Coronary artery disease (CAD) is a cardiovascular disease characterized by atherosclerosis, angiogenesis, thrombogenesis, inflammation, etc. Xintong granule (XTG) is considered a practical therapeutic strategy in China for CAD. Although its therapeutic role in CAD has been reported, the molecular mechanisms of XTG in CAD have not yet been explored.A network pharmacology approach including drug-likeness (DL) evaluation, oral bioavailability (OB) prediction, protein-protein interaction (PPI) network construction and analysis, and Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses was used to predict the active ingredients, potential targets, and molecular mechanisms of XTG associated with the treatment of CAD. Molecular docking analysis was performed to investigate the interactions between the active compounds and the underlying targets.Fifty-one active ingredients of XTG and 294 CAD-related targets were screened for analysis. Gene Ontology enrichment analysis showed that the therapeutic targets of XTG in CAD are mainly involved in blood circulation and vascular regulation. KEGG pathway analysis indicated that XTG intervenes in CAD mainly through the regulation of fluid shear stress and atherosclerosis, the AGE-RAGE signaling pathway in diabetic complications, and the relaxin signaling pathway. Molecular docking analysis showed that each key active ingredient  of XTG can bind to the core targets of CAD .The present study revealed the CAD treatment-related active ingredients, underlying targets, and potential molecular mechanisms of XTG acting by regulating fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, and relaxin signaling pathway. Some studies have shown that hypertension, dyslipidemia, diabetes, and an unhealthy lifestyle are various risk factors for CAD.–10 The immutable risk factors for CAD include age, sex, and family history.–13 The diagnosis of CAD depends mainly on typical clinical symptoms, combined with auxiliary examinations to find evidence of myocardial ischemia or coronary artery occlusion and evaluation of myocardial injury markers to determine whether myocardial necrosis has occurred. Currently, the recommended preventive and therapeutic approaches for CAD include lipid-lowering, antithrombotic, and antiinflammatory therapies, percutaneous coronary intervention (PCI), and coronary artery bypass graft (CABG).,16 Angiotensin-converting enzyme inhibitors, angiotensin receptor blockers (ARBs), beta-blockers, calcium channel blockers (CCBs), ranolazine, aspirin, P2Y12 receptor antagonists, protease-activated receptor-1 antagonists, and nitrates are recommended for clinical drug therapy.,18 Moreover, due to complex pathogenesis, no single treatment can cure CAD. Therefore, multiple intervention approaches should be employed for joint intervention with the pathological process and new strategies are needed to complement existing interventions.Coronary artery disease (CAD) is a cardiovascular disease that leads to myocardial ischemia, hypoxia, or necrosis due to stenosis or occlusion of the coronary arteries. CAD may be quite extensive and include inflammation, atherosclerosis, angiogenesis, thrombogenesis, apoptosis, and other causes of luminal stenosis or occlusion. TCM also provides an effective treatment for CAD. A study by Li showed that TCM could treat CAD via its antiinflammatory effects. Guanxin V is widely used for the clinical treatment of CAD and has been shown to be effective and safe. From the perspective of TCM, the potential for CAD-related therapeutics and drug discovery still need to be improved. Xintong granule (XTG) is widely used to treat CAD in China. XTG contains Huangqi , Danshen , Dangshen , Maidong , Heshouwu , Yinyanghuo , Gegen , Danggui , Zaojiaoci , Haizao , Kunbu , Muli , and Zhishi . Animal experiments have shown that XTG exerts a protective effect on myocardial infarction in rats and a hypolipidemic effect. However, the molecular mechanisms of XTG against CAD remain unclear. In this study, a network pharmacology strategy was employed to explore the multiple components, targets, and signaling pathways closely associated with the therapeutic effect of XTG in CAD. A molecular docking approach was applied to verify the binding of the active ingredients to the action targets to reveal and predict the efficacy of XTG for the treatment of CAD. The detailed workflow is shown in Figure Traditional Chinese medicine (TCM) relies on TCM theory to collect, process, and prepare the components that can be used to prevent, treat, and diagnose diseases, explain the mechanism of action, and guide clinical application.https://tcmsp-e.com/) and the Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine . These databases, which contain the relationships between drugs, targets, and diseases, are the most commonly used databases for searching the components of TCMs.,24 The absorption, distribution, metabolism, and excretion (ADME) parameters “oral bioavailability (OB) ≥ 30%, drug-likeness (DL) ≥ 0.18” and “validated” status were set as filtering criteria for the active ingredients and their predicted targets in the TCMSP database. Some XTG components could not be retrieved from the TCMSP database and were therefore searched in the BATMAN-TCM database. The active compounds of XTG and their predicted targets were obtained according to the input parameter “score cutoff ≥ 20”. The predicted targets were further standardized using the UniProt database (https://www.uniprot.org/) and corrected to the official gene names. Cytoscape 3.7.0 software was used to construct the drug-ingredient-target network of XTG.All chemical constituents of XTG were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform , (2) Comparative Toxicogenomics Database , (3) DrugBank (https://drugbank.ca/), and (4) GeneCards (https://www.genecards.org/). The keyword “coronary artery disease” and the species “Homo sapiens” were set as screening parameters.The CAD-related targets were collected by integrating data from the following well-recognized disease databases: (1) Therapeutic Target Database  database is widely used to predict protein-protein interactions (PPIs). The common targets of XTG and CAD were imported into the STRING database, and the minimum required interaction score was chosen as “high confidence (0.700)” and the species was set as “Homo sapiens” to acquire the interaction relationships among the targets. The data were then imported into Cytoscape 3.7.0 software to construct the PPI network. NetworkAnalyzer was used to calculate the topological parameters of the PPI network, such as node degree, betweenness centrality, and closeness centrality. According to the topological relationships, the targets that met the criteria of degree ≥ average value (8.98), betweenness centrality ≥ average value (0.0117), and closeness centrality ≥ average value (0.266) were considered as core target candidates.The Search Tool for the Retrieval of Interaction Genes/Proteins  is a knowledge base for systematic analysis of gene functions that links genomic information with pathway annotations. GO and KEGG functional enrichment analyses are widely applied to reveal the potential biological functions of genes. The Database for Annotation, Visualization, and Integrated Discovery  was adopted to predict the molecular mechanisms of XTG in CAD treatment. The results of GO biological process (BP) and KEGG pathway enrichment analysis were saved and sorted by false discovery rate (FDR)-corrected and adjusted P values for each term.Gene Ontology (GO) is an international standard system for functional classification of genes.After analyzing the drug-ingredient-target network of XTG, the key active ingredients were identified according to the topological parameter degree ≥ average value (32.32) and annotation of their use for the treatment of CAD in the CTD. The core targets were obtained by intersecting the targets in the BPs and pathways identified by GO and KEGG functional enrichment analysis as highly correlated with CAD. First, the 2D structures of the key active ingredients were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The structures were imported into AutoDockTools-1.5.6 software to add charges and display rotatable bonds, and were then saved in pdbqt format. Second, the crystal structures of the proteins corresponding to the core targets were downloaded from the Protein Data Bank . Then, these structures were imported into PyMOL software to remove water molecules and heteromolecules, and imported into AutoDockTools-1.5.6 to add hydrogen atoms and charges and saved in pdbqt format. Finally, the 3D grid box for molecular docking simulation was created using AutoDockTools-1.5.6 and visualized with AutoDock Vina 1.1.2. The results were analyzed and visualized using PyMOL and Ligplot software.Molecular docking analysis was performed to validate whether the key active ingredients could bind to the core targets.http://links.lww.com/MD/G838. The drug-ingredient-target network revealed complex ingredients, multiple targets, and close interactions between ingredients and targets, as shown in Figure In the present study, 53 active ingredients and 614 predicted targets were identified from the thirteen drugs in XTG, as shown in Supplementary Table S1, http://links.lww.com/MD/G839. The targets of XTG and CAD were used to construct a Venn diagram for mapping and intersection. A total of 294 overlapping targets associated with 51 active ingredients were considered as potential therapeutic targets of XTG in CAD, as shown in Figure http://links.lww.com/MD/G840.The CAD-related targets were integrated from the multisource database, and 1943 disease-related targets were obtained after the elimination of duplicates, as shown in Supplementary Table S2, http://links.lww.com/MD/G841.The 294 intersected targets were used to construct the PPI network using the STRING database and Cytoscape 3.7.0 software. The network contained 237 nodes and 1064 edges, as shown in Figure http://links.lww.com/MD/G842, it can be speculated that the therapeutic effect of XTG in CAD may be correlated primarily with the following BP terms: (1) regulation of blood circulation, (2) vascular process in the circulatory system, (3) regulation of blood vessel size, (4) regulation of blood vessel diameter, and (5) regulation of blood pressure.Through GO BP analysis, the top 30 BPs of potential therapeutic targets of XTG were identified in CAD. The targets were mainly enriched in the terms of cellular responses to stimuli, regulation of biological quality, metabolism, blood circulation, and vascular regulation. From the data in Figure http://links.lww.com/MD/G843. The biological effects of these signaling pathways suggest that the key active ingredients of XTG exert their effects on CAD via regulating the following signaling pathways: (1) fluid shear stress and atherosclerosis, (2) AGE-RAGE signaling pathway in diabetic complications, and (3) relaxin signaling pathway. Based on the BPs and signaling pathways identified above, AKT1, JUN, RELA, MAPK8, NFKB1, EDN1, and NOS3 were considered as the core targets, as shown in Table To further explore the potential molecular mechanisms of XTG in CAD treatment, the top 30 signaling pathways were examined in detail, as shown in Figure lerosis,  AGE-RAGEThe results obtained by molecular docking software are shown in Table  Currently, CAD is mainly treated with antiinflammatory, antiapoptotic, antiatherosclerotic, antiangiogenic, and antithrombogenic agents. TCM has a long history in clinical practice and is widely used for the treatment of diseases; in addition, many resources are worthy of further study. Among the complex ingredients in herbal medicines, unique combinations in traditional complex formulas may play an important role in intervention with specific chronic diseases by targeting specific cell signaling nodes. Network pharmacology has recently been used to explore the intervention mechanisms of drugs in diseases. It systematically reveals the therapeutic effects of drugs in diseases based on the interactions between drugs, targets, and diseases.,44 To better understand the potential mechanisms of XTG, network pharmacology and molecular docking analyses were used to determine the underlying molecular mechanisms of its bioactive compounds.CAD is a major public health challenge in China, and some scholars have written treatment guidelines to address this situation.–50 Moreover, quercetin can reduce the transcriptional activity of nuclear factor kappa B (NF-κB), thereby decreasing the levels of inflammatory cytokines such as IL-1β, IL-10, and TNF-α, which may treat CAD via antiinflammatory effects. Resveratrol increases nitric oxide (NO) production in endothelial cells, stimulates endothelial nitric oxide synthase (eNOS) activity, and prevents eNOS uncoupling by upregulating its expression. In addition, resveratrol inhibits the synthesis of endothelin-1 (ET-1) and reduces oxidative stress in endothelial cells and smooth muscle cells. Resveratrol can also improve pathological stimulation-induced smooth muscle cell proliferation, vascular remodeling, and arterial stiffness, thus preventing and treating arterial development of CAD. These previous findings suggest that XTG probably decrease the levels of inflammatory cytokines, inhibits the proliferation of vascular endothelial cells, and prevents thrombogenesis and atherosclerosis in CAD mainly by quercetin, luteolin, kaempferol, stigmasterol, resveratrol, fisetin, gamma-sitosterol, and beta-sitosterol.Network topology analysis revealed that the key active ingredients in XTG are quercetin, luteolin, kaempferol, stigmasterol, resveratrol, fisetin, gamma-sitosterol, and beta-sitosterol. These compounds are reported to have antiinflammatory, hypolipidemic, and cardioprotective effects, indicating their potential use in the treatment of CAD. NO is the main endothelium-derived regulator of vascular tension. Deactivation of AKT1, leading to a decrease in phosphorylation of eNOS (encoded by NOS3), results in a decrease in NO. A decrease in NO causes proliferation of vascular smooth muscle cells (VSMCs), resulting in vascular proliferative diseases, is a pathophysiological mechanism of CAD. AKT1 can translocate to the nucleus, bind to RELA to form a complex, and activate the transcription of NF-κB. Some researchers found that macrophage infiltration and expression of proinflammatory genes (TNF-α and IL-6) were increased in vessels lacking AKT1. TNF-α and IL-6 are highly expressed by foam cell macrophages, and increased expression of VCAM-1 and ICAM-1 in endothelial cells supports further recruitment of monocytes into atherosclerotic plaques. Increased expression of NOS3 inhibits caspase-3 (CASP3) expression and activity, preventing cardiomyocyte apoptosis. MAPK8 (JNK1), a member of the mitogen-activated protein kinase family of serine-threonine protein kinases, is a positive regulator of c-Jun expression, cell proliferation, and vascular physiology. Activation of JNK1 induces NF-κB activation and upregulates TNF-α and IL-6 expression, leading to inflammation and atherosclerosis.,61 In addition, JNK1 mediates dissociation of the Bcl-2/Beclin 1 complex, leading to apoptosis. NF-κB (composed of RELA and NFKB1) is a transcriptional regulator of inflammation-related genes and plays an important role in atherosclerosis. Some studies have shown that NF-κB selectively upregulates proinflammatory and prothrombotic mediators in atherosclerosis.,65 Researchers found that downregulation of NF-κB with specific NF-κB inhibitors can reduce inflammation and prevent atherosclerosis.,67 Vascular endothelial growth factor is a proangiogenic factor in pathological processes, and NF-κB activation is positively correlated with the Vascular endothelial growth factor production.,69 Activating protein-1 (AP-1) is a collective term for dimeric transcription factors consisting of the subunits JUN, FOS, and ATF that are positively regulated by JNKs. After AP-1 is activated, the JNK protein translocates to the nucleus and phosphorylates c-Jun and activating transcription factor-2. As a results, the level of inflammatory cytokines increases, leading to inflammation.,72 ET-1 is a potent vasoconstrictor peptide linked to vascular diseases through EDN1. ET-1 mediates the activation of the G protein-coupled receptor endothelin A (ETA) in VSMCs, leading to endothelial dysfunction, inflammation, atherosclerosis, and vascular proliferation. A study by Liang showed that the EDN1 tag SNP rs6458155 was associated with CAD risk in the Chinese Han population, possibly due to an increase in circulating ET-1 level.Our results suggest that the core target genes of XTG are enriched primarily in the following signaling pathways: fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, and relaxin signaling pathway. These signaling pathways are related to inflammation, angiogenesis, atherogenesis, vasodilation and thrombogenesis. In the endothelium, AKT1 is the main AKT subtype and phosphorylates eNOS directly at Ser1177.By summarizing the results of previous studies and the present study, we hypothesized the molecular mechanisms of XTG in the treatment of CAD, as shown in Figure As described above, a network pharmacology strategy was proposed to investigate the CAD treatment-related active ingredients, potential therapeutic targets, and regulatory pathways of XTG. The results revealed the active compounds of XTG and the underlying molecular mechanisms by which XTG protects against inflammation, thrombogenesis, atherosclerosis, and angiogenesis by regulating fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications and relaxin signaling pathway. These findings were partially verified by previous studies. Molecular docking analysis showed that each key active ingredient  of XTG had a favorable binding affinity for the core target , further supporting the potential molecular mechanisms of XTG in CAD. This study provides a comprehensive reference for investigating the therapeutic mechanisms of XTG in CAD.Conceptualization: Jiarui Wu.Data curation: Shan Lu, Zhishan Wu, Yingying Tan, Xiaotian Fan.Formal analysis: Chao Wu, Shanshan Jia, Guoliang Cheng, Bingbing Li.Methodology: Siyu Guo, Changgeng Fu, Wei Zhou, Xinkui Liu, Jingyuan Zhang.Supervision: Jiarui Wu, Yanfang Mou.Writing – original draft: Zhihong Huang.Writing – review & editing & language polishing: Zhihong Huang, Siyu Guo, Antony Stalin, Jiarui Wu.The authors are thankful to Beijing University of Chinese Medicine, Xiyuan Hospital of China Academy of Chinese Medical Sciences and Shandong Lunan Pharmaceutical Co., Ltd. for the help in conducting this study.


Citrus aurantium through a network pharmacology approach, construct a component-disease target network, explore its molecular mechanism for the treatment of non-small-cell lung cancer (NSCLC), and validate it experimentally.  To screen the main active components of Citrus aurantium and the targets of Citrus aurantium and NSCLC were collected through the Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform (TCMSP), GeneCards, and OMIM databases. The protein interaction network was constructed using the STRING database, and the component-disease relationship network graph was analyzed using Cytoscape 3.9.1. The Metascape database can be used for GO and KEGG enrichment analyses. The Kaplan-Meier plotter was applied for overall survival analysis of key targets of Citrus aurantium in the treatment of NSCLC. Real-time PCR (RT-PCR) and Western blotting were used to determine the mRNA and protein levels of key targets of Citrus aurantium for the treatment of NSCLC.  The active ingredients in Citrus aurantium were screened, and 54 potential targets for the treatment of NSCLC were found, of which the key ingredient was nobiletin and the key targets are TP53, CXCL8, ESR1, PPAR-α, and MMP9. GO and KEGG enrichment analyses indicated that the mechanism of nobiletin in treating NSCLC may be related to the regulation of cancer signaling pathway, phosphatidylinositol-3 kinase (PI3K)/protein kinase B (Akt) signaling pathway, lipid and atherosclerosis signaling pathway, and neurodegenerative signaling pathway. The experimental results showed that nobiletin could inhibit the proliferation of NSCLC cells and upregulate the levels of P53 and PPAR-α and suppress the expression of MMP9 (P < 0.05).  Five active ingredients of Citrus aurantium can participate in the treatment of NSCLC through multiple targets and pathways. The pro pathway –6. Accor pathway , 8. Howe pathway . In addi pathway , 11. Stu pathway , 13.Aurantii Fructus, also known as ZhiQiao, is the dried unripe fruit of Citrus aurantium L. and its cultivated variants and is a traditional Chinese medicine. According to previous studies, Citrus aurantium has several potential pharmacological effects, such as promotion of intestinal motility [Citrus aurantium also has potential therapeutic effects on cardiovascular disease and cancer [Citrus aurantium, to relieve precancerous lesions of gastric cancer by regulating intestinal microbial balance and treating atrophy and intestinal metaplasia (IM) [Citrus aurantium is a good anticancer herbal medicine, but the therapeutic effect and mechanism of action of Citrus aurantium on NSCLC have not been reported yet and deserve further study.motility , antidepmotility , 16, antmotility , and antmotility . In addid cancer . Weifuchsia (IM) . The aboCitrus aurantium, drug targets, and NSCLC-related disease genes was constructed through a network pharmacology approach to predict the potential targets and related pathways of Citrus aurantium for the treatment of NSCLC, which was then validated by cellular and molecular experiments, thus providing a theoretical basis for further clinical studies.In this study, the protein interaction network between the active ingredients of Citrus aurantium were obtained by searching “Zhiqiao” using the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP) according to our previous study [Citrus aurantium was created. The target names were corrected using UniProt (https://www.uniprot.org/).The active ingredients of us study . Then, whttps://www.genecards.org/) and OMIM (https://omim.org/), were searched using the keyword “non-small-cell lung cancer” to obtain the NSCLC-related genes. The genes from the two databases were then integrated to create a database of NSCLC disease targets.Two online human-related gene databases, GeneCards  to construct a network diagram of the Citrus aurantium active ingredient-NSCLC target gene relationship. The data were analyzed using the CentiScaPe2.2 plug-in in Cytoscape 3.9.1 to calculate the nodes of each active ingredient in the network.The information related to Citrus aurantium and NSCLC were entered into the STRING database (https://string-db.org/), and “Homo sapiens” was selected to map the protein interaction network. Subsequently, data analysis was performed using the CentiScaPe2.2 plug-in in Cytoscape 3.9.1 to screen for key targets in Citrus aurantium and NSCLC. Then, the crossover genes were entered into the Metascape database, “Homo sapiens” was selected, GO and KEGG enrichment analyses were performed, the data obtained from the analyses were stored, P values were calculated, and the relevant data were entered into the online platform Weishengxin (https://www.bioinformatics.com.cn/) to plot bubble plots.The crossover genes of https://kmplot.com/analysis/) was able to assess the impact of 54,000 genes on survival rates for 21 cancer types. Among them, the largest dataset includes breast, ovarian, lung, and gastric cancers [P < 0.05, it indicates that the results are significantly different. In this study, the threshold value with the best performance was used as the cutoff value, and “array quality control” was selected as “no filtered array quality.”The Kaplan-Meier plotter . Chemiluminescent substrates were purchased from Pierce .Dulbecco's modified Eagle medium (DMEM), fetal bovine serum (FBS), 0.25% trypsin-EDTA, and penicillin-streptomycin solution were purchased from Gibco . Real-time PCR kits were purchased from Takara Co., Ltd. . Rabbit anti-p53 and 2 in a humid environment. When the cells reached 80% to 90% fusion, the cells were digested with trypsin-EDTA , followed by passaging culture.A549 cells were added to DMEM medium (Gibco) with 10% fetal bovine serum (FBS), 100 U/mL penicillin, and 100 U/mL streptomycin and maintained at 37°C, 95% air, and 5% CO3 per well into 96-well microplates and cultured for 24 h. Then, NSCLC cells were treated with different concentrations of nobiletin  for 12, 24, and 48 h. CCK-8 solution (10 mL) was added to each well and incubated for another 1 h. Finally, the absorbance of each well was measured at 450 nm by a multivolume spectrophotometer system .Cell viability was assayed using Cell Counting Kit-8 (CCK-8). NSCLC cells in logarithmic growth phase were inoculated at 5 × 103cells/well) in logarithmic growth phase were inoculated in 6-well culture plates and incubated at 37°C for 48 h. The cells were fixed with 4% paraformaldehyde  for 10 min and stained with crystal violet  for 30 min, and colonies containing more than 10 cells were observed under a microscope.NSCLC cells . mRNA was subsequently quantified, and cDNA was synthesized by reverse transcription, and mRNA levels of target genes were measured using a Bio-Rad quantitative PCR instrument. The specific primers used for RT-PCR are shown in Total protein was extracted from the cells using RIPA lysis buffer, and the extracted protein was then quantified using the BCA protein quantification kit according to the manufacturer's instructions. Subsequently, equivalent proteins were separated on 10% SDS-PAGE, then transferred to poly(vinylidene fluoride) (PVDF) membranes and blocked against nonspecific antibodies; primary antibodies were added, incubated for 1-2 h at room temperature, and then incubated with secondary antibodies for 1 h at room temperature; and target protein expression was detected by chemiluminescence, and the bands were analyzed in grayscale by the ImageJ software.P < 0.05.All data were expressed as mean ± standard deviation, and statistical analysis was performed using the SPSS 13.0 statistical software. One-way analysis of variance (ANOVA) was used for comparison of means between multiple groups. Differences were considered statistically significant at Citrus aurantium were available, and the list of ingredients was screened by oral bioavailability (OB) ≥ 30% and adult drug similarity (DL) ≥ 0.18, and finally, five highly active ingredients were obtained , closeness centrality (CC), and degree centrality (DC) parameters. The results showed that nobiletin was higher than the other four components, suggesting that nobiletin in Citrus aurantium is a key component in the treatment of NSCLC , and 18 pathways with the highest correlation to NSCLC were obtained by screening , CXCL8 , BCL2L1 , MMP9 , and NOS2  high mRNA expressions were associated with significantly shorter OS in all NSCLC patients (P < 0.05); i.e., survival was lower in the high expression group than in the low expression group. In contrast, CAT , ESR1 , MAPK14 , CASP9 , PIK3CG , and PPAR-α  mRNA high expressions were associated with significantly improved OS in all NSCLC patients (P < 0.05); i.e., survival was higher in the high expression group than in the low expression group. In addition, CDK1 lacked relevant data.Survival analysis was performed by the Kaplan-Meier plotter to evaluate the expression and prognosis of 12 key targets in NSCLC. As shown in μM) for 12, 24, and 48 h and then measured cell viability using the CCK8 assay. The results showed that nobiletin inhibited the proliferation of NSCLC cells in a dose-dependent manner  for 48 h and detected the mRNA expression of p53, ESR1, MMP9, CXCL8, and PARPA by real-time PCR , involved in tumor angiogenesis to promote tumorigenesis and metastasis . The Kapα) is a ligand-activated nuclear receptor that regulates transcription of target genes associated with lipid homeostasis, differentiation, and inflammation in a variety of ways [α deficiency in mice increased azomethane- (AOM-) induced colon tumorigenesis and tumor growth. In addition, they demonstrated by IHC staining that the PPAR-α-DNMT1/PRMT6-p21/p27 regulatory pathway may also play a role in the early stages of human colorectal carcinogenesis. Previous studies found that apatinib exerts its antitumor effects through the induction of ketogenesis and demonstrated that this tumor-suppressive effect is PPAR-α-dependent [α agonist, was found to alleviate resistance to gefitinib in NSCLC cell lines by modulating the PPAR-α/AMPK/AKT/FoxO1 signaling pathway and demonstrated that the increased antiproliferative effect of fenofibrate was abolished when PPAR-α was silenced [α can inhibit lung cancer growth and metastasis by downregulating cytochrome P450 arachidonic acid cyclooxygenase (Cyp2c) [α, suggesting that PPAR-α may also be involved in the regulatory role of nobiletin in NSCLC.Peroxisome proliferator-activated receptor alpha (PPAR- of ways –45. Luo  of ways  showed tependent . In anotsilenced . In addi (Cyp2c) , 50. In Citrus aurantium may be a key active ingredient in the treatment of NSCLC. In addition, further analysis showed that nobiletin inhibited the development of NSCLC through targets such as TP53, CXCL8, ESR1, PPAR-α, and MMP9 and related signaling pathways. Through pharmacological experiments, we verified that nobiletin inhibited the proliferation of NSCLC A549 cell line and affected the expression of P53, PPAR-α, and MMP9. In future experiments, we will further clarify the downstream targets of nobiletin that regulate the proliferation of NSCLC through gene overexpression and silencing. This study provides a reference for further research on the mechanism of action of Citrus aurantium in the treatment of NSCLC.In conclusion, we found through our study that nobiletin in


Keyin pill (KP), a patented medicine in China, is used to treat psoriasis. However, KP has been reported to have liver toxicity, but its toxic substance basis and underlying mechanisms remain unclear. Therefore, this study aimed to explore the pharmacological mechanisms and components of KP-induced liver injury through animal experiments, UPLC-QTOF/MS combined with network pharmacology.  Firstly, based on the immune stress mouse model, liver function parameters and hematoxylin-eosin (H&E) staining were detected to investigate KP-induced liver injury. The UPLC-QTOF/MS method was used to identify the components of KP. CTD database and literature mining were further applied to screen nonliver protective components. Subsequently, the nonliver protective components and their corresponding targets and targets of hepatotoxicity were analyzed by the method of network pharmacology. Finally, key targets from networked pharmacology were examined by the enzyme-linked immunosorbent assay (ELISA) and molecular docking. α, CASP3, and CASP8 targets, thereby causing inflammation, excessive release of factors, and hepatocyte necrosis. The results of the ELISA experiments indicated that KP could increase the release of IL6 and TNFα inflammatory factors in liver tissues. Molecular docking suggested that methoxsalen, obacunone, limonin, and dictamnine had moderate binding ability with CASP3 and CASP8.  Our results indicated that KP had hepatotoxicity in male Kunming mice, which could favor hepatocyte necrosis and infiltration of inflammatory cells. A total of 70 nonliver protective compounds were identified and screened. The results of network pharmacology illustrated that methoxsalen, obacunone, limonin, and dictamnine might be the main compounds that caused liver damage. The potential hepatotoxicity mechanisms of KP might be through the IL17 and apoptosis pathways to regulate IL6, TNF In this study, the material basis and potential pharmacological mechanisms of KP-induced liver injury were preliminarily explored. Our research provides the initial theoretical basis for reducing the toxicity of KP. Smilax glabra Roxb, Dictamnus dasycarpus Turcz, the rhizome of Menispermum dauricum, and Polygonum bistorta L., which is often used in the clinical treatment of blood-heat type psoriasis [Keyin pill (KP) is a Chinese patent medicine composed of soriasis . Clinicasoriasis , 3. Howesoriasis , 5, and Dictamnus dasycarpus Turcz and Chinese medicine compounds containing Dictamnus dasycarpus Turcz [Dictamnus dasycarpus Turcz were the main components that cause liver damage [Menispermum dauricum could cause liver damage in mice, and the mechanism of liver damage was related to the induction of lipid peroxidation after causing oxidative stress in the body [Menispermum dauricum might be the main component of its hepatotoxicity [Smilax glabra Roxb might have hepatotoxicity [In recent years, cumulating evidence suggested the toxicity of medicinal materials in KP, including severe or even fatal liver damage, which occurred after taking us Turcz ; Huang er damage ; modern the body ; dauricitoxicity ; the stutoxicity . HoweverPolygonum multiflorum and Xian-Ling-Gu-Bao [In addition, KP-induced liver injury has specific properties. The incubation period of liver injury caused by KP ranged from 1 to 90 days, and the cumulative dose ranged from 20 to 1800 g. There was no difference in the dependence of liver injury on the dose and course of treatment . Therefog-Gu-Bao , 13.Network pharmacology is a promising approach to investigating the comprehensive mechanisms of TCM and herbal formulae . CompareThis study investigated the dose and pathology of KP-induced hepatotoxicity in male Kunming (KM) mice based on the immune stress model. The UPLC-QTOF/MS method was used to identify KP compounds and combine the CTD database and literature mining to screen nonliver protective components. Next, KP network analysis was performed to investigate the hepatotoxicity mechanisms and toxic ingredients of KP, including protein-protein interaction (PPI) analysis, functional enrichment analysis, and compound-target-pathway (CTP) network construction. Finally, the top-ranked targets obtained by network pharmacology were investigated by applying enzyme-linked immunosorbent assay (ELISA) experiments and molecular docking.Smilax glabra Roxb, Dictamnus dasycarpus Turcz, the rhizome of Menispermum dauricum, and Polygonum bistorta L. were purchased from Hebei Renxin Pharmaceutical Co. Ltd.  and met the standards of the Chinese Pharmacopoeia (Edition 2020).Smilax glabra Roxb, 90 g of Dictamnus dasycarpus Turcz, 30 g of the rhizome of Menispermum dauricum, and 90 g of Polygonum bistorta L. were mixed and decocted three times . The decoction was filtered and then transferred to a rotary evaporator for a concentration at 70°C to obtain the whole formula (containing 1 g of herbs per 1 mL). The whole recipe of KP was then stored in a refrigerator at 4°C.According to the preparation standard of the Ministry of Health Drug Standard Chinese Medicine Formulas Volume VI, 90 g of Animal experiments were carried out with the approval of the Ethics Committee of the General Hospital of Northern Theater Command. The experiments strictly complied with the Guide for the Care and Use of Laboratory Animals. The 18–20 g male KM mice were acquired from Liaoning Changsheng Biotechnology Co Ltd  2020-0001). KM mice adapted to at least a week in the mouse room at 25°C with adequate water and food amount. The LPS  came from Sigma-Aldrich .Based on previous studies, it was found that KP caused liver injury with special heterogeneity. Idiopathic drug-related liver injury is the body's idiosyncratic response to drugs, both allergic (immune specific) and metabolic (metabolic specific). Drug-induced liver injury induced by the organism's idiosyncratic response to a drug cannot be replicated in commonly used experimental animal models. Therefore, it is necessary to construct an animal model to reflect the specificity of the liver. In the past 30 years, studies have found that the combination of nonliver injury dose LPS and specific liver injury drugs can increase the liver injury effect of drugs on mice by inducing immune stress , 17. In n = 8): the high-dose group (14.625 g/kg), the medium-dose group (4.875 g/kg), the low-dose group (1.625 g/kg), the model group, and the control group. KP group and model group mice were injected with LPS 0.1 mg/kg via the tail vein. The control group was injected with 0.9% NaCl via the tail vein. After 2 hours, 14.625 g/kg , 4.875 g/kg , 1.625 g/kg  of KP, and 0.9% NaCl were given by gavage, respectively. After 8 hours, blood from the mice of each group was collected from the retroorbital plexus. The blood was gathered in a coagulation tube and then centrifuged at 3000 rpm in a high-speed centrifuge for 10 minutes, and then serum was collected and stored in a refrigerator at −80°C. The livers were removed and fixed in 4% paraformaldehyde and then stored at room temperature until further analysis , aspartate aminotransferase (AST), and alkaline phosphatase (AKP) in the mouse serum were detected by using the “ALT, AST, and AKP detection kits” .μm. The sections were stained with hematoxylin solution for 3–5 minutes and rinsed with tap water. The sections were then treated with hematoxylin. Finally, the sections were stained with eosin dye for 5 minutes. Afterward, we observed through microscopic examination, image acquisition, and analysis.Liver tissues were removed from the fixed liquid. Target tissues were trimmed with a scalpel in the ventilation cupboard. Then, trimmed tissues and the label were put in the dehydration box. Next, they were dehydrated and dipped in wax. The modified tissue sections were mounted on a wax block on the paraffin microtome, and the thickness of the sections was 4 μL sample of KP consisted of adding 500 μL pure water, vortex mixing for 30 seconds, ultrasonic treatment with an ice bath for 1 hour, centrifugation at 12000 rpm at 4°C for 10 minutes, and removing the supernatant into a 2 mL sample bottle for machine detection. LC30A UPLC system  was conducted for analysis. The separation was performed on the UPLC BEH C18 column (1.7 μm∗2.1∗100 mm) with gradient elution of 0.1% formic acid in liquid A water and 0.1% formic acid in liquid B acetonitrile. The sample injection volume was 5 μL. Primary and secondary MS data were gathered by the AB 5600 Triple TOF TF 1.7 Mass Spectrometer Control Software Analyst, AB Sciex. In each data collection cycle, molecular ions with the strongest strength greater than 100 were selected to collect the corresponding secondary MS data. Bombardment energy: 40 eV, impact energy difference: 20 V, 15 secondary spectra every 50 ms. ESI Ion source parameters were set as follows: atomizing pressure (GS1): 55 Psi, auxiliary pressure: 55 Psi, air curtain pressure: 35 Psi, temperature: 550°C, spray voltage: 5500 V (positive ion mode) or −4000 V (negative ion mode). The raw mass spectra were imported into Progenesis QI software. The retention time correction, peak recognition, peak extraction, peak integration, peak alignment, and other work were carried out. Meanwhile, the corresponding Chinese medicine library in the compound was established, and the peaks containing MSMS data were identified by the self-builttwo-stage mass spectrometry database and the corresponding cracking law matching method.The processing procedure of 100 https://ctdbase.org/) based on the results of the UPLC-QTOF/MS analysis.The nonliver protective ingredients were selected as the target ingredients in combination with the literature, the Pharmacopoeia (2020 edition), and the CTD database (https://pubchem.ncbi.nlm.nih.gov/). Subsequently, smile format files were uploaded to SEA (https://sea.bkslab.org/) and Swiss Target Prediction (https://www.swisstargetprediction.ch/) databases to obtain compound-related targets. Meanwhile, the targets of nonliver protective ingredients were also screened by HERB (https://herb.ac.cn/) and CTD databases. Targets were only limited to “Homo sapiens” to obtain reliable results. Liver injury was used as the keyword to search for targets in GeneCards (https://www.genecards.org/) and DiGSeE (https://210.107.182.61/geneSearch/) databases. Liver injury targets with above-average relevance scores were selected from the GeneCards database, and liver injury targets of higher-than-average evidence were obtained from the DiGSeE database. Liver injury targets obtained from both databases were deduplicated and uploaded to Uniprot's website (https://www.uniprot.org/) for standardization.The smiles format files for nonliver protective compounds were downloaded from the PubChem database (https://www.omicshare.com/). The Venn function module was used to screen the overlapping targets. Intersecting targets included 556 targets associated with liver injury in the CTD database. Among the 556 targets, targets with liver injury-related scores greater than the average were screened out as direct targets of KP-induced liver injury. Direct targets of liver injury were classified by pharmacological mechanisms.The compound targets and hepatotoxicity targets were uploaded to the OmicShare online platform (https://cn.string-db.org/). The conditions were set as the PPI interaction score >0.4, and the species was restricted to “Homo sapiens.” The results were exported to the TSV format files. For visual analysis of direct targets, the TSV format files were imported into Cytoscape 3.7.1 software, and the “Network Analyzer” function was used to analyze the topology structure of the PPI network. Degree > mean value was set as the cut-off threshold, of which met the previously mentioned screening criteria were core targets.The direct targets were imported into the STRING database (Homo sapiens.” Screening criteria were as follows: (1) false discovery rate <0.01; (2) observed gene count was greater than the average; and (3) literature has reported that signaling pathways were relevant to direct or indirect liver injury. Afterwards, the top 10 signaling pathways and GO terms that met the previously mentioned criteria were filtered out and were visualized by using the histogram and bar chart through Bioinformatics online website (https://www.bioinformatics.com.cn/).In order to clarify the biological functions and related signaling pathways of the targets, the screened direct targets were enriched and analyzed. GO and KEGG functional annotation of direct targets were acquired from the STRING database, with the species limited to “The corresponding targets of the top 10 signaling pathways and relevant compounds were imported into Cytoscape 3.7.1 software to construct the CTP network. The CTP network topological isomerism parameter was analyzed by the network analyzer function. The targets were regarded as key targets of liver injury induced by KP which met the following two criteria: (1) the CTP network topological isomerism parameter of degree value was greater than the mean value and (2) the topology degree value of the PPI network was greater than the average. Compounds with above-average degrees were considered potentially hepatotoxic compounds. Furthermore, combined with the structure of hepatotoxicity warning , the livα  in liver tissues, which were the top 2 inflammatory factors.According to the results of network pharmacology, the ELISA experiments were carried out on IL6 and TNFhttps://www1.rcsb.org/). Molecular docking was carried out on Maestro software  with the following 4 steps: protein preparation wizard, receptor grid generation, ligand preparation, and ligand docking.In order to explore the relationship between liver injury caused by the main components of KP and apoptosis, molecular docking was applied to decipher the interaction between core compounds and hub apoptotic targets. The three-dimensional structure “SDF” files of methoxsalen, obacunone, limonin, and dictamnine were downloaded from the PubChem database. PDB format files of CASP3 (3PD1) and CASP8 (3KJQ) were downloaded from the PDB database . A value of P < 0.05). Meanwhile, compared with the model group, the serum ALT, AST, and AKP levels in the medium-dose group had a rising tendency. There was no significant difference in liver function parameters between the low-dose group and the model group. Biochemical analysis results indicated that KP had a certain degree of liver injury in KM mice when given a high dose of 14.625 g/kg. KP had potential hepatotoxicity when given a medium dose of 4.875 g/kg.Liver function parameters of ALT, AST, and AKP were measured to evaluate the effect of KP on the degree of liver injury based on the immune stress model. As shown in Figures H&E staining was applied to observe the histopathological changes in liver tissues in each group. In the control group, there was a large amount of hepatocyte steatosis, with tiny round vacuoles in the cytoplasm (black arrow) and no obvious inflammatory changes . CompareUPLC-QTOF/MS was used to qualitatively analyze the chemical constituents of KP. The total ion chromatograms (TIC) of KP were scanned in positive and negative ion modes Figures . Based oA total of 1618 component targets were screened from SEA, Swiss target prediction, HERB, and CTD databases after removing the duplicated targets. There were 2381 targets with an above-average relevance score that were filtered through the GeneCards database, and 106 targets with higher-than-average evidentiary results were achieved in the DiGSeE database. Subsequently, 2357 liver toxicity targets were obtained after normalization by the Uniprot database. There were 564 intersection targets that were obtained. Among the intersection targets, 556 targets were associated with liver injury in the CTD database. There were 87 targets with higher-than-average inference scores of liver injury evidence in the CTD database. Consequently, the 87 targets were considered to be direct targets of KP-induced liver injury. Direct targets were classified into 6 categories including the metabolic enzyme family, the transporter family, apoptosis, inflammation, nuclear hormone receptors, and oxidative stress according to the pharmacological mechanisms.β, with respective degrees of freedom of 68, 63, 59, 58, 58, 57, 57, 50, 49, and 49.There were 87 targets uploaded to the STRING database to obtain PPI files, and they constructed a PPI network by using Cytoscape 3.7.1 software. The PPI network contained 87 nodes and 1221 edges, with an average node degree of 28.06 . A totalThe GO enrichment analysis was performed to describe the gene's functions of 87 targets. GO terms include 3 categories: biological process (BP), cellular component (CC), and molecular function (MF). Among them, BP was primarily associated with the cellular process, response to chemicals, metabolic process, cell communication, regulation of cell death, and regulation of the apoptotic process. CC mainly consisted of intracellular organelle, cytoplasm, intracellular membrane-bounded organelle, and endomembrane system. MF was chiefly involved in protein binding, heterocyclic compound binding, organic cyclic compound binding, and ion binding .KEGG enrichment analysis showed that apoptosis, hepatitis B, IL17 signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway, nonalcoholic fatty liver disease, metabolic pathways, the toll-like receptor signaling pathway, bile secretion, and necroptosis were the top 10 signaling pathways . The resβ, MAPK1, CASP3, CASP8, NFKB1, CXCL8, CCL2, CYP2E1, and CYP3A4. A total of 15 compounds had degree values greater than the mean in the analysis of topological isomerism value. According to previous research on the structure-toxicity relationship of hepatotoxic compounds [There were 10 pathways combining 47 active components and 68 targets that were used to construct a CTP network. The CTP network demonstrated 125 nodes  and 386 edges  . Accordiompounds  and liteompounds , the comα levels in the liver were detected in control, model, and high-dose KP groups. Compared with the model group, the IL6 level in the liver was significantly increased in the high-dose of the KP group (α level in the liver was significantly increased in the high-dose of the KP group (Depending on the results of network pharmacology, inflammatory factors could be related to liver damage caused by KP. IL6 and TNFKP group . CompareKP group . The resNetwork pharmacology studies predicted that methoxsalen, obacunone, limonin, and dictamnine might be the main substances causing liver injury, and the mechanism may be related to apoptosis. Therefore, the molecular docking approach examined the interactions between these four major compounds and the apoptotic proteins CASP3 and CASP8. The molecular docking results demonstrated the binding affinity of methoxsalen, obacunone, limonin, and dictamnine to the targets CASP3 and CASP8 Figures . The resin vivo experiments and computer screening models can quickly screen hepatotoxic substances and find the main mechanisms of hepatotoxicity. The advantages of this method save the use of experimental animals, experimental costs, and time.KP has been used in the treatment of psoriasis for more than 30 years . Previouα, CASP3, and CASP8 targets by acting on the IL17 and apoptosis pathways.As the main metabolic organ and excretory organ, the liver is the main target organ for toxic injury . Our finThe results of the “toxic component-target-pathway” network showed that methoxsalen, obacunone, limonin, and dictamnine might be the key compounds of KP-induced liver damage. In recent years, compounds with furans, anilines, quinones, hydrazines, thiophenes, arylpropionic acids, and alkynes in their structures may have potential hepatotoxicity . Furan rα are considered the key mediators of hepatotoxicity [α in the liver of mice. The production of TNFα is closely related to the activation of Kupffer cells. Elevated TNFα can stimulate hepatocyte apoptosis [α can initiate the cleavage of pro-CASP8 and/or −CASP10 to activate CASP8/10, and CASP8/10 directly activates downstream CASP3 [At present, there are many factors and complex mechanisms of liver injury caused by TCM, including liver enzyme abnormality, immunologic reaction, oxidative stress, lipid metabolism disorder, cholestasis, hepatocyte necrosis caused by mitochondrial dysfunction, and the release of inflammatory factors . During toxicity . Our respoptosis . The incam CASP3 . The resin vitro and in vivo experiments.This study also has limitations in studying the material basis and mechanisms of liver injury caused by KP. First, although the research methods of network pharmacology have developed rapidly in the past decade, the accuracy of algorithm-based prediction of related targets of compounds needs to be further improved, and the analysis methods of network topological heterogeneity results need to be more accurate. Second, the screened toxic compounds and their corresponding mechanisms need to be further verified by α, CASP3, and CASP8 targets to exert hepatotoxic effects. Based on the results of network pharmacology, the ELISA experiments showed that KP might increase the IL6 and TNFα levels in liver tissues. Molecular docking results demonstrated that methoxsalen, obacunone, limonin, and dictamnine could interact with the targets CASP3 and CASP8. Our study lays the theoretical foundation for further study on hepatotoxic compounds and their mechanisms of KP.To conclude, we used a research method combining animal experiments, UPLC-QTOF/MS analysis, and network pharmacology to analyze toxic substances and potential mechanisms of KP hepatotoxicity. The results of animal experiments confirmed that KP was toxic to the liver of mice. The components of KP were identified by the UPLC-QTOF/MS analysis method. Through network pharmacologic analysis, methoxsalen, obacunone, limonin, and dictamnine might be the main substances of liver injury caused by KP. Its potential toxicity mechanisms might be via the IL17 signaling pathway and the apoptosis pathway acting on IL6, TNF


Chronic liver disease(CLD) is a slow-developing and long-term disease that can cause serious damage to the liver. Thus far, it has been associated with viral hepatitis, non-alcoholic fatty liver disease(NAFLD), alcoholic liver disease(ALD), hepatic fibrosis(HF), liver cirrhosis (LC), and liver cancer. Qinghao Biejia Decoction (QBD) is a classic ancient Chinese herbal prescription with strong immune-enhancing, anti-inflammatory, and anti-tumor effects. In this study, we used a network pharmacology approach to investigate the molecular mechanisms of QBD in the inflammation-carcinoma transformation process of chronic liver disease. Two key drug targets, MAPK1 and PIK3CA, were screened using network pharmacology and molecular docking techniques, revealing dihydroartemisinin, artesunate, 12-O-Nicotinoylisolineolone, caffeic acid, and diincarvilone A as active ingredients involved in QBD mechanisms. The main signaling pathways involved were the PI3K-AKT signaling pathway and MAPK signaling pathway. In summary, our results indicated that QBD affects the inflammatory transformation of chronic liver disease through MAPK1 and PIK3CA and signaling pathways MAPK and PI3K/AKT. These data provide research direction for investigating the mechanisms underlying the inflammation-carcinoma transformation process in QBD for chronic liver disease. Chronic liver disease (CLD) is a progressive morphology-based chronic disease of the liver, with a long disease course and complex medical history that can severely harm people's health. It is a slow-developing and long-term disease that can cause serious damage to the liver. CLD has been closely associated with viral hepatitis, non-alcoholic fatty liver disease (NAFLD), alcoholic liver disease (ALD), hepatic fibrosis (HF), liver cirrhosis (LC), and liver cancer . It ofteHelicobacter pylori-associated gastritis and stomach cancer, inflammatory bowel disease or polyp and colorectal cancer [According to the cancer-related data from 2015, the number of patients with primary liver cancer significantly increased in China, ranking high in incidence and mortality rate among all malignant tumors , 4. Manyl cancer .In China, patients with chronic liver disease are often treated with traditional Chinese medicine(TCM). The traditional Chinese medicine compounds contain various components characterized by multi-level and multi-link comprehensive regulation and conforming to the complex pathologies of chronic liver disease to a certain extent. Although there is no such disease called chronic liver disease in traditional Chinese medicine, according to the symptom, it belongs to the categories of hypochondriac pain, accumulation, jaundice, tympanites, liver fixity, and abdominal mass . In thisα, allowing valuable time for liver cell regeneration and repair [Qinghao is the dried above-ground part of Artemisia annua L., family Asteraceae. It has been traditionally used as a treatment of mild fever, hemostasis, and skin diseases . It can d repair . Zhimu id repair . Zhimu ed repair . The extd repair . Mudanpid repair , which cd repair . Mudanpid repair . In summNetwork pharmacology analyzes the interaction between drugs, diseases, and their targets through biological information database. It can be used to preliminarily predict the pathways of drug action on diseases in an integrated and systematic pattern, based on which new methods for the disease treatment are found. The study methods include network construction, network analysis, and network verification –27. ChinNon-controllable inflammation has been identified as an early event in multiple cancers. Systematically studying the transformation process of non-controllable inflammation into cancer, exploring important nodes of inflammation-carcinoma transformation, and analyzing the key nodes of complex networks, are the critical steps for achieving early prevention, diagnosis, and treatment of cancer. In this study, we systematically studied the mechanism of Qinghao Biejia Decoction in treating inflammation-carcinoma transformation of chronic liver disease, including ALD, NAFLD, hepatitis B(HB), hepatitis C(HC), HF, LC, and HCC, using network pharmacology and molecular docking technology. The study workflow is shown in http://tcmspw.com/) and comprehensive models, including oral bioavailability (OB) and drug-likeness (DL), were used to screen potential active compounds and predict ADME  properties. According to previous research literature, most network pharmacology articles only used OB ≥30% and DL ≥0.18 as criteria for compound screening, so this method was cited in our study and the rest of ADME attributes were accepted by default. AAH, ARR, MC, RR, and TC were used as keywords for retrieval; OB ≥30% and DL ≥0.18 of components in AAH, ARR, and MC were applied for component candidate assessment. No relevant information on RR and TC was found in TCMSP, so RR and TC were used as keywords for retrieval in the Traditional Chinese Medicines Integrated Database . Since the results of TC in TCMID did not comply with the research range, the relevant chemical component from other literature was supplemented. The molecular structures of chemical components in RR and TC were obtained via PubChem database (https://pubchem.ncbi.nlm.nih.gov/), and the 3D structures were saved as sdf files. Next, the active compounds were screened by SwissADME database (http://www.swissadme.ch/), and more than two items from Lipinski, Ghose, Veber, Egan, and Muegge in gastrointestinal absorption were set as High, and Druglikeness was selected as YES. In the Supplement Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform ; all target proteins of compounds were downloaded, and the repeated ones were deleted. Then, assisted by the UniProt database (https://www.uniprot.org/), all the target protein names were transferred as corresponding gene names. In the supplement The target protein of each active ingredient was predicted using Swiss Target Prediction (http://oncodb.hcc.ibms.sinica.edu.tw/index.htm) and Liverome database (http://liverome.kobic.re.kr/index.php). A gene was selected if it satisfied at least one of the following criteria: (i) experimentally validated in OncoDB.HCC; (ii) occurrence frequency>7 among the various collected gene signatures in Liverome. Duplicate genes were removed from both databases. The genes were normalized using the UniProt database. A total of 566 genes were obtained. In the supplement HCC-related human genes were collected from two databases, OncoDB.HCC database (http://db.idrblab.net/ttd/), GeneCards database (https://www.genecards.org/), DisGeNet database (https://www.disgenet.org/), and OMIM database (https://omim.org/). The target genes obtained from the GeneCards database were screened using a median Relevance Score to obtain the final target genes. The target genes obtained from each gene database were searched one by one through the UniProtKB search function target in the UniProt database, and the species were defined as “human” and “Reviewed” in the retrieval results. After summarizing target genes obtained from each database, duplicate values were deleted, and the total number of target genes was recorded. In the supplement The remaining six chronic liver diseases were ALD, NAFLD, HB, HC, HF, and LC. The target genes of each disease were obtained by searching the Therapeutic Target database (http://bioinfogp.cnb.csic.es/tools/venny/index.html) to obtain drug-disease intersection targets and Venn diagrams.The relevant targets of active components in QBD and the target of each chronic liver disease were introduced into Venny2.1.0 , respectively. They were then searched with Multiple Proteins; the protein type was set as “Homo sapiens” at confidence ≥0.9, network display mode was: interactive svg, and free nodes were hidden. The tsv data files were downloaded and imported into Cytoscape 3.7.2 software for visualization analysis, and the degree centrality (DC) was calculated. Using the Visualize Parameters of Cytoscape to visualize the important nodes of the PPI network, the top 10 targets of chronic liver diseases were screened out to construct the sub-network.Common targets of each disease were imported into the STRING database (http://metascape.org/gp/index.html#/main/step1), with the P value < 0.01. Combined with relevant literature, the important enrichment results were visually analyzed using Bioinformatics (http://www.bioinformatics.com.cn/) and ImageGP (http://www.ehbio.com/ImageGP/index.php/Home/Index/index.html) online mapping websites.The Gene Ontology (GO) biological function and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway enrichment of drug-disease intersection targets were analyzed using the Metascape database  Small Molecule Processing. The key active components involved in therapeutic targets of seven chronic liver diseases were selected according to the construction result of the drug-compound-therapeutic target network and PPI network. The molecular structure diagram was obtained through PubChem, and its 3D structure was saved in sdf format. Next, the file was imported into Chem3D at a minimum Root-mean-squared gradient: 0.0100 for molecular energy minimization. The small molecule compound file was exported in mol2 format.(2) Preparation and Treatment of Protein. According to the construction and analysis of the PPI sub-network, the same targets from the top 10 targets of seven chronic liver diseases were screened and researched in the UniProt database to download key target proteins. The species in Popular organisms was Human, and the protein structure with Reviewed (Swiss-Prot) was selected. In the Structure, the appropriate protein structure was selected. After entering the Protein Data Bank database (PDB), the PDB file was downloaded by selecting the secondary protein structure with high structural similarity and high resolution between the original protein-ligand and the docking chemical composition. The file was imported to Sybyl-X 2.0.0, after which irrelevant original ligands and water molecules were removed from the protein through the Surflex-Dock(SFXC). The selected structure was analyzed, and the result was hydrogenated to calculate and distribute charges. The Automatic protomol docking method was selected to generate the molecular docking file in SFXC format.(3) Molecular Docking. SFXC in the Sybyl-X 2.0.0 was used. Standard and fully automatic SURFLEX-dock Geom software, with an original threshold of 0.50, and the original expansion of 0, the small molecule was docked with the therapeutic target. SFXC calculated the result to determine the affinity between the small molecular compound and the therapeutic target.Among 177 chemical components that were obtained in QBD, 76 were AAH, 32 were ARR, 27 were MC, 20 were RR, and 22 were TC. There were 881 target genes in QBD. The search terms for chronic liver disease and the number of target genes in each database are shown in The intersection of disease targets of seven chronic liver diseases and QBD were 310, 403, 344, 364, 241, 292, and 116, respectively, as shown in The therapeutic targets of seven chronic liver diseases and the drugs and active ingredients of QBD were imported into Cytoscape 3.7.2 software to construct a drug-compound-therapeutic target network of QBD. The network was analyzed and visually processed. As shown in Target of each disease were imported into the STRING database, respectively, and the obtained PPI network was imported into Cytoscape 3.7.2 software. The top 10 key target proteins of DC were screened using the software plug-in. The highest node of DC was the central node in the pathway, in an important position for the targets' interaction. The obtained key target proteins were used to construct the sub-network, as shown in P <0.01. Bioinformatics was used to visualize the first 20 pathways of enrichment results. The output GO enrichment bar graph showing the top 20 enrichment results of biological process (BP), cell composition (CC), and molecular function (MF) is represented in GO and KEGG enrichment of treatment targets was performed via Metascape, with Homo sapiens as the biological species and the threshold of KEGG enrichment analysis was performed on the disease targets of seven chronic liver diseases, after which the DC of each enrichment pathway of the target set was calculated by Cytoscape 3.7.2. The first 20 pathways of the enrichment results of each disease were visualized to obtain the pathway target diagram, as shown in P value, illustrating that the pathway types of the seven chronic liver diseases were similar. However, with the development of the inflammation-carcinoma transformation of liver diseases, the intervention intensity of QBD on pathways of chronic liver diseases was different in different periods.Fourteen pathways, including pathways in cancer, microRNAs in cancer, bladder cancer, and other pathways, which ranked the highest among seven chronic liver diseases, were selected to output KEGG clustering heat maps . The cor corresponded to the key active ingredients involved in QBD as dihydroartemisinin (DHA), artesunate (ART), 12-o-nicotinoylisolinolone, caffeic acid (CA), and diincarvilone A  also known as ERK2, is a member of the MAPK family and the core signal transductor of the MAPK/ERK pathway , whose cα, p110β, or p110δ, encoded by PIK3CA, PIK3CB, and PIK3CD genes, respectively) and a regulatory subunit . The key genes involved in the PI3K/AKT signaling pathway are PI3K and AKT, so this pathway is directly named after these two genes. Class IA PI3K can activate downstream signals, such as AKT, having an effect on important cellular signal transduction pathways, including growth, metabolism, migration, proliferation, and survival. The genome changes of the PI3K signaling pathway have been associated with many different tumors. The most common cancer-related alterations in the PI3K pathway are mutations activating PIK3CA-encoding p110α. Besides, the PIK3CA mutations have also been found in some non-malignant diseases [PI3K (phosphatidylinositol 3-kinase) is a phospholipid kinase family, classified in Type IA, IB, II, and III based on structural characteristics. Class IA PI3K is a catalytic subunit (p110diseases . The PI3diseases . Other sdiseases . Currentdiseases . HBV cordiseases . All theThe interaction between MAPK and the PI3K/AKT pathways has been found in skin melanoma. Interestingly, all these studies indicate that the two pathways are activated in parallel and describe the evidence of a correlation between PI3K/AKT and MAPK/ERK1/2 cascades. There are multiple intersections between these two pathways, and their synergies determine the outcomes of cells. The PI3K/AKT and MAPK pathways interact at different stages of signal transmission . Zhuang All of the above evidence indicate that MAPK1 and PIK3CA may participate in the inflammation-carcinoma transformation process of chronic liver disease . The devBy using network pharmacology and molecular docking technique, we revealed the effect of QBD with DHA, ART, and CA as the active ingredients, MAPK1 and PI3K as the important targets, and the important pathways of MAPK and PI3K/AKT on the intervention on the inflammation-carcinoma transformation process of chronic liver disease. These findings provide a research direction for investigating the mechanism underlying the inflammation-carcinoma transformation process in QBD for chronic liver disease, identifying potential therapeutic targets, and preventing the transformation from non-controllable inflammation to cancer. However, our conclusions are based on theoretical simulations, so more experiments are needed to further verify our findings.


Hui Yang Jiu Ji (HYJJ) decoction has been applied as a prescription of traditional Chinese medicine for the treatment of chronic heart failure (CHF). However, its comprehensive molecular mechanism remains unclear now. Our study aimed to explore the possible function and lncRNA-miRNA regulation networks of HYJJ on CHF induced by doxorubicin (DOX) in rats. Our study showed that HYJJ could recover cardiac function and alleviate myocardial injury of DOX-induced CHF. Besides, HYJJ had an effect on restraining myocardial apoptosis in CHF rats. Moreover, RNA-sequencing and bioinformatics analysis indicated that among a total of 548 significantly up- and down-regulated differentially expressed (DE) long noncoding RNA (lncRNA), 511 up- and down-regulated DE miRNAs were identified. Cushing's syndrome and Adrenergic signaling in cardiomyocytes were common pathways between DE-lncRNAs-enriched pathways and DE-miRNAs-enriched pathways. Finally, we observed a new pathway-MSTRG.598.1/Lilrb2 pathway with the HYJJ treatment; however, it needs further studies. In conclusion, this study provided evidence that HYJJ may be a suitable medicine for treating CHF. Moreover, several pivotal miRNAs may serve important roles in these processes by regulating some key miRNAs or pathways in CHF. Chronic heart failure (CHF) was defined as a group of clinical syndromes caused by ventricular filling or impaired ejection ability when some cardiac diseases develop to the terminal stage . Owing tHui Yang Jiu Ji decoction (HYJJ) was a traditional decoction often used for patients with acute gastroenteritis, excessive vomiting, diarrhea, shock, myocardial infarction, heart failure, and some acute symptoms. For example, Jin et al.  reportedTranscriptome sequencing utilizes high-throughput sequencing technology to reflect the expression levels of miRNA and noncoding RNA . Transcrin vivo. In addition, transcriptome sequencing technology was used to analyze the lncRNA/miRNAs expressions of CHF model rats after HYJJ treatment in order to find the possible molecular mechanism and key targets of HYJJ intervention in CHF.In the present study, we investigated the cardioprotective effect of HYJJ in DOX-induced CHF Hui Yang Jiu Ji decoction (HYJJ) was provided by the Second Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine. Digoxin was purchased from Shanghai Shangyao Xinyi Pharmaceutical Factory Co., Ltd . HYJJ and digoxin were administered at 7.25 g/kg/d and 0.027 mg/kg/d by intragastric administration, respectively. DOX hydrochloride was obtained from Shanxi Pude Pharmaceutical Co., Ltd  and configured as a test solution with a concentration of 0.8 mg/ml by 0.9% saline.Male Sprague–Dawley (SD) rats, weighing 200 ± 20 g, were obtained from Shanghai Slack Laboratory Animal Co., Ltd.  and housed under a 12 h–12 h dark/light cycle at 21 ± 2°C and 30–70% relative humidity, with freely available rodent chow and water.Rats in the CHF groups were intraperitoneally injected with DOX hydrochloride at a concentration of 1.5 mg/kg (twice a week for four weeks). Correspondingly, the control group was intraperitoneally injected with saline. After the successful construction of the CHF model, rats were divided into the following three groups: CHF, HYJJ (7.25 g/kg), and digoxin (0.027 mg/kg). HYJJ and digoxin groups were administered intragastrically with corresponding drugs once a day for two consecutive weeks. The rats in the control and CHF groups received an equal volume of normal saline.Rats were anesthetized using 1% sodium pentobarbital, then the left ventricular internal diameter at end-diastole (LVIDd), left ventricular internal diameter at end-systole (LVIDs), ejection fraction (EF), and fractional shortening (FS) were measured by transthoracic echocardiography using a Vivid-i echocardiography machine  equipped with an 11.5 MHz transducer.The myocardial tissues were collected and embedded in paraffin and sliced into sections. The tissues were then subjected to the One Step TUNEL Apoptosis Assay Kit on the basis of the manufacturer's protocol . TUNEL-positive cells were imaged under a fluorescence microscope .μm sections, deparaffinized, and rehydrated as previously described. After rehydration, the sections were stained with HE Staining Kit followed by the manufacturer's instructions . The stained sections were then viewed under the ordinary optic microscope .The myocardial tissues were fixed in 4% paraformaldehyde, embedded in paraffin, sliced into 5 The paraffin sections of myocardial tissues were dewaxing, alcohol dehydration, and antigen retrieval. Then, the sections were washed three times for five minutes each time by PBST and blocked by 2% BSA. Sections were then treated with primary antibody cleaved caspase-3  at 4°C overnight. After six times washing, the sections were incubated with fluorescein isocyanate (FITC)-conjugated goat anti-mouse  as a secondary antibody at 37°C for 30 min. Finally, images were acquired using a fluorescent microscope  at 100x magnification. Moreover, the expression level was quantified by mean fluorescence intensity (MFI).Serum from rats in each group was collected, and the levels of cardiac troponin I (cTNI) and N-terminalpro-brain natriuretic peptide (NT-proBNP) were determined by ELISA based on the manufacturer's instructions. The ELISA kits were both purchased from Shanghai Kanglang Biological Technology Co., Ltd . The OD value of each well was immediately read at 450 nm.β-actin using ImageJ software.To confirm the expression level of c-caspase 3 in myocardial tissues, myocardial tissues were lysed with RIPA buffer. Lysates were separated on 10% SDS-polyacrylamide gel and proteins were transferred to polyvinylidene difluoride (PVDF) membranes. Then, the membranes were blocked with 5% nonfat milk for 1 h at room temperature, and membranes were incubated with the corresponding primary antibody at 4°C overnight following incubation with horseradish peroxidase-conjugated secondary antibody at room temperature for 1 h. Protein expression was detected with high sensitivity ECL chemiluminescence detection kit . The bands' intensity was expressed as fold change by normalizing the data to the values of https://github.com/Zhanghuanhaoxuexi/Analysis-of-function-role-and-long-non-coding-RNA-expression-in-chronic-heart-failure-rats-treated-w, No: dc211eb).The sequencing work was performed by Shanghai Paisennuo Biotechnology Co. LTD. In brief, total RNA was extracted from the myocardial tissue and removed rRNA. The extracted RNA was interrupted to a 300 bp fragment by ion interruption and constructed the library through reverse transcription. Then, the library was sequenced using the next-generation sequencing (NGS) and Illumina HiSeq Sequencing platform. Clean data obtained by filtering raw data were used for the identification of lncRNA and mRNA. Finally, Gene Ontology (GO) analysis and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analysis were used for the prediction of action pathways. The analysis data has been uploaded to GitHub . One-way analysis of variance (ANOVA) or two-way ANOVA was used to compare the difference among multiple groups followed by post hoc tests. The diagram of modeling and grouping in this experiment is shown in To further explore the function of HYJJ in CHF rats, TUNEL assay and WB were performed. The data showed that the obviously enhanced apoptosis rate was observed in the CHF group, while HYJJ and digoxin treatment could prominently downregulate the apoptosis rate . AdditioIn this part, we analyzed the effect of HYJJ decoction on the expression of lncRNA in CHF rats through transcriptome. After normalizing the abundance of lncRNA genes, the distribution of total lncRNA genes was provided in The Gene Ontology (GO) functional enrichment results showed that HYJJ affected the biological processes (BP), cellular components (CC), and molecular functions (MF) of CHF rats' hearts . CompareThe specific signaling pathways enriched with lncRNA DE genes were identified using tools at the Kyoto Encyclopaedia of genes and genomes (KEGG) database. As shown in Next, we explored the effect of HYJJ decoction on miRNA expression profiles. The distribution of total miRNA genes is provided in The gene ontology (GO) functional enrichment results showed that HYJJ affected the biological processes (BP), cellular components (CC), and molecular functions (MF) of miRNA in CHF rats' hearts . CompareThe specific signaling pathways enriched with miRNA DE genes were also identified using the KEGG database. As shown in Finally, the potential interactions of miRNA and lncRNA were predicted. Coding-noncoding gene coexpression networks (CNC networks) were drawn based on the correlation analysis between miRNAs and miRNAs. There were many lncRNA-miRNA pairs that act on each other in both cis- and trans-fashion in the mod vs. con comparison ; howeverThis study has shown that the HYJJ could inhibit myocardial apoptosis and alleviate myocardial injury in DOX-induced CHF rats. Then transcriptome sequencing technology was used to analyze the lncRNA/miRNAs expressions of CHF model rats after HYJJ treatment. Through DE lncRNAs and DE miRNA analyses, we found HYJJ had an impact on the biological processes (BP), cellular components (CC), and molecular functions (MF) of CHF rats' hearts, and HYJJ improved DOX-induced CHF via multiple signaling pathways.The apoptosis of cardiomyocytes is known to be an important mechanism of myocardial injury. Many studies showed that myocardial apoptosis was involved in the pathogenesis of acute myocardial infarction , ischemiAs previously reported, transcriptional profiling has been utilized extensively in human heart failure to explore the new pathways involved in the complex disease , identifβ, mTOR, P70S6K, and eIF-4E [KEGG results showed that the lncRNA DE genes were mainly enriched in the PI3K-Akt signaling pathway in the mod vs. con comparison. In cardiac hypertrophy, the PI3K-Akt signaling pathway is activated by many types of cellular stimuli or toxic insults to regulate fundamental cellular processes including protein synthesis, proliferation, and survival, via its downstream signals, such as AKT, GSK3d eIF-4E –32. Mored eIF-4E , 34, whid eIF-4E , 34, oncFurther KEGG results showed that there were 13 pathways significantly enriched with lncRNA DE genes and 18 pathways significantly enriched with miRNA DE genes in the HY group compared with the mod group. Among them, Cushing's syndrome and Adrenergic signaling in cardiomyocytes were common pathways between lncRNAs-enriched pathways and miRNAs-enriched pathways.Glucocorticoid excess in Cushing's syndrome contributes to complications such as increased visceral adipose tissue, obesity, impaired glucose tolerance, diabetes mellitus, hypertension, hyperlipidemia, osteoporosis, and neuropsychiatric comorbidities, and is associated with a significantly increased mortality –38. Typeβ-adrenergic receptors on cardiomyocytes [β-adrenergic receptors [β-AR leads to large increases in the generation of arrhythmogenic spontaneous Ca2+ waves (SCaWs), especially in cells from heart failure animal models [β-adrenergic receptors 3 [Neurotransmitter noradrenaline released by the sympathetic neurons increases heart rate by activating myocytes . In the eceptors . Activatl models . It has eptors 3 , 44. In In addition, we also observed hypertrophic cardiomyopathy (HCM) and arrhythmogenic right ventricular cardiomyopathy (ARVC) were enriched with DE miRNA. Cardiomyopathy is the leading cause of heart failure and mainly consists of the following four types: DCM, HCM, restrictive cardiomyopathy, and ARVC , so we sMAPK signaling pathway was observed to enrich with DE miRNA. MAPK pathway regulation and controlled expression of MAPK target genes are essential in mediating diverse physiologic outcomes including cellular transformation, tumorigenesis, developmental disorders, cardiac hypertrophy, and heart failure . MoreoveFinally, one lncRNA-miRNA interaction of MSTRG.598.1 and Lilrb2 was observed in the HY vs. mod comparison. Lilrb2 is leukocyte immunoglobulin-like receptor B2. A study showed that high expression of Lilrb2 was identified in patients with myocardial infarction , 52, andThere are a few limitations of the present study. First of all, the sample size is limited which does not find more differences; therefore, more samples will be needed in the future. Notably, this was a pilot study and results must be considered preliminary but the initial findings of the study would be helpful to the follow-up study confirmation. As a next step, our group will conduct a further study to validate those molecular mechanisms.The present study showed the cardioprotective effects of HYJJ in CHF via reducing myocardial apoptosis. Transcriptome results revealed that HYJJ might contribute to improving CHF via pathways, including Adrenergic signaling in cardiomyocytes, neuromodulation signaling pathway, cAMP signaling pathway, MAPK signaling pathway, and MSTRG.598.1/Lilrb2 pathway. Further studies would contribute to a better understanding of the cardioprotective roles played by HYJJ in the hypertrophied arena.


Coptis chinensis, Anemarrhena asphodeloides, rhubarb wine, Cortex Moutan, Rehmannia glutinosa, and dried ginger) in the type II diabetes therapy.  In view of network pharmacology and molecular docking technology, to explore the targets as well as effect mechanism of the Huanglian Jiangtang formula (including  TCMSP and Batman database (DB) were used to retrieve the chemical components and action targets of drugs; GeneCards, OMIM, TTD, DrugBank, and other databases were applied to screen the disease targets. We used the UniProt DB to annotate the targets before building the drug-compound-target network with Cytoscape 3.9.1. We also exploited the String DB to construct the protein-protein interaction (PPI) network. In addition, the targets for the treatment of type II diabetes were searched in the DrugBank, OMIM, GeneCards, and TTD database; then, we utilized Venn to intersect the key targets for the therapy of type II diabetes and active ingredient targets to obtain common targets. Furthermore, we exploited the common targets using GO and KEGG enrichment analysis method. The common targets and core components were analyzed by molecular docking using the AutoDock software.  A total of 61 effective components of this compound were screened out; drugs and type II diabetes have 278 common targets; the PPI network screened core target proteins such as CDKN1A, CDK2, and E2F1 with the help of molecular docking technology; the three main compounds including quercetin, kaempferol, and gamma-aminobutyric acid were obtained. Besides, the key target proteins had excellent binding properties with the main components. The signal pathways of six compound interventions in type II diabetes were mostly related to cancer, cocaine addiction, aminoacyl-tRNA biosynthesis, glycine, serine, threonine metabolism, platinum drug resistance, and other pathways, according to the KEGG enrichment analysis method.  In the treatment of diabetes, the Huanglian Jiangtang formula has sorts of properties especially in the aspects of composition, target, and pathway. Its molecular target and mechanism of action may be related to pathways in cancer, cocaine addiction, aminoacyl-tRNA biosynthesis, glycine, serine, threonine metabolism, platinum drug resistance, and other pathways. This conclusion can provide theoretical support and science for further research. Diabetes mellitus (DM) is a typical endocrine and metabolic disorders marked by high blood glucose levels. As a common and frequently occurring disease in modern lifestyles, DM is also a refractory disease requiring lifelong treatment. Although it will not cause severe reactions in a short time, serious complications will be induced if it is not treated in time. Diabetes complications include retinopathy, nephropathy, and peripheral neuropathy .Due to its extremely complex etiology and pathogenesis, it has not been fully clarified so far. Clinical treatment is generally based on lifestyle interventions such as diet and exercise, and hypoglycemic drugs are used for treatment. For example, chlorpropamide and clofibrate can treat diabetes insipidus complicated with diabetes . Insulinβ Cells, improving insulin resistance, inhibiting glucosidase activity, inhibiting glucose absorption in the body, promoting glucose utilization, improving the level of oxidative stress, and other ways to play a hypoglycemic role. The promotion of the traditional Chinese medicine as a therapy for diabetes relies on the findings of research on individual traditional Chinese medicines that have been shown to have a hypoglycemic impact [In addition, research has demonstrated that bone therapy structural diagnosis and bone therapy manipulative treatment have superior efficacy in the management of diabetes and the comorbidities associated with the condition . Becausec impact .Paeonia lactiflora Pall, and Anemarrhenae Rhizoma Decoction in order to explore the effective components and important ways to treat diabetes. They came to the conclusion that the combination of molecular docking and network pharmacology is a feasible strategy to explore the bioactive components and mechanisms of traditional Chinese medicine. At the same time, they clearly understood that Cinnamomi Ramulus, Paeonia lactiflora Pall, and Anemarrhenae Rhizoma Decoction are all able to successfully treating diabetes through the use of multiple elements, multiple targets, and many approaches [Emerging network pharmacology has been used in recent years to screen drug molecular action targets using computer simulation and various databases. It also predicts the signal pathway and action mechanism of traditional Chinese medicine in the treatment of diabetes, and it carries out experimental verification when it is required. It is derived from system biology as its foundation and uses computer simulation and various databases to screen drug molecular action targets, to explain the process of disease development, and through signal pathway analysis, and it possesses the qualities of “multigenes, multitargets, multiple pathways, and integrated regulation,” all of which are congruent with the dialectical style of thinking that is utilized in the traditional Chinese medicine. For instance, Zhang et al. used molecular docking and network pharmacology to identify Cinnamomi Ramulus, proaches .Coptis chinensis (HL), Anemarrhena asphodeloides (ZM), wine-processed rhubarb (JDH), Cortex Moutan (DP), Rehmannia glutinosa (SDH), and dried ginger (GJ), we explored the target and pathway of hypoglycemic effect of this compound according to the network pharmacology and molecular docking technologies. The flow chart of our result is presented in Accordingly, to explore the hypoglycemic mechanism of the compound of https://tcmspw.com/tcmsp.php); PubChem (https://pubchem.ncbi.nl-m.nih.gov/); BATMAN-TCM; Swiss Target Prediction (https://www.swisstargetprediction.ch/); Uniprot (https://www.uniprot.org/); OMIM (https://mirror.omim.org/); DrugBank; GeneCards (https://www.genecards.org/); TTD-DatabaseCommons (https://ngdc.cncb.ac.cn/databasecommons/database/id/4948); String (https://cn.string-db.org/); Metascape (https://metascape.org/gp/index.html#/main/step1); Cytoscape (version 3.9.1); PDB (https://www.rcsb.org/); PyMOL were used.TCMSP  . Through in-depth analysis of go enrichment, this study discusses the biological process, molecular function, cellular components, and other potential targets of the compound bioactive components in the treatment of DM; KEGG enrichment analysis was used to discover the pathway distribution of potential targets of the compound active ingredients in the treatment of DM.Metascape was used to analyze the enrichment of go and KEGG pathways, and the results were visualized through a bubble diagram. More genes are represented by a larger bubble, and the higher the importance of enrichment is indicated by a darker hue. In order to identify and clarify the roles of transcription factors from three perspectives, namely, biological processes (BPs), cell components (CCs), and molecular functions (MFs), annotation is used in gene ontology (GO). Besides, information about the genome, biochemistry, and normal functioning is combined in the database known as KEGG. Accordingly, we uploaded the abbreviation of the intersection target obtained in the previous step to the David database in gene symbol format for go enrichment analysis, and the variation is statistically meaningful, according to KEGG enrichment analysis , 15 from Anemarrhena asphodeloides (ZM), 16 from wine-processed rhubarb (JDH), 11 were from Cortex Moutan (DP), and 5 were from dried ginger (GJ). In Supplementary Rehmannia glutinosa (SDH). Detailed information on SDH is listed in Supplementary A total of 61 active components were found in the compound using TCMSP DB with the standards of OB of more than 30% and DL of more than 0.18, of which 14 were from By using targets prediction function in the TCMSP DB, corresponding target protein was found through data screening. Then, the duplicate and invalid values were deleted, and 339 compound action targets were obtained. Introduce the compound drugs, drug active ingredients, and targets into Cytoscape 3.9.1 and build the relationship network diagram of compound chemical active ingredients and predicted targets, as shown in Through the disease target database under item 1.1, the weight of the targets obtained from each database was removed, and finally, 2218 DM-related targets were obtained. Match with the targets of the effective ingredients of the compound, construct the “drug disease” targets Venn diagram, and get a total of 278 common targets to provide relevant data for subsequent PPI and GO enrichment analysis, as shown in Utilizing String database DB, enter the intersection gene of compound prescription and DM to acquire the PPI network as depicted in To obtain the biological effects of key target proteins, GO biological function enrichment analysis was carried out on the intersection targets. It was found that the main targets were enriched on 969 entries, including 706 BP, 174 CC, and 89 MF. BP showed that it may be related to biological processes such as cellular amino acid metabolic process and response to inorganic substance L and alpha-amino acid transmembrane transport. Shopit et al. have researched that DM is closely related to the above processes . The res6The purpose of the KEGG pathway analysis is to obtain the pathways enriched by the key targets and carry them out on the key targets. According to the findings, the most important targets of compound therapy for DM were enriched on 104 pathways, including pathways in cancer, cocaine addiction, aminoacyl-tRNA biosynthesis, glycine, serine, threonine metabolism, platinum drug resistance, and other pathways. The top ten results of the KEGG enrichment study are represented by bubbles in −1, indicating that the binding is excellent. With the help of PyMol 2.5.0 software, the molecular docking visualization of gamma-aminobutyric acid, quercetin, kaempferol, CDK2, E2F1, and CCND1 was carried out in From this analysis, the 6 target proteins with the highest importance values obtained in the PPI network were docked. The purpose of this docking was to investigate whether the primary bioactive constituents of the compound played a role in hypoglycemia through the compound's key targets, which were CDK2, E2F1, CDKN1A, CDK1, and CCND1 and CCNB1. Aiming at obtaining the crystal structure of key targets from the database, we downloaded PDB format files and used AutoDock 4.2.6 software to dock the compounds and target proteins one by one (as shown in Table 3). It can be seen from Supplementary Table Coptis chinensis, Anemarrhena asphodeloides, Rhubarb, Cortex Moutan, Rehmannia glutinosa, and dried ginger. Protein interaction network analysis and KEGG enrichment analysis showed that the functional proteins corresponding to the five genes CDK2, E2F1, CDKN1A, CDK1, and CCND1 and CCNB1 may be the key proteins for the compound to play the role of hypoglycemic; the KEGG enrichment analysis obtained seven signal pathways: protein domain specific binding, protein homodimerization activity, protein kinase activity, DNA-binding transcription factor binding, ligase activity, cyclin-dependent protein serineL-amino acid transmembrane transporter activity, calcium ion binding, and amino acid binding. At present, the compound drug researched by Quazi et al. has proved that the extract possesses numerous classes of chemicals such as alkaloids, glycosides, tannins, polyphenols, and terpenoids, which can contribute to the antidiabetic activity through alpha-amylase inhibition [In view of the research method of network pharmacology, this work studies the hypoglycemic mechanism of the compound active ingredients composed of HL, ZM, JDH, SDH, DP, and GJ; the results showed that the compound had 54 active compounds, as well as 2218 type 2 DM targets, were found. The two summarized and obtained 278 common hypoglycemic targets of the compound composed of hibition , 13.Coptis chinensis have a significant synergistic effect on reducing blood glucose [Anemarrhena asphodeloides to reduce blood glucose.Traditional Chinese medicine is commonly used to treat diabetes, and the studies have shown that some traditional Chinese medicine has a better effect on reducing blood glucose, such as Coptis polysaccharide, and other substances in  glucose . Further glucose . It is tThere are obvious differences between network pharmacology and traditional pharmacology . The bigHowever, if we only use network pharmacology to explore a certain mechanism of drugs, it will inevitably be biased and limited, because several omissions cannot be ignored . First,