic therapeutic effects. Following the construction of Compound-Target-Pathway (C-T-P) network, and the network topological feature calculation, we obtained top 10 core genes in this network which were AKT1, IL1B, TNF, IL6, JUN, CCL2, FOS, VEGFA, CXCL8, and ICAM1. KEGG pathway enrichment analysis. These indicated that quercetin produced the therapeutic effects against cardiovascular disease by systemically and holistically regulating many signaling pathways, including Fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, MAPK signaling pathway, IL-17 signaling pathway and PI3K-Akt signaling pathway.

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Potential mechanisms of Guizhi decoction against hypertension based on network pharmacology and Dahl salt-sensitive rat model

Chinese Medicine ◽

10.1186/s13020-021-00446-x ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Jiye Chen ◽

Yongjian Zhang ◽

Yongcheng Wang ◽

Ping Jiang ◽

Guofeng Zhou ◽

...

Keyword(s):

Signaling Pathway ◽

Rat Model ◽

Experimental Studies ◽

Matrix Metalloproteinase 2 ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Therapeutic Effects ◽

Active Ingredients ◽

Active Compounds ◽

Guizhi Decoction

Abstract Background Guizhi decoction (GZD), a classical Chinese herbal formula, has been widely used to treat hypertension, but its underlying mechanisms remain elusive. The present study aimed to explore the potential mechanisms and therapeutic effects of GZD on hypertension by integrating network pharmacology and experimental validation. Methods The active ingredients and corresponding targets were collected from the Traditional Chinese Medicine Systems Pharmacology database and Analysis Platform (TCMSP). The targets related to hypertension were identified from the CTD, GeneCards, OMIM and Drugbank databases. Multiple networks were constructed to identify the key compounds, hub targets, and main biological processes and pathways of GZD against hypertension. The Surflex-Dock software was used to validate the binding affinity between key targets and their corresponding active compounds. The Dahl salt-sensitive rat model was used to evaluate the therapeutic effects of GZD against hypertension. Results A total of 112 active ingredients, 222 targets of GZD and 341 hypertension-related targets were obtained. Furthermore, 56 overlapping targets were identified, five of which were determined as the hub targets for experimental verification, including interleukin 6 (IL-6), C–C motif chemokine 2 (CCL2), IL-1β, matrix metalloproteinase 2 (MMP-2), and MMP-9. Pathway enrichment analysis results indicated that 56 overlapping targets were mainly enriched in several inflammation pathways such as the tumor necrosis factor (TNF) signaling pathway, Toll-like receptor (TLR) signaling pathway and nuclear factor kappa-B (NF-κB) signaling pathway. Molecular docking confirmed that most active compounds of GZD could bind tightly to the key targets. Experimental studies revealed that the administration of GZD improved blood pressure, reduced the area of cardiac fibrosis, and inhibited the expression of IL-6, CCL2, IL-1β, MMP-2 and MMP-9 in rats. Conclusion The potential mechanisms and therapeutic effects of GZD on hypertension may be attributed to the regulation of cardiac inflammation and fibrosis.

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

10.21203/rs.3.rs-39351/v2 ◽

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.

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A Network Pharmacology Approach to Uncover the Potential Mechanism of Yinchensini Decoction

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2018/2178610 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 5

Author(s):

Guoming Chen ◽

Chuyao Huang ◽

Yunyun Liu ◽

Tengyu Chen ◽

Ruilan Huang ◽

...

Keyword(s):

Rna Polymerase Ii ◽

Enrichment Analysis ◽

Drug Binding ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Regulation Of Transcription ◽

Potential Mechanism ◽

Ppi Network ◽

Protein Protein Interaction ◽

System Pharmacology

Objective. To predict and explore the potential mechanism of Yinchensini decoction (YCSND) based on systemic pharmacology. Method. TCMSP database was searched for the active constituents and related target proteins of YCSND. Cytoscape 3.5.1 was used to construct the active ingredient-target interaction of YCSND and network topology analysis, with STRING online database for protein-protein interaction (PPI) network construction and analysis; and collection from the UniProt database of target protein gene name, with the DAVID database for the gene ontology (GO) functional analysis, KEGG pathway enrichment analysis mechanism and targets of YCSND. Results. The results indicate the core compounds of YCSND, namely, kaempferol, 7-Methoxy-2-methyl isoflavone, and formononetin. And its core targets are prostaglandin G/H synthase 2, estrogen receptor, Calmodulin, heat shock protein HSP 90, etc. PPI network analysis shows that the key components of the active ingredients of YCSND are JUN, TP53, MARK1, RELA, MYC, and so on. The results of the GO analysis demonstrate that extracellular space, cytosol, and plasma membrane are the main cellular components of YCSND. Its molecular functions are mainly acting on enzyme binding, protein heterodimerization activity, and drug binding. The biological process of YCSND is focused on response to drug, positive regulation of transcription from RNA polymerase II promoter, the response to ethanol, etc. KEGG results suggest that the pathways, including pathways in cancer, hepatitis B, and pancreatic cancer, play a key role in YCSND. Conclusion. YCSND exerts its drug effect through various signaling pathways and acts on kinds of targets. By system pharmacology, the potential role of drugs and the mechanism of action can be well predicted.

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Network Pharmacology Analysis on the Mechanism of Huangqi Sijunzi Decoction in Treating Cancer-Related Fatigue

Journal of Healthcare Engineering ◽

10.1155/2021/9780677 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Yixin Cui ◽

Haiming Wang ◽

Decai Wang ◽

Jiwei Mi ◽

Gege Chen ◽

...

Keyword(s):

Topological Analysis ◽

Enrichment Analysis ◽

Venn Diagram ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Active Ingredients ◽

Protein Protein Interaction ◽

Kegg Pathways ◽

Cancer Related Fatigue ◽

Tumor Gene Expression

Objective. This study aimed to determine the active ingredients of Huangqi Sijunzi Decoction (HQSJZD) and the targets in treating cancer-related fatigue (CRF) so as to investigate the treatment mechanism of HQSJZD for CRF. Methods. This study adopted the method of network pharmacology. The active ingredients and targets of HQSJZD were retrieved, and the targets of HQSJZD in treating CRF were obtained using a Venn diagram. Next, a protein-protein interaction (PPI) network was constructed using the String database. The core targets of HQSJZD in treating CRF were identified through topological analysis, and functional annotation analysis and pathway enrichment analysis were carried out. Subsequently, a compound-disease-target regulatory network was constructed using Cystoscape 3.8.0 software. Results. A total of 250 targets of HQSJZD ingredients, 1447 CRF-related genes, and 144 common targets were obtained. Through topological analysis, 61 core targets were screened. Bioinformatics annotation of these genes identified 2366 gene ontology (GO) terms and 172 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Conclusion. The active ingredients in HQSJZD, that is, quercetin, luteolin, kaempferol, and naringenin, may act on AKT1, IL-6, VEGFA, MAPK3, CASP3, JUN, and EGFR to regulate the PI3K-Akt, TNF, and IL-17 signaling pathways, thereby suppressing inflammatory response, tumor gene expression, and tumor angiogenesis to treat CRF. This study investigated the pharmacological basis and mechanism of HQSJZD in the treatment of CRF using systematic pharmacology, which provides an important reference for further elucidation of the anti-CRF mechanism and clinical applications of HQSJZD, and also provides a method protocol for similar studies in the future.

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Examining the effector mechanisms of Xuebijing Injection on COVID-19 based on network pharmacology.

10.21203/rs.3.rs-26834/v2 ◽

2020 ◽

Author(s):

Wenjiang Zheng ◽

Qian Yan ◽

Yongshi Ni ◽

Shaofeng Zhan ◽

Liuliu Yang ◽

...

Keyword(s):

Growth Factor ◽

Protein Kinase ◽

Signaling Pathway ◽

Cellular Response ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Therapeutic Effects ◽

Active Ingredients ◽

Effector Mechanism ◽

Effector Mechanisms

Abstract Objective: To examine the potential effector mechanisms of Xuebijing (XBJ) on coronavirus disease 2019 (COVID-19) based on network pharmacology.Methods: We searched Chinese and international papers to obtain the active ingredients of XBJ. Then, we compiled COVID-19 disease targets from the GeneCards gene database and via literature searches. Next, we used the SwissTargetPrediction database to predict XBJ’s effector targets and map them to the abovementioned COVID-19 disease targets in order to obtain potential therapeutic targets of XBJ. Cytoscape software version 3.7.0 was used to construct a “XBJ active-compound-potential-effector target” network and protein-protein interaction (PPI) network, and then to carry out network topology analysis of potential targets. We used the ClueGO and CluePedia plugins in Cytoscape to conduct gene ontology (GO) biological process (BP) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of XBJ’s effector targets. Results: We obtained 144 potential COVID-19 effector targets of XBJ. Fourteen of these targets—glyceraldehyde 3-phosphate dehydrogenase (GAPDH), albumin (ALB), tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), Caspase-3 (CASP3), signal transducer and activator of transcription 3 (STAT3), MAPK8, prostaglandin-endoperoxide synthase 2 (PTGS2), JUN, interleukin-2 (IL-2), estrogen receptor 1 (ESR1), and MAPK14—had degree values >40 and therefore could be considered key targets. They participated in extracellular signal–regulated kinase 1 and 2 (ERK1, ERK2) cascade, the T-cell receptor signaling pathway, activation of MAPK activity, cellular response to lipopolysaccharide, and other inflammation- and immune-related BPs. XBJ exerted its therapeutic effects through the renin–angiotensin system (RAS), nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB), MAPK, phosphatidylinositol-4, 5-bisphosphate 3-kinase (PI3K)–protein kinase B (Akt)–vascular endothelial growth factor (VEGF), toll-like receptor (TLR), TNF, and inflammatory-mediator regulation of transient receptor potential (TRP) signaling pathways to ultimately construct a “ingredient-target-pathway” effector network. Conclusion: The active ingredients of XBJ regulated different genes, acted on different pathways, and synergistically produced anti-inflammatory and immune-regulatory effects, which fully demonstrated the synergistic effects of different components on multiple targets and pathways. Our study demonstrated that existing studies on the pharmacological mechanisms of XBJ in the treatment of sepsis and severe pneumonia, could explain the effector mechanism of XBJ in COVID-19 treatment, and those provided a preliminary examination of the potential effector mechanism in this disease.

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Examining the effector mechanisms of Xuebijing Injection on COVID-19 based on network pharmacology

10.21203/rs.3.rs-26834/v1 ◽

2020 ◽

Author(s):

Wenjiang Zheng ◽

Qian Yan ◽

Yongshi Ni ◽

Shaofeng Zhan ◽

Liuliu Yang ◽

...

Keyword(s):

Growth Factor ◽

Protein Kinase ◽

Signaling Pathway ◽

Cellular Response ◽

Interleukin 2 ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Therapeutic Effects ◽

Active Ingredients ◽

Effector Mechanisms

Abstract Objective To examine the potential effector mechanisms of Xuebijing (XBJ) on coronavirus disease 2019 (COVID-19) based on network pharmacology.Methods We searched Chinese and international papers to obtain the active ingredients of XBJ. Then, we compiled COVID-19 disease targets from the GeneCards gene database and via literature searches. Next, we used the SwissTargetPrediction database to predict XBJ’s effector targets and map them to the abovementioned COVID-19 disease targets in order to obtain potential therapeutic targets of XBJ. Cytoscape software version 3.7.0 was used to construct a “XBJ active-compound–potential-effector target” network and protein–protein interaction (PPI) network, and then to carry out network topology analysis of potential targets. We used the ClueGO and CluePedia plugins in Cytoscape to conduct gene ontology (GO) Biological Process (BP) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of XBJ’s effector targets.Results We obtained 147 potential COVID-19 effector targets of XBJ. Fourteen of these targets—glyceraldehyde 3-phosphate dehydrogenase (GAPDH), albumin (ALB), tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), Caspase-3 (CASP3), signal transducer and activator of transcription 3 (STAT3), MAPK8, prostaglandin-endoperoxide synthase 2 (PTGS2), JUN, interleukin-2 (IL-2), Estrogen Receptor 1 (ESR1), and MAPK14—had degree values > 40 and therefore could be considered key targets. They participated in extracellular signal–regulated kinase 1 and 2 (ERK1, ERK2) cascade, the T-cell receptor signaling pathway, activation of MAPK activity, cellular response to lipopolysaccharide (LPS), and other inflammation- and immune-related BPs. XBJ exerted its therapeutic effects through the renin–angiotensin system (RAS), nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB), MAPK, phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)–protein kinase B (Akt)–vascular endothelial growth factor (VEGF), toll-like receptor (TLR), TNF, and inflammatory-mediator regulation of transient receptor potential (TRP) signaling pathways to ultimately construct a “ingredient–target–pathway” effector network. Conclusion: The active ingredients of XBJ regulated different genes, acted on different pathways, and synergistically produced anti-inflammatory and immune-regulatory effects, which fully demonstrated the synergistic effects of different components on multiple targets and pathways. The results of this study validated current pharmacological mechanistic studies of XBJ in the treatment of sepsis and severe pneumonia and could better explain XBJ’s effector mechanisms in the clinical treatment of COVID-19.

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A Study on the Mechanism of Milkvetch Root in the treatment of Diabetic Nephropathy based on Network Pharmacology

10.21203/rs.3.rs-22529/v1 ◽

2020 ◽

Author(s):

Chunli Piao ◽

Qi Zhang ◽

De Jin ◽

Li Wang ◽

Cheng Tang ◽

...

Keyword(s):

Diabetic Nephropathy ◽

Signaling Pathway ◽

Diabetic Complications ◽

Drug Targets ◽

Topological Analysis ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Systems Pharmacology ◽

Pathway Enrichment

Abstract Background: Diabetic nephropathy (DN) is one of the most common complications of diabetes mellitus. Milkvetch Root has been extensively used to treat DN in clinical practice in China for many years, but the active ingredients, drug targets, and its exact molecular mechanism are not known. The aim of this study was to decrypt the underlying mechanisms of Milkvetch Root in the treatment of DN by using a systems pharmacology approach. Methods: The components and targets of Milkvetch Root were analyzed using the Traditional Chinese Medicine Systems Pharmacology database. Then we found the common target of Milkvetch Root and disease, constructed a protein-protein interaction (PPI) network using String, and screened the key targets from these common targets through topological analysis. Analyses of enrichment of Gene Ontology (GO) pathways and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Subsequently, the major hubs were imported to the Database for Annotation, Visualization and Integrated Discovery to perform a pathway enrichment analysis. Results: There were 20 active compounds of Milkvetch Root and 10 diabetic nephropathy -associated targets (AKT1, VEGFA, IL6, PPARG, CCL2, NOS3, SERPINE1, CRP, ICAM1, SLC2A4) that were obtained. Then, the results of GO and KEGG pathway enrichment analyses suggested that the AGE-RAGE signaling pathway in diabetic complications, HIF-1 signaling pathway, PI3K-Akt signaling pathway and TNF signaling pathway in diabetic complications might serve as the key points and principal pathways for DN treatment. Conclusions: In brief, Milkvetch Root has multiple components, multiple targets and multiple pharmacological effects in the treatment of DN, which provides clues for further research on DN.

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Network Pharmacology Study and Molecular Docking on the Mechanism of Treatment of Colorectal Cancer by Xiao-Chai-Hu-Tang

10.21203/rs.3.rs-157805/v1 ◽

2021 ◽

Author(s):

Jingyun Jin ◽

Bin Chen ◽

Xiangyang Zhan ◽

Zhiyi Zhou ◽

Hui Liu ◽

...

Keyword(s):

Colorectal Cancer ◽

Gene Ontology ◽

Molecular Docking ◽

Signaling Pathway ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Signal Pathways ◽

Active Ingredients ◽

Pathway Gene

Abstract Background and objective: To predict the targets and signal pathways of Xiao-Chai-Hu-Tang (XCHT) in the treatment of colorectal cancer (CRC) based on network pharmacology, to further analyze its anti-CRC material basis and mechanism of action.Methods: TCMSP and TCMID databases were adopted to screen the active ingredients and potential targets of XCHT. CRC-related targets were retrieved by analyzing published microarray data (accession number GSE110224) from the Gene Expression Omnibus (GEO) database. The above common targets were used to construct the “herb-active ingredients-target” network by Cytoscape 3.8.0 software. And then, the protein-to-protein interaction（PPI）was constructed and analyzed with BisoGenet and CytoNCA plug-in in Cytoscape. Gene Ontology (GO) functional and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis for target genes were then performed using the R package of cluster Profiler. Further, AutoDock Vina software was used to conduct molecular docking studies on the active ingredients and key targets to verify the network pharmacological analysis results.Results: A total of 71 active ingredients of XCHT and 20 potential targets for anti-CRC were identified. The network analysis revealed that quercetin, stigmasterol, kaempferol, baicalein, acacetin may be the key compounds. And PTGS2, NR3C2, CA2, MMP1 may be the key targets. The active ingredients of XCHT interacted with most disease targets of CRC. It fully showed that XCHT exerted its therapeutic effect through the synergistic action of the multi-compound, multi-target, and multi-pathway. Gene ontology enrichment analysis showed 46 GO entries, including 20 biological processes, 6 cellular components, and 20 molecular functions. 11 KEGG signaling pathways had been identified, including IL-17 signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, and NF-kappa B signaling pathway. It showed that XCHT played a role in the treatment of CRC by regulating different signal pathways. Molecular docking confirmed the correlation between five core compounds (including quercetin, stigmasterol, kaempferol, baicalein, and acacetin) and PTGS2.Conclusion: The potential active ingredients, possible targets, and key biological pathways for the efficacy of XCHT in the treatment of CRC were preliminarily described, which provided a theoretical basis for further experimental verification research.

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