A large body of literature has shown that ginseng had a role in diabetes mellitus management. Ginsenosides are the main active components of ginseng. But what ginsenosides can manage in diabetic are not systematic. The targets of these ginsenosides are still incomplete. Our aim was to identify which ginsenosides can manage diabetes mellitus through network pharmacology and molecular docking. To identify the targets of these ginsenosides. In this work, we retrieved and screened ginsenosides and corresponding diabetes mellitus targets across multiple databases. PPI networks of the genes were constructed using STRING, and the core targets were screened out through topological analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed by using the R language. Finally, molecular docking was performed after bioinformatics analysis for verification. Our research results showed that 28 ginsenosides in ginseng might be against diabetes mellitus by modulating related proteins such as VEGFA, Caspase 3, and TNF-α. Among the 28 ginsenosides, 20(R)-Protopanaxatriol, 20(R)-Protopanaxadiol, and Ginsenoside Rg1 might play a significant role. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analysis showed that the management of diabetes mellitus by ginsenosides may be related to the positive regulation of reactive oxygen metabolic processes, associated with the insulin signaling pathway, TNF signaling pathway, and AMPK signaling pathway. Molecular docking results and molecular dynamics simulation showed that most ginsenosides could stably bind to the core target, mainly hydrogen bonding and hydrophobic bond. This study suggests the management of ginseng on diabetes mellitus. We believe that our results can contribute to the systematic study of the mechanism of ginsenosides for the management of diabetes mellitus. At the same time, it can provide a theoretical basis for subsequent studies on the management of ginsenosides in diabetes mellitus. More than 400 million people already had DM, and the number of people with the disease continues to increase worldwide2. According to the International DM Federation, there will be 700 million people with DM by 20453. Although a growing number of researchers had discovered effective drugs that can manage DM, a complete and fundamental cure for type 2 DM remains difficult4. Currently, the drugs that can manage DM were mainly insulin or its peptide derivatives. In addition, dietary control can also contribute to the management of DM5. It is important to note that insulin and its peptide derivatives need to be injected intravenously, which can cause inconvenience to diabetic patients. More importantly, the long-term use of such chemical drugs may be harmful to the body of diabetic patients6. For these reasons, scholars have turned their attention to natural chemical-based substances with less toxic side effects, such as traditional herbal medicines. Excitingly, the management of DM by Chinese herbs has been clearly documented in classical Chinese medical writings7.Diabetes mellitus (DM) is a non-communicable metabolic disease characterized by chronic hyperglycemia. It had become the third epidemic following cardiovascular diseases and tumors. Significant and persistent hyperglycemia can lead to dysfunction in various cell types, efficiently inducing complications such as nephropathy, retinopathy, angiocardiopathy, cerebrovascular diseases, and neuropathyPanax ginseng C.A. Meyer) had a long history of use and was edible in China and had been widely recorded in various ancient texts of Chinese medicine7. In China, ginseng was used to treat Xiaoke disease, which begins with overeating and ends with dramatic weight loss. The clinical symptoms of this disease were almost identical to those of DM mellitus8. Studies have shown that steroidal structure is responsible for its multiple pharmacological activities, enabling ginsenosides to be absorbed to control biological activities at the transcriptional level9. Ginsenosides could manage the concomitant conditions of DM, such as anti-angiogenic, anti-apoptotic, and hepatoprotective11. However, research on which ginsenosides in ginseng play a role in managing DM is still incomplete. Which ginsenosides act on which targets regulate which signaling pathways and which biological processes (BP) are involved in managing DM have not been systematically reported. Importantly, it may be difficult to obtain ginsenosides in large quantities and with high purity but with low content in ginseng, which is not conducive to the conduct of experiments. Moreover, DM models often require a long modeling time in vivo experiments12. These make routine experiments face many difficulties.Ginseng . We selected components with OB ≥ 30% and DL ≥ 0.18 as targets. The ginsenosides managed diabetic were then searched for from the reported papers to look for the active ingredients. However, the proven ginsenosides were not removed for the above qualification. The ginsenosides were downloaded SDF format files from the PubChem database (https://pubchem.ncbi.nlm.nih.gov). We used Swiss Target Prediction (http://www.swisstargetprediction.ch/) databases to obtain the targets of the ginsenosides. It be recorded when the predicted target probability > 0.1. Due to some ginsenosides had too many targets, such as ginsenoside 20(R)-Protopanaxatriol (PPT) had 94 targets. Too many targets will be detrimental to the subsequent analysis. Therefore, for ginsenosides with too many targets, we only selected the top 20 targets associated with DM for subsequent analysis. The UniProt database (https://www.uniprot.org/) was used to convert target protein names into target gene names to standardize gene names18.First, we obtained the active ingredients and targets in ginseng through the database and analysis platform of Traditional Chinese Medicine Systems Pharmacology , the Online Mendelian Inheritance of Human Beings , the Pharmacogenetics and Pharmacogenomics Database , the Therapeutic Targets Database , and the Drugs and Drug Target Information Databases DM-related disease target genes  were integrated18. Finally, we used BioLadder (https://www.bioladder.cn/web/#/pro/index) to identify and visualise the intersecting targets of both ginsenosides and disease targets.Next, the GeneCards  to obtain their reciprocal relationships19. Selection parameters were set to “Homo sapiens”, and the confidence level was set at 0.400 for the minimum required interaction score, and hid ligand disconnected nodes in the network. The PPI network was visualized by the Cytoscape 3.7.0 (https://cytoscape.org/) software. The network analyzer plugin in Cytoscape 3.7.0 was used to calculate topological parameters in the network. DC (Degree count) was used as the key indicator and BC , CC , EC , LAC , and NC  were used as secondary indicators to screen the core targets21. Finally, the key targets of the component ginsenosides against DM were obtained.We inputted the intersection targets into STRING 11.0 (https://www.r-project.org/). The results were ranked according to the number of molecules in pathways with a cut-off value (P < 0.05)18. Plotting histograms of GO and KEGG analysis using R. We collected some significant and DM related targets. These targets are present in the top 15 BP or signaling pathways. We used OringPro 8.6 (https://www.originlab.com/software) to draw a component-target-BP or signaling pathway parallel graph to show their relationship to each other. At the same time, we obtained the signal pathway map from the KEGG database (https://www.kegg.jp/kegg/kegg1.html)22.All intersecting targets were analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment by the Bioconductor cluster analyzer of R 4.0.2 (https://www.rcsb.org/). The 2D structures of active substances from ginseng were sought by the PubChem Database23. The ChemBio Draw 3D (https://www.chemdraw.com.cn/ChemBio3D.html) and Autodock Tool was used to optimise the structure of ginsenosides and receptor proteins and to convert the two formats24. PyMol (https://pymol.org/2/) was used to optimize receptor proteins25. We used Chem3D software to minimize the energy of the ligand. We used Pymol software to remove water and increase hydrogen bonds in the acceptor protein. Finally, AutDockTools were used to draw the grid and unify. Autodock Vina was used for molecular docking to obtain the conformation with the lowest binding energy during docking. In addition, we used standard molecules and core targets for molecular docking and molecular dynamics analysis. The same binding pocket of protein molecules was used in the docking process. After, the lowest energy binding profile was visualized. Discovery Studio and PyMol software were used to visualize docking results26.We obtained the structure of the target protein from the RCSB PDB database  = G (Complex)-G (Receptor)-G (Ligand) , where “G (complex) is total free energy of the ligand–protein complex, G (receptor) and G (ligand) are total free energies of the isolated protein and ligand in the solvent, respectively. The molecular mechanic’s potential energy plus the energy of solvation may be used to determine the total free energy of any of the three entities stated . We used the “amber-mmpbsa module” tool to perform binding energy calculations. The stable trajectory observed between the 90–100 ns was chosen for the binding energy calculation by selecting representative snapshots with an interval of 50 framesVeen diagram was drawn as shown in Fig. We obtained 28 ginsenosides through the TCMSP database combined with literature supplementation. These components and their information were presented in Table The core target network diagram was analyzed using Cytoscape 3.7.0. The results showed that 99 interacting target proteins were obtained by screening the String database. The core target network graph was composed of 99 nodes and 829 edges. Interactions were represented by each edge between proteins. The network analyzers identified 9 highly connected nodes  as significantly core targets. A total of 99 protein–protein intersection targets were obtained and visualized in the PPI network . There were 2029, 56, and 153 GO terms associated with BP, cellular components, and molecular functions, respectively  was an important basis to measure whether the system was stable or not. The mTOR-Protopanaxatriol, STAT3-Ginsenoside Rh4, TNFα-Ginsenoside Rg1, and IL-1β-Ginsenoside Rh2 systems were consistently lower fluctuation throughout the molecular dynamics simulations. Their average RMSD value were 0.21299 ± 0.00016 nm, 0.24917 ± 0.00021 nm, 0.28859 ± 0.00019 nm, and 0.34124 ± 0.00030 nm. The JUN-Protopanaxadiol and VEGFA-Ginsenoside Rh1 systems fluctuated slightly throughout the molecular dynamics simulations. Their average RMSD value were 0.65815 ± 0.00115 nm. The protopanaxadiol was unstable against JUN. But may be suitable drug candidate for JUN.  on type 2 DM induced by HFD/STZ via down-regulation of PEPCK and G6Pase expression in the liver37. We found that CK could provide beneficial anti-diabetic effects in DM mice, and this protective effect may be mediated by preventing β-cell apoptosis by inhibiting the AMPK-JNK pathway38. The predicted results of the present work almost coincide with the findings of our group.Our group conducted some research on ginsenosides to manage DM. The ginsenoside 20(S)-Ginsenoside Rg3 had a protective effect on animal model DM by regulating the MAPK/NF-κB signaling pathwayThe results of KEGG signalling pathway enrichment analysis indicated that the above signalling pathway might be a significant signalling pathway for ginsenosides to manage DM. However, whether the unreported saponins in ginseng can manage DM mellitus and its mechanism of action are still unclear. Therefore, this study aimed to investigate the managing effects of ginsenosides on DM and their possible mechanisms of action based on network pharmacology and the molecular docking methodology.40. TNF is an important pro-inflammatory factor that can manage DM by reducing insulin signaling through phosphorylation of serine41. Moreover, TNF-α is closely associated with diabetic nephropathy and vascular dysfunction42.As seen above, ginsenosides, as the main active ingredient, can be effective against DM. These previous reports provide sufficient support for our next step to explore the molecular mechanism of components obtained from ginseng to manage DM. In this work, the ginsenosides and their action targets were collected by data mining. In the Venn diagram of the intersection of components in ginseng and DM, 99 cross targets of protein–protein, and 9 core targets were screened by Cytoscape 3.7.0 software. VEGFA plays a major role in endothelial cell growth and angiogenesis and is an essential factor. There were considerable evidence that when DM occurs it often lead to overexpression of VEGFA, which can have a detrimental effect on the body44. This will lead to further deterioration of the disease. These conditions increased the likelihood of cardiovascular disease in people with DM. When a patient suffers from concomitant cardiovascular disease, it was not conducive to the management of DM, forming a vicious circle45. Fortunately, we found that ginsenosides may have a certain regulatory effect on the regulation of blood vessel diameter and vascular process in the circulatory system. When DM occurs, the body's skin healing ability is greatly reduced46. BP results show that ginsenosides have a regulatory effect on epithelial cell proliferation. This BP may manage DM-induced skin healing difficulties.The results of BP results showed that the ginsenosides were related to the process of positive regulation of protein STK activity, positive regulation of MAP kinase activity, regulation of lipid metabolic process, and positive regulation of reactive oxygen species metabolic process, etc. A lot of evidence has shown that oxidative stress damage leads to lipid peroxidation. Lipid peroxidation causes the rearrangement of peroxyl radicals which can lead to a large of pathological changes in the body. The occurrence of DM often leads to an abnormally high expression of reactive oxygen species (ROS). This can accelerate the onset of cardiovascular disease in DM. Many studies have confirmed that during the development of DM, a shift in glycolytic metabolism occurs, which lead to an overproduction of ROS in monocytes and macrophages. Furthermore, macrophages were produced in large numbers when DM occurs, releasing excessive amounts of pro-inflammatory factors and proteases that lead to inflammation. Because ROS were important mediators in the activation of pro-inflammatory signaling pathways, obesity- and hyperglycemia-induced ROS overproduction may favor the induction of M1-like pro-inflammatory macrophages during the onset and progression of DMThese BP were mainly closely related to the molecular functions of steroid binding, steroid hormone receptor activity, and adrenergic receptor activity. And that mainly occur in the mitochondrial outer membrane, an integral component of the presynaptic membrane, neuronal cell body, etc.Ginsenosides might be involved in a wide range of BP in managing DM and its complications, and these BP are closely related to a variety of molecular functions and cellular components. Overall, our GO results clearly indicated that ginseng could be used to manage DM by modulating BP to ultimately manage it.38. The HIF-1 pathway was a well-known regulator of cellular glucose47. When diabetics were exposed to a hypoxic environment, HIF was activated and this led to the release of large amounts of inflammatory factors from damaged organs49. Diabetic patients often had a condition with elevated pro-inflammatory factors, such as TNF-α. TNF-α mediated inflammation, obesity, and insulin resistance were associated with DM50. It had been reported that the PI3K/AKT signalling pathway is activated when HKC cells were exposed to a high glucose environment. This promoted islet cell proliferation51. Ultimately it was beneficial for DM to manage. Overall, our results suggested that ginsenosides could act on multiple targets and in multiple pathways.A lot of evidence shown that when DM occurs, it tends to cause a series of symptoms such as obesity, insufficient insulin secretion, inflammation, elevated cholesterol, and hardened blood vessels. Our KEGG enrichment analysis showed that insulin, AGE-RAGE, TNF, AMPK, VEGF, adipocyte factor, HIF-1, and PI3K-Akt signaling pathway might be the major signaling pathways53. When the binding energy of the receptor protein and the small molecule fraction is less than 0 kcal/mol, it indicates that the two can bind together, and when the binding energy is less than − 7.0 kcal/mol, it indicates that the two can bind together more stably19. Our results show 224 molecular dockings with binding energies less than − 7.0 kmol/mol, which accounts for 89.2% of all docking results. This showed that the ginsenosides in ginseng could bind well to the nine core genes. Ginsenosides bind to core targets in a variety of ways, mainly hydrogen bonds, as we know that the binding energy of molecular docking is determined by both the receptor protein and the small molecule ligand. However, it is not clear which of the two has a greater impact on binding energy. In this part of the results, we found that receptor proteins had the greatest effect on the binding energy. The conformation and relative molecular mass of small molecules such as S- or R-type receptors had no significant effect on the binding energy. Although further investigation is needed to obtain more reliable conclusions, these results give us an important hint. Combined with the results of molecular docking implied that ginsenosides have some targeting to the receptor protein. To some extent, it can explain that ginsenosides can manage DM with certain targeting properties.Our findings showed that ginsenosides might regulate the above signaling pathways by acting on Caspase 3, MTOR, VEGFA, MAPK1, JUN, TNF-α, STAT3, IL-1β, EGFR and other targets, thereby managing DMFurthermore, molecular dynamics simulation, one of the effective tools used to check the stability of the protein ligand complex, was applied to assess the stability of ginsenosides to receptor proteins in the binding pocket in 100 ns. Interestingly, the molecular dynamics simulation results were in agreement with the molecular docking results and suggest the favorable stability along with proper interaction during the time, for both bioactive ingredients with the receptor proteins. The MM-PBSA analysis further clarified the major amino acid sites of different targets and their contribution values. The composition of free energy of ginsenosides and targets during the binding process was also defined clearly. These results further provide that ginsenosides have activity in the management of diabetes mellitus.54. The predicted amount of saponins accounted for about one fourth of the total. The remaining unpredicted ginsenosides may also have anti-diabetic effects. The reason for this could be the low OB and DL of other ginsenosides or their unclear target. However, compared with the existing reports, our predicted ginsenosides are relatively comprehensive and the mechanism of action is relatively systematic. Meanwhile, ginseng polysaccharide components have been shown to manage DM, but due to some limitations, these components could not be analyzed by network pharmacology55. This type of active ingredient still needs further research. Overall, the application of network pharmacology allowed the mechanisms of action of traditional Chinese medicine in the treatment of disease to be explored at the molecular level. It was more systematic to provide valid evidence and basis for the management of DM by ginseng. However, there were shortcomings, such as lesser reported herbs not being searchable, less comprehensive targets for some ingredients, and less software available to correspond to them.So far, more than 100 ginsenosides have been identified. Our prediction results indicated that 28 saponins had an anti-diabetic effectIn conclusion, our findings clearly indicated that the managed results of 28 ginsenosides in ginseng on DM were through a multi-component, multi-target, multi-pathway overall regulation. The process involved 99 relevant targets, 2238 GO terms and 169 signaling pathways. This result was consistent with traditional chinese medicine theory. Among the 28 ginsenosides, PPT, Rh1, Rh2, Rh4, Ro, Rg1, and PPD interacted with more targets, BP, and signaling pathways. We speculated that these seven saponins might play a significant role in managing DM. A comparison with the literature, molecular docking, and molecular dynamics simulation showed the authenticity and reliability of our results.Supplementary Figure S1.Supplementary Figure S2.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.Supplementary Table S6.Supplementary Table S7.Supplementary Table S8.Supplementary Table S9.


Immune thrombocytopenic (ITP) is an autoimmune bleeding disease with genetic susceptibility. Twenty newly diagnosed active primary ITP patients who had not been treated with glucocorticosteroids, immune globulin or immunosuppressants prior to sampling were enrolled in this study. Bone marrow blood mononuclear cells were used for whole exome sequencing to further elucidation the variant genes of ITP.High-molecular-weight genomic DNA was extracted from freshly frozen bone marrow blood mononuclear cells from 20 active ITP patients. Next, the samples were subjected to molecular genetic analysis by whole-exome sequencing, and the results were confirmed by Sanger sequencing. The signaling pathways and cellular processes associated with the mutated genes were identified with gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses.The results showed that there were 3998 missense mutations involving 2269 genes in more than 10 individuals. Unique genetic variants including phosphatase and tensin homolog, insulin receptor, and coagulation factor C homology were the most associated with the pathogenesis of ITP. Functional analysis revealed these mutation genes mainly affect Phosphatidylinositol 3 kinase/serine/threonine kinase B signaling pathways  and platelet activation (immune system).Our finding further demonstrates the functional connections between these variant genes and ITP. Although the substantial mechanism and the impact of genetic variation are required further investigation, the application of next generation sequencing in ITP in this paper is a valuable method to reveal the genetic susceptibility. The patients of ITP presented with varying degrees of bleeding tendency even causing acute intracranial hemorrhage and life-threatening. Most ITP cases are sporadic but study have showed that pediatric ITP cases had a positive familial history. Further, it is proposed that the existence of genetic susceptibility to ITP. Another study found that inflammation-related single nucleotide polymorphisms (SNPs) may be genetic risk factors associated with the disease severity and treatment of ITP. These results inspired us to use BMBMCs (bone marrow blood mononuclear cell) from a group of primary acute ITP inpatients for whole exome sequencing (WES) to further elucidation the variant genes of ITP.Immune thrombocytopenia (ITP) is a complex bleeding disease with autoimmune trait. It is characterized by both decreased platelet production and/or increased platelet destruction.,5 In recent years, experimental and clinical evidence has associated perturbations of PI3K/Akt signal transduction pathway with a number of neoplastic and autoimmune diseases, such as lymphomas, chronic and acute lymphocytic leukemias,,8 endometrial cancer, bladder cancer, rheumatoid arthritis, and ITP. Platelet autophagy is regulated through the PI3K/Akt/mTOR signaling pathway by phosphatase and tensin homolog (PTEN) in ITP. Elevated platelet autophagy may prolong the life span of platelets from ITP patients by inhibiting platelet apoptosis and improving platelet viability.Phosphatidylinositol 3 kinase/serine/threonine kinase B (PI3K/Akt) signaling pathway plays a critical role in regulating immune response and the release of inflammatory factor in vivo and in vitro by regulating the activation of downstream signaling molecules.In this study, we identified several genes harboring an excess number of rare damaging mutations in patients with ITP: PTEN, insulin receptor (INSR), and coagulation factor C homology (COCH). Interestingly, these genes are collectively involved in the signal transduction of PI3K/Akt signaling pathway and played an important immunomodulation role in platelet activation. By identifying genetic alterations of ITP patients, our study further enriches the pathology of disease, promotes the potential biomarkers diagnose and therapeutic for ITP. The platelet counts of patients ranged between 1 and 29 × 109/L with a median count of 10 × 109/L , were enrolled in this study between May 2017 and November 2018 at the Department of Hematology, Shandong Provincial Hospital, Jinan, China. The diagnosis for ITP was according to recently published criteria including patient history, complete blood count, physical and peripheral blood smear examination.Twenty active ITP patients’ genomic DNA was pooled from BMBMCs using a QIAamp DNA Blood Mini kit  according to the manufacturer’s instructions. Each genomic DNA was fragmented using the CovarisLE220 (Massachusetts) to about 200 to 250 bp fragment size. After fragmentation, DNA fragments were pair-ended and phosphorylated at the 5′ end and successively adenylated at the 3′ end (following Illumina paired-end protocols), and the libraries ligated to pre-capture adaptor were amplified and indexed via polymerase chain reaction. Whole exons were captured with an AI Whole-Exome Enrichment kit  after the construction of the sequencing libraries.Whole exons were submitted to the massive parallel sequenced by 150 pair-end reads on a HiseqX-Ten sequencer . The program provided with the Illumina Pipeline software package was used to processed the raw data in the fastQ format following image analysis and base calling. Clean reads were mapped uniquely for further analysis by removing the adapters and the low-quality reads . Filtered reads were aligned to the human reference genome sequence  using the BWA Multi-Vision software package (version 0.7.10).https://www.broadinstitute.org/gatk/guide/best-practices). Base quality score recalibration and indel realignment were performed using GATK, with duplicate reads removed by the Picard tools. The sequencing specificity and coverage across each sample were calculated based on the alignments. We applied GATK (v3.3.0) performed SNPs and insertions and deletions discovery and genotyping across all genomic variants. In addition, a strict data analysis quality control system was used throughout the whole pipeline to guarantee the sequencing data quality.In order to ensure accurate variant calling, we applied the recommended best practices for variant analysis in the Genome Analysis Toolkit  was applied to perform: verification that the allele frequencies of the mutations in the HapMap database, dbSNP, 1000 Genomes Project should all be “0” and the allele frequencies of the remaining mutations in ExAC East Asian AF and ESP6500 AF should be < 0.1%; verification that the mutations in the deleterious coding regions, such as nonsense, missense, frameshift, splice-variant and coding indels, were retained; a co-segregation analysis was based on family history using the de novo, autosomal dominant, and autosomal recessive model, and excluding that did not follow the inheritance pattern part; the retained misperception variant should be predicted to be “Damaging” by at least one of the above software packages previously introduced.After high-confidence SNPs and insertions and deletions were identified, the SnpEff variants identification tool . Differences between groups were compared using 1-way ANOVA. A We used whole-exome sequencing to detect the DNA mutation profiles of BMBMCs from ITP patients (n = 20) to identify genomic alterations associated with the pathogenesis of ITP. A total of 3998 missense mutations involving 2269 genes were identified in more than 10 individuals . Next, the potential functions of the mutated genes were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology pathways.Functional analysis revealed that most signal transduction genes were enriched. Significantly associated biological process, cellular component and molecular function involving the mutated genes were obtained from the gene ontology analysis Fig. . In the The results indicated that the 4 genes  were all harbored missense-mutated in each ITP patient . These genetic  alterations in PI3K/Akt signaling pathway might affect the activate of platelet and be associated with the pathogenesis of ITP. In addition, PTEN gene regulate the autophagy by mTOR signaling pathway to mediate the onset of ITP. INSR is also involved in hypoxia-inducible factor 1 (HIF-1) pathway regulation. COCH takes part in the regulation of immune platelet activation.The 4 pathways HIF-1, mTOR, PI3K/Akt signaling pathways  and platelet activation (Immune system) were the most disease-associated in this finding, the details of these pathways were shown in Table In addition, we found Fanconi anemia-associated protein (FAAP20) mutations in DNA samples from 19 patients and mucin 20 (MUC20) mutations in DNA samples from 18 patients. They are also involved in platelet activation and the regulation of the PI3K/Akt signaling pathway, which further supplement the pathogenesis of ITP.Moreover, the mutations in these genes  were also verified by using Sanger sequencing  technology verification. KEGG enrichment analysis showed that these differentially proteins were also closely related to the PI3K/Akt signaling pathway.Several DNA polymorphisms induced by SNPs played an important role in the pathogenesis of ITP. Akt interacts with the cytoplasmic domain of GPIbα and transduces vWF–GPIbα interaction signaling leading to platelet activation. PI3K/Akt signaling may be antagonized by the tumor suppressor PTEN which identified as a frequently mutated gene in many types of tumors particularly endometrium, skin, brain, and prostate.,22 Our previous research has shown that the perturbations of normal autophagy leads to abnormal function of platelet and megakaryocyte, which may be caused by deletion of autophagy-related genes such as autophagy-related genes 7 and abnormal signaling due to overexpression of mTOR. mTOR is a key kinase and negative regulator in the PI3K/Akt/mTOR signaling pathway and can regulate cell proliferation, growth, survival, and angiogenesis under physiological conditions and in the presence of environmental stress.In this study, we found several DNA missense mutations related with PI3K/Akt signaling pathway in BMBMCs from ITP patients, which may indicate the pathway is involved in the pathogenesis of ITP. Particularly, PTEN, INSR and COCH unique genetic variation associated with the pathogenesis of ITP’s. The PI3K pathway is an essential pathway in various cellular processes, which is also one of the most frequently activated signal transduction pathways in human cancer and autoimmune disease. The central role of Akt in the PI3Ks pathway makes it one of the most activated downstream effectors. In vitro experiments, indirubin was observed to restore the expression of programmed cell-death 1 and PTEN on the cluster of differentiation (CD4+) T cells of ITP patients, leading to the subsequent attenuation of the Akt/mTOR pathway and modulating the homeostasis of T cell. Thus, it may be hypothesized that PTEN mutations lead to activation of the PI3K/Akt/mTOR pathway and inhibition of autophagy, and play a role in ITP initiation and progression.PTEN is a key positive regulatory molecule of autophagy that blocks the inhibitory effect of PI3K/PKB on autophagy, thereby activating autophagy and forming autophagosome.,28 Several studies have shown that PI3K/Akt pathway could be induced by insulin and act as an indispensable effector.,30 As a downstream molecule of PI3K/Akt pathway, mTOR activity reduces not only influence autophagy balance by this signaling, but also increases HIF-1α activity and production of reactive oxygen species, leading to the oxidative stress in cells. Treins et al have shown that insulin regulates HIF-1 subunit accumulation and activation through a PI3K/mTOR dependent pathway, resulting in increased vascular endothelial growth factor expression. Vascular endothelial growth factor is a key angiogenic factor involved in a wide variety of biological processes including embryonic development, tumor progression and metastasis, and regulated by platelet-derived growth factor, insulin, insulin-like growth factor-I, tumor necrosis factor.,34 Functional analysis revealed that INSR mutation in ITP patients involved in the PI3K/Akt signaling pathway and HIF-1 signaling pathway in this study. Although the depth mechanism of INSR mutation in ITP patients is still uncover, the exon mutations of INSR and PTEN may be involved in the PI3K/Akt signaling pathway, further affecting the expression of downstream molecules and eventually participating in the pathogenesis of ITP.INSR is the central mediator in the insulin response upstream of PI3K that induced tyrosine phosphroylation of INSR substrate and followed by transduction of downstream enzymes of PI3K. COCH encodes cochlin, which contains a short-predicted signal peptide, an N-terminal factor C homology domain and 2 von Willebrand factor A-like domains (vWFA1 and vWFA2).,36 vWFA domain is known for shear-induced self-aggregation and adherence to macrophages, platelets or leukocytes. PI3K associated with the cytoplasmic domain of GPIbα transduces the vWF-binding signaling, leading to Akt activation.,37 Some DFNA9 (vestibular disorder) patients develop vascular diseases such as cerebral ischemia and acute myocardial infarction, and the vWFA domain has been implicated in increased shear-induced platelet aggregation. However, the function of COCH gene in ITP pathogenesis remains to be elucidated fully. The MAMDC4 protein is associated with the unique endocytotic mechanism observed in the intestine of mammals, which may be related to the autophagy activities mediated by the PI3K/Akt signaling pathway. In addition, 20 kDa FAAP20, and MUC20 also participate in the PI3K/Akt pathway and platelet activation in most ITP samples. Further improved the missense mutation genes and related function pathway in ITP.In addition, the function clustering analysis shown that COCH participate in the platelet activation. The COCH gene is the first gene identified to cause vestibular dysfunction. reported that enhanced autophagy-related protein and autophagy flux in PI3K/Akt/mTOR signaling pathway, inhibiting apoptosis and improving platelet viability, thereby alleviating platelet destruction and prolonging the life span of platelets from ITP patients. Furthermore, microRNAs acts through targeting insulin-like growth factor 2 mRNA-binding protein 1, and the subsequent downregulation of insulin-like growth factor 2 causes inhibition of the PI3K/Akt pathway, which is involved in the process of mesenchymal stem cells deficiency in ITP. We reported previously that abnormal expression of multiple proteins in PI3K/Akt pathway in patient groups compared with control groups using protein profiles technology. In support of this finding, this study confirmed some exon mutations  in PI3K/Akt pathway at the gene level in ITP bone marrow samples, which further verified the important role of this signaling pathway in ITP pathogenesis.Wang et al The pathogenesis may that ITP and SLE share a common genetic predisposition. ITP and SLE are based on aberrations in adaptive or innate immunity. Though lots of signaling pathways implicated in systemic autoimmunity, PI3K/AKT pathway occupies a central position in the regulation of multiple pathogenic cascades that can lead to the development of autoimmunity. ITP remains a diagnosis of exclusion, patients with ITP are at an increased risk for SLE. ITP may occur in the absence of an evident predisposing etiology (primary ITP) or as a sequela of a growing list of associated conditions (secondary ITP). Beşliu et al results demonstrated that both expression and phosphorylation levels of Akt are more increased in SLE than in healthy donors CD4+ T cells suggesting an up-regulation of PI3K and mTOR activities. Mice with constitutive activation of the PI3K pathway developed SLE-like disease due to the inactivation of PTEN. These mice were treated with selective PI3K inhibitors improved renal function and lived longer than vehicle-treated controls. In vitro experiments, indirubin was observed to restore the expression of PTEN on the CD4+ T cells of ITP patients, leading to the subsequent attenuation of the PI3K/AKT pathway and modulating the homeostasis of T cell. It may be hypothesized that PI3K/Akt/mTOR pathway play a role in ITP and SLE initiation and progression. This axis attractive as a therapeutic target in ITP and SLE.Recent cohort study reported that ITP had a 26 times higher risk for the development of systemic lupus erythematosus (SLE) than the control population.However, little is known about the concrete transcription process or protein expression pathogenesis of mutation genes leading to thrombocytopenia. We speculate whether the genetic mutation of patients with ITP is related to genetic susceptibility, which will also be a new area of exploration of the pathogenesis of ITP. Because of the rarity of familial ITP families available for study and the heterogeneity of sporadic ITP, family linkage analyses or genome-wide association studies are limited. we fail to study whether the genetic mutation of patients with ITP is related to genetic susceptibility. Moreover, many studies have shown that the PI3K-Akt signaling pathway is involved in the regulation of apoptosis in ITP, but the function of mutation gene in ITP pathogenesis remains to be elucidated fully and little is known about the concrete transcription process or protein expression pathogenesis of mutation genes leading to thrombocytopenia. In the follow-up experiments, we want to conduct at the RNA and protein levels to observe the differential condition of these genetic mutations from both the level of transcription and proteins.In conclusion, our findings improved the understanding of the PI3K/Akt signaling pathway and, more significantly, suggest targeted therapeutic strategies and research direction for ITP caused by related genes mutation or other pathogenic factors. Future work is needed to solve the mystery that how does the transcription and translation mechanisms of key mutation genes or molecules in this pathway affect the occurrence and development in this disease.Data curation: Jing-Shu Ruan, Rui-Jie Sun, Jin-Ping Wang, Xiao-Hui Sui, Hui-Ting Qu, Dai Yuan.Funding acquisition: Ningning Shan.Methodology: Jing-Shu Ruan, Rui-Jie Sun.Writing – original draft: Ningning Shan.Writing – review & editing: Ningning Shan.


Prostate cancer (PCa) is one of the common malignant tumors of the urological system, and metastasis often occurs in advanced stages. Chemotherapy is an effective treatment for advanced PCa but has limitations in terms of efficacy, side effects, multidrug resistance, and high treatment costs. Therefore, new treatment modalities for PCa need to be explored and improved. Pueraria lobata (PL). Next, Cytoscape software was used to draw the interactive network diagram of “drug–active component–target pathway.” Based on the STRING database, the protein–protein interaction network was constructed. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were applied for the genes. Molecular docking was used to visualize the drug–target interaction via AutoDock Vina and PyMOL. Finally, prognosis-related genes were found by survival analysis, and Protein Atlas was used for validation.  R language and GEO database were used to obtain differentially expressed genes for PCa single-cell sequencing. TCMSP, STITCH, SwissTargetPrediction, and PubChem databases were used to obtain the active ingredients and targets of AR, MIF, HSP90B1, and MAOA genes were highly expressed, and molecular docking analysis showed that representative components had a strong affinity with receptor proteins. Survival analysis found that APOE, CA2, IGFBP3, MIF, F10, and NR3C1 could predict progression-free survival (PFS), and some of them could be validated in PCa.  Four active components and 31 target genes were obtained through the regulatory network of PL. Functional enrichment analysis showed that PL played a pharmacological role in the treatment of PCa by regulating the metabolic processes of reactive oxygen species, response to steroid hormones, and oxidative stress as well as IL-17 signaling pathway, PCa, and estrogen signaling pathway. Single-cell data showed that AR, MIF, HSP90B1, and MAOA to regulate several biological processes and related signaling pathways to interfere with the occurrence and development of PCa. APOE, CA2, IGFBP3, MIF, F10, and NR3C1 were also important as target genes in predicting PFS. In this paper, a drug–active ingredient–target pathway network of PL at the single-cell level of PCa was constructed, and the findings revealed that it acted on genes such as  Prostate cancer (PCa) is one of the common malignant tumors of the urinary system, and nearly 1.3 million new cases of PCa and 359,000 related deaths were estimated worldwide in 2018, ranking second and fifth in incidence and mortality rates of cancer in males, respectively [Pueraria lobata (PL) is one of the TCM. Liu et al. showed that extracts of PL could induce apoptosis in PCa cells by upregulating the expression of RASD1 and Bax [AKT, and CDK4 to induce cell cycle arrest in the G1 phase [Traditional Chinese Medicine (TCM) is a treasure house of potential drugs. Studies have shown that TCM has its unique advantages on the complex pathogenic mechanism of cancer. Therefore, the study of the mechanism of TCM may contribute to the development of a new combination of Chinese and Western medicine therapy . Network and Bax . In addiG1 phase . StudiesG1 phase , 9. HoweSingle-cell sequencing technology, which sequences the whole genome, transcriptome, and epigenome of individual cells, is significant in studying the differences and evolutionary relationships of the cell, revealing the complex heterogeneous mechanisms involved in disease onset and progression, and improving disease diagnosis, prognosis prediction, and monitoring of drug treatment effects , 11. Thihttps://www.ncbi.nlm.nih.gov/geo/) for inclusion in this paper. R language was used to define cell subsets according to marker genes, and cancer cell subsets were defined by copy number variation (CNV). Differentially expressed genes (DEGs) were obtained by FindAllMarkers function. DEG screening conditions were as follows: differential expression change fold change (FC) of mRNA, ∣logFC | >1, and adj. P < 0.05.Thirteen cases of PCa single-cell RNA sequencing data in GSE141445 were obtained from the GEO database (https://old.tcmsp-e.com/tcmsp.php). The screening conditions of active ingredients were as follows: oral bioavailability > 0.30 and drug‐likeness > 0.18. STITCH (http://stitch.embl.de/), SwissTargetPrediction (http://www.swisstargetprediction.ch/), and PubChem databases (https://pubchem.ncbi.nlm.nih.gov/) were used for complete PL composition target. The UniProt database (https://www.uniprot.org/) was used to transform IDs, and the final drug target was obtained after merging and deleting duplicates.The active ingredients and corresponding targets of PL were obtained using the TCMSP database  pathway was further obtained through the target gene. On this basis, the drug component–target gene pathway network was constructed and visualized through Cytoscape. Topological parameters of the network are obtained through “CytoNCA” plugin of the Cytoscape software.https://string-db.org/) for target genes, medium confidence > 0.4, and the results were visualized by Cytoscape.Gene Ontology (GO) and KEGG analyses were performed using clusterProfile, enrichplot, and ggplot packages in R language with PvalueCutoff = 0.05 and qvalueCutoff = 0.05 as the screening conditions. The results were presented as bubble plots. Protein interactions were analyzed by the STRING database (https://pubchem.ncbi.nlm.nih.gov/). The PDB files of the molecular structures of the target proteins were obtained using the RCSB PDB database (https://www.rcsb.org/). PyMOL and AutoDock Tools software were used to dehydrate, hydrogenate, and delete the original ligand of the target protein, save it as a pdbqt file for the receptor, and save the mol2 file of PL active ingredient as a pdbqt file for the ligand. The center of the binding pocket is centered on the ligand, which contains the smallest possible area for the ligand to bind, and was obtained by analysis with AutoDock Tools software. Molecular docking was achieved by the AutoDock Vina software, using the Vina force field for optimisation and binding energy calculations. Docking parameters are as follows: exhaustiveness is 8, and num_modes is 9. Root mean squared deviation (RMSD) analysis was performed on the docking results, and interaction pattern analysis was performed using PyMOL.The coordinate mapping diagrams of target gene expression at the level of cell clusters were obtained by the FeaturePlot function of R language. The mol2 files of the active ingredients of PL were obtained using the PubChem database . Progression-free survival (PFS) was analyzed by the univariate Cox proportional risk analysis and Kaplan–Meier analysis. Gene expression was analyzed by Student's t-test. From the online database Protein Atlas (https://www.proteinatlas.org/), the immunohistochemical microscopic images of genes were obtained and exported for visualization using image processing software Adobe Illustrator. The specific flow is shown in R language was used to obtain the data of 494 patients with PCa with complete clinical and transcriptome data from the cBioPortal database . The Kaplan–Meier survival analysis obtained APOE, CA2, F10, IGFBP3, MAOB, MIF, NR3C1, PLA2G2A, and SNCA differed in PFS (P < 0.05), but ABCG2 and PTGS2 showed no statistically significant difference in PFS (P > 0.05)   was the main driver of tumor cell genesis and development. It was activated and translocated to the nucleus to bind to androgen response elements in DNA and recruit regulatory factors or transcription factors that mediated target gene transcription to regulate biological processes such as cell proliferation, apoptosis, migration, invasion, and differentiation [MIF can promote PCa growth and metastasis by upregulating MAPK and CXCR7 expressions, thus activating the PI3K-AKT signal transduction pathway [AR, MIF, HSP90B1, and MAOA, as the targets of PL, exerted antitumor effects by regulating related pathways, suggesting that PL can interfere with tumorigenesis and progression through multicomponent, multitarget, and multipathway mechanisms. In addition, molecular docking experiments showed that the active monomers of PL had a spatial conformational docking effect with the proteins of the four corresponding target genes with good docking scores/binding energy, suggesting that PL may affect the protein activity and the regulation of its downstream pathways by directly binding to the active centers of the proteins. APOE, CA2, IGFBP3, MIF, F10, and NR3C1 were obtained for the prediction of PFS in PCa by the univariate Cox proportional risk analysis and Kaplan–Meier survival analysis of target genes. Protein Atlas verified that APOE, MIF, and NR3C1 were expressed in tumor tissues at different levels. The findings suggested that PL may intervene in the clinical prognosis of PCa by acting on the corresponding targets.Based on marker genes, nine cell populations were obtained, including cancer cells, to explore the potential role of PL in the treatment of PCa at the single-cell level. ntiation –28. Our  pathway . HSP90B1 pathway . In addi pathway , 32. MAO pathway –35. AR, This study may provide clues to the search for potential therapeutic targets and drugs for prostate cancer and refine our understanding of the efficacy of Pueraria lobata and its active ingredients. The limitation of this study is that the components of herbal medicine are complex and diverse in their therapeutic effects, and it is uncertain which combinations are at play, and more evidence is needed.AR, MIF, HSP90B1, and MAOA genes regulated several biological processes and related signaling pathways to interfere with the occurrence and development of PCa while its action on APOE, CA2, IGFBP3, MIF, F10, and NR3C1 genes was also important in interfering with clinical prognostic regression. The multiple drug–active component–target pathway axis constructed in this paper provided a new direction for exploring the therapeutic and prognostic targets of PCa. The above results were initially validated in molecular docking experiments and Protein Atlas, and the specific related molecular mechanisms need to be investigated further through experiments.In this paper, the drug–active ingredient–target pathway network of Chinese herbal medicine PL was constructed at the single-cell level of PCa by network pharmacology combined with single-cell sequencing technology. The findings revealed that its action on


To explore the mechanism of Shaoyao Gancao decoction (SGD) in treatment of alopecia areata (AA) by network pharmacology and animal experiments.  Based on the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP), the components and targets of SGD were determined. Then, the related targets of AA were retrieved from DrugBank, GeneCards, OMIM, and DisGeNET databases. The intersection of drug targets and disease targets was determined, and the key targets of the protein-protein interaction network were obtained with the String database. Gene Ontology (GO) biological process enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of potential key targets were carried out using the DAVID database using AutoDock for molecular docking verification. Finally, the key pathway was validated by animal experiments.  A total of 102 active components, 212 predicted targets, and 812 AA disease-related targets were obtained. Topological analysis yielded 45 key targets of SGD in the treatment of AA, including IL-6, PTGS2, TNF, VEGFA, CCL2, IL-1B, CXCL8, CASP3, MPO, and IL-10. There were 324 GO entries obtained through GO biological process enrichment analysis, and 20 pathways were obtained through KEGG pathway enrichment analysis, involving the PI3K-Akt signaling pathway, osteoclast differentiation, and Jak-STAT signaling pathway. The molecular docking results showed effective ingredients  have good docking results with targets . The results of animal experiments showed that SGD can effectively upregulate the expression of PI3K and AKT proteins.  This is the first in-depth study on the mechanism of SGD's treatment effect in AA using network pharmacology, and preliminary animal experiments verified that it is closely related to the PI3K/AKT signaling pathway. This finding may provide a new basis for SGD's clinical application in AA. The Chinese Guidelines for the Treatment of Alopecia Areata (2019) [Alopecia areata (AA) is a T-cell-mediated nonscarring alopecia characterized by an autoimmune reaction in the hair follicle. AA is associated with various factors such as genetics, neurological and psychiatric conditions, oxidative stress, and viral infections. Studies have shown that the lifetime prevalence of AA is about 2% globally and 0.27% in China . AA is aTreatise on Febrile and Miscellaneous Diseases. The whole formula consists of two herbs: Shaoyao and Gancao. Shaoyao is sour in taste and cold in property, with the effect of nourishing blood and astringing yin, softening the liver and relieving pain; Gancao is sweet in taste and warm in property, strengthening the spleen and benefiting qi and relieving pain. The combination of the two herbs nourishes the yin with sour and sweet and harmonizes the liver and spleen as well as relieves pain. Modern pharmacological studies [Shaoyao Gancao decoction (SGD) is derived from Zhang Zhongjing's  studies  have sho studies  has conf studies .Network pharmacology is an emerging systems biology research method that combines modern medicine and bioinformatics. This method uses biological databases to construct and demonstrate the relationships among diseases, targets, and drugs, thus systematically identifying the possible mechanisms of action of drugs . The chaIn this study, we investigated active components of SGD through network pharmacology, constructed a multilevel bioinformatics network of SGD for AA treatment, and successfully predicted the potential key targets and pathways of SGD for the treatment of AA, which strongly reflected the multicomponent, multitarget, and multipathway action characteristics of SGD. At the same time, molecular docking and animal experiments were used to validate the pharmacological mechanism, providing a theoretical basis for further research.The SGD granules formula consisted of 45 g of Shaoyao and 15 g of Gancao, prepared by the Pharmacy Department of the China-Japan Friendship Hospital, 10 g per bag (containing 30 g of raw medicine), in which the content of paeoniflorin was not less than 900 mg and that of glycyrrhetinic acid was not less than 75 mg.We obtained 30 female C57BL/6J mice (22 ± 3 g), 6∼8 weeks old, from Beijing Vital River Laboratory Animal Technology Co., Ltd. . All the animal experiments strictly followed the relevant standards and requirements of the animal experimental platform of the Institute of Clinical Medicine, China-Japan Friendship Hospital.The 30 mice were randomly and equally divided into three groups: the control group, the model group, and the SGD group. The latter two groups were treated with topical imiquimod cream combined with 21-day chronic unpredictable mild stress to establish a mouse model of AA, after which the SGD group was given SGD (9.76 g/kg) by gavage for 21 consecutive days.Reagents included imiquimod cream , phosphoinositide 3-kinase (PI3K) P85 alpha monoclonal antibody , AKT , goat-anti-mouse IgG-HRP , and RIPA-containing lysis buffer .We used a cryogenic frozen centrifuge , Bio-Rad Mini PROTEAN Tetra, Bio-Rad Mini Trans-Blot, Bio-Rad PowerPac Basic.https://lsp.nwu.edu.cn/tcmsp.php). The screening criteria for the main chemical components were set as oral bioavailability (OB) ≥ 30% and drug-like index (DL) ≥ 0.18. The Universal Protein Database  was then used to standardize the target information.The chemical components of Shaoyao and Gancao were researched separately in the traditional Chinese medicine systems pharmacology database and analysis platform , GeneCards database , Online Mendelian Inheritance in Man (OMIM) (https://omim.org/), and DisGeNET database (https://www.disgenet.org/). The disease targets obtained from these databases were pooled and duplicate targets were removed to obtain AA disease-related targets.The keywords “alopecia areata” were used to screen for AA-related gene targets in the DrugBank database (https://bioinfogp.cnb.csic.es/tools/venny/). Cytoscape 3.8.0 software was then used to construct the network of interactions between overlapping genes, bioactive compounds, and herbs, called the H-C-T network.The potential target genes of SGD in the treatment of AA were the intersecting parts of the target genes corresponding to the action of active components of SGD and the target genes related to AA. The overlapping partial genes were visualized by Venn diagrams (https://string-db.org/), setting the species genus as “Homo sapiens,” the minimum interaction score as “medium confidence (0.400),” and hiding the free sites. The protein-protein interaction (PPI) network of SGD for AA was obtained using Cytoscape 3.8.0 software for network topology analysis.The potential targets of SGD for AA treatment were imported into the STRING 11.0 database (https://david.ncifcrf.gov/).Gene Ontology (GO) biological process enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the core targets for the action of SGD using the David database (https://www.rcsb.org/). Subsequently, AutoDock Vina was used to verify molecular docking between core components and core targets. The molecular docking results were optimized and mapped with the help of PyMOL software.We screened the highest five-node degree active components in the H-C-T network of SGD and the highest five-node degree core proteins in the PPI network. Then, the potential interaction of ligands in the complex molecular network was studied by molecular docking simulation. The 3D structure Protein Data Bank (PDB) format files of the target proteins were retrieved and downloaded from the database  and PI3K (60225-1-lg) were purchased from Proteintech . After washing three times with Tris-buffered saline with Tween 20 (TBST), the membranes were incubated with goat-anti-mouse secondary antibody , washed as previously described, and then detected by western blotting. The grayscale intensity of the protein bands was analyzed and quantified using Image Lab.t-test was used for normally distributed data, the rank sum  test was used for nonnormally distributed data, and the chi-square test was used for count data. Differences were considered statistically significant when P < 0.05.SPSS 21.0 was used for the analysis. Normally distributed data were expressed as mean ± standard deviation, nonnormal distributions were expressed as median and quartiles, and count data were expressed as percentages. When comparing the sample means of two groups, the independent samples A total of 105 active ingredients were screened by searching the TCMSP database, including 13 Shaoyao roots and 92 Gancao roots, among which three compounds  were repeated. After deduplication, 102 active ingredients were obtained. The results are shown in Among the abovementioned 102 related active compounds of SGD, nine compounds  failed to match relevant targets. The remaining 93 components were matched to 1447 human gene targets in the TCMSP database, and 212 components were finally obtained after deduplication. By importing active compounds and corresponding targets into Cytoscape 3.8.0 software, the network of interactions between herbs, compounds, and target genes was constructed, with 307 nodes and 1543 edges, as shown in The keywords “alopecia areata” were searched in the DrugBank, GeneCards, OMIM, and DisGeNET databases. After deduplication, 812 AA disease target genes were obtained. The resulting Venn diagram of the intersection of SGD main active compounds' target genes and AA target genes is shown in The PPI network  was consThrough GO biological process enrichment analysis, 45 core targets yielded a total of 324 GO entries using David, including 270 biological process (BP) entries, 35 molecular function (MF) entries, and 19 cell composition (CC) entries, as shown in P < 0.05, as shown in Through the KEGG pathway enrichment analysis, 45 potential targets were mapped to a total of 60 signaling pathways using David, 20 of which were identified as Quercetin, kaempferol, 7-methoxy-2-methyl isoflavone, beta-sitosterol, and formononetin were molecularly docked with IL-6, PTGS2, TNF, VEGFA, and CCL2. The lower the binding energy, the better the docking results. The results showed that the binding energy of each component to the target protein was less than −5 kcal/mol, among which quercetin had the best combination with PTGS2 , as shown in P=0.021). The AKT protein level in mice in the SGD group was 1.92 ± 0.45, and the result was higher compared with the model group (P=0.021). The AKT protein levels of mice in the control and SGD groups were similar, with no significant statistical difference (P=0.773).The AKT protein level of mice in the model group was 0.99 ± 0.22, and the result was lower compared with that of the control group of 1.89 ± 0.39 (P=0.029). The PI3K protein level of mice in the SGD group was 1.98 ± 0.57, which was higher than that of the model group (P=0.021). The PI3K protein levels of mice in the control and SGD groups were similar, and no clear statistical difference was observed (P=0.773). The results are shown in The PI3K protein level of mice in the model group was 1.24 ± 0.11, which was lower than that of mice in the control group, 1.90 ± 0.38 , and increased anti-inflammatory cytokines  [β-sitosterol has biological activities such as anti-inflammatory, antioxidant, antitumor, antibacterial, antidepressant, and antihair loss [β-sitosterol can inhibit the activation of NLRP3, the production of CAS1, and the activation of the MAPK signaling pathway, leading to a significant decrease in cellular TNF-α, IL-1β, IL-6, and IL-8. In terms of immunomodulation, Alappatl and Valerio [β-sitosterol and vitamin D3 can enhance the immune effects of macrophages. As an antidepressant, β-sitosterol can reduce the symptoms of depression by increasing norepinephrine, 5-serotonin, and its metabolite 5-hydroxyindole acetic acid in the brains of mice [According to the H-C-T network, the active components of SGD with high values include quercetin, kaempferol, 7-methoxy-2-methyl isoflavone, beta-sitosterol, and formononetin. Quercetin has anti-inflammatory, antiviral, antitumor, hypoglycemic, and immunomodulatory effects . It was and VEGF , reducedtor-β 1) , thus extor-β 1) . Kaempfetor-β 1) . In additor-β 1) . β-sitosair loss . Liao etair loss  found th Valerio  showed t of mice . Formono of mice . For pae of mice . All theα is a potent inhibitor of proliferation. TNF-α, IL-1α, and IL-1β were found to inhibit the growth of cultured hair follicles in vitro, leading to the pathogenesis of AA through activation of T cells [γ and TNF-α, leading abnormal expression of MHC class I molecules in hair follicles during the anagen phase and disrupting HF IP, thus leading to the pathogenesis of AA. IL-10 is a Th2 cytokine that inhibits the synthesis of cytokines such as IFN-γ, IL-2, and TNF-α [γ, and promote the re-establishment of HF IP, which facilitates hair regrowth [Based on the results of the PPI network diagram analysis, it is evident that targets such as IL-6, PTGS2, TNF, VEGFA, CCL2, IL-1B, CXCL8, CASP3, MPO, and IL-10 are potential key targets of SGD in the treatment of AA. Proinflammatory factors such as IL-6 and IL-1B are essential for the differentiation of Th1 and Th17, which in turn affect the development of AA by influencing Th1 and Th17 cells involved in the development of AA . Prostag T cells . Hair fond TNF-α . It was regrowth . CCL2 isregrowth , so it mα synthesis, increased IL-4 production, and downregulated the effects of type I cytokines in treating AA by modulating immunity and anti-inflammation [The results of the GO analysis showed that the biological processes involved in AA treatment with SGD were mainly inflammatory response, immune response, oxidation-reduction process, etc. Previous studies showed that SGD had anti-inflammatory and immunomodulatory effects, inhibiting the production of prostaglandin E2 (PGE2), nitric oxide (NO), and interleukin-6 (IL-6) , as wellammation .γ, TNF-α, IL-10, and TGF-β. Studies of AA patients [In the KEGG enrichment analysis of target genes, the PI3K-AKT signaling pathway, osteoclast differentiation, hepatitis B, and Jak-STAT signaling pathways were enriched to a higher degree and were closely related to AA. It was found that many cytokines involved in the pathogenesis of autoimmune and inflammatory diseases transmit intracellular signals through the JAK/STAT signaling pathway; for example, IL-6 can bind to JAK1 to exert biological effects . Meanwhipatients  suggest The molecular docking results showed that the binding energy of the main active components of SGD to the core protein was less than −5 kcal/mol, among which quercetin had the best binding property to PTGS2 , thus verifying the effect of SGD for the treatment of AA at the molecular level.Through animal experiments, we initially verified the possible mechanism of SGD in the treatment of AA. First, unlike previous studies that reported upregulation of antiapoptotic protein p-Akt expression in lymph node cells of AA mice , this stUtilizing network pharmacology's multilevel (drug-component-target-pathway) characteristics, this study determined the features of SGD responsible for drug synergy and the holistic concept of TCM and successfully predicted possible pharmacological mechanisms of SGD for the treatment of AA. Furthermore, the potential key pathway, the PI3K-AKT signaling pathway, was verified by animal experiments, providing a basis for exploring mechanisms of SGD in AA treatment.In this study, network pharmacology findings suggested that the possible mechanism of SGD in AA treatment is through acting on IL-6, PTGS2, TNF, CCL2, IL-1B, IL-10, and other targets, mainly regulating the PI3K-AKT signaling pathway and Jak-STAT signaling pathway, thus exerting anti-inflammatory, immunomodulatory, and antidepressant functions. However, network pharmacology cannot predict the specific regulation of drugs on the targets, and false-positives may occur, so further experiments are needed to verify the results. Our subsequent animal experimental results showed that SGD could effectively upregulate PI3K and AKT protein expression in AA mice, which strongly supports the prediction of network pharmacology.However, network pharmacology is based on predicting the main active components of traditional Chinese medicine, as a result of which we cannot analyze products of traditional Chinese medicine after absorption in the body. In addition, the experiment in this study was validated at the protein level, and subsequent further studies can investigate the changes in factors related to the PI3K/AKT pathway affected by SGD at the molecular level to explore the possible mechanism in greater depth.


The Jiedu Huazhuo Quyu formula (JHQ) shows significant beneficial effects against liver fibrosis caused by Wilson's disease (WD). Hence, this study aimed to clarify the mechanisms of the JHQ treatment in WD-associated liver fibrosis. First, we collected 103 active compounds and 527 related targets of JHQ and 1187 targets related to WD-associated liver fibrosis from multiple databases. Next, 113 overlapping genes (OGEs) were obtained. Then, we built a protein-protein interaction (PPI) network with Cytoscape 3.7.2 software and performed the Gene Ontology (GO) term and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses with GENE DENOVO online sites. Furthermore, module analysis was performed, and the core target genes in the JHQ treatment of WD-associated liver fibrosis were obtained. Pathway and functional enrichment analyses, molecular docking studies, molecular dynamic (MD) simulation, and Western blot (WB) were then performed. The results indicated that 8 key active compounds including quercetin, luteolin, and obacunone in JHQ might affect the 6 core proteins including CXCL8, MAPK1, and AKT1 and 107 related signaling pathways including EGFR tyrosine kinase inhibitor resistance, Kaposi sarcoma-associated herpesvirus infection, and human cytomegalovirus infection signaling pathways to exhibit curative effects on WD-associated liver fibrosis. Mechanistically, JHQ might inhibit liver inflammatory processes and vascular hyperplasia, regulate the cell cycle, and suppress both the activation and proliferation of hepatic stellate cells (HSCs). This study provides novel insights for researchers to systematically explore the mechanism of JHQ in treating WD-associated liver fibrosis. Wilson's disease (WD), also termed hepatolenticular degeneration, is an autosomal recessive inherited fatal disorder with excessive copper accumulation . AlthougTherefore, in this study, we used network pharmacology, molecular docking, and molecular dynamics (MD) simulation to identify the key compounds and core targets of JHQ and further explored the potential mechanisms involved in the treatment of WD-associated liver fibrosis. Furthermore, we validated the results of network pharmacology in in vivo by western blot (WB) experiments.The strategy of this study is available in https://old.tcmsp-e.com/), and active compounds were mainly identified by integrated analysis of the pharmacokinetic properties of oral bioavailability (OB) ≥30% and drug likeness (DL) ≥0.18 [https://www.omicshare.com) [https://www.swisstargetprediction.ch/), DrugBank (https://go.drugbank.com/drugs), and PubChem database (https://pubchem.ncbi.nlm.nih.gov/) [https://www.uniprot.org/) [Compounds in the herbs in JHQ, including Da Huang, Huanglian, Danshen, E Zhu, Jiang Huang, and Jinqiancao, were searched in the TCMSP database (L) ≥0.18 –19. An uare.com) . The preih.gov/) , 22. Theot.org/) . We usedhttps://www.genecards.org/) and the OMIM database (https://omim.org/) [https://stringdb.org/) with the confidence score set as ≥0.4, and we analyzed the PPI data and constructed the network with Cytoscape 3.7.2 [The target genes related to WD-associated liver fibrosis were retrieved from the GeneCards database (im.org/) , 25. Thepe 3.7.2 , 27.P < 0.05 and counted in descending order.Then, Gene Ontology (GO) functional enrichment analysis included biological process (BP), molecular function (MF), and cellular component (CC) ontologies, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment which were visualized and analyzed by GENE DENOVO. The retrieval results were filtered with a threshold value of The molecular complex detection (MCODE) algorithm was used to develop the hub network to obtain clusters . Genes ihttps://www.rcsb.org). Moreover, 2D structures of compounds were obtained from the TCMSP and PubChem databases. In addition, DPA is believed to be an effective first-line drug in the treatment of WD-associated liver fibrosis [https://www.swissdock.ch/), and the results were visualized with PyMOL and Chimera1.14rc software [Molecular docking simulation was performed to verify the critical components' binding ability to key targets and explore their accurate binding modes. The macromolecular protein target receptors were acquired from the RCSB PDB database , and the CHARMM topology file was generated according to the CGenFF force field. Then, the MD simulation workflow includes four specific processes for minimizing energy, heating, balancing, and production dynamics. First, confine the heavy atoms  of proteins and perform 10,000 steps of energy minimization on the water molecule, then unlock the constraints, and perform 10,000 power minimization steps on the entire system. For energy optimization, the system was slowly heated to 300 K within 100 ps. After the heating was completed, the system was equilibrated for 100 ps under the npt ensemble. Finally, the system was subjected to 50 ns MD simulation under the npt ensemble. Trajectory data were saved every 10 ps. In order to calculate the binding affinity of three protein-ligand complexes, the g_mmpbsa tool was used to calculate the binding free energy of each complex. The specific formula is as follows:The GROMACS package with the force field CHARMM was used for MD simulation. Simultaneously, the compound small-molecule ligands were uploaded to the CGenFF online tool  and centrifuged at 12000 rpm for 10 min. Total protein was separated by 10% SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and was then transferred to nitrocellulose (NC) blotting membranes. After blocking with 5% nonfat milk for 2 h at room temperature, membranes were incubated with rabbit monoclonal anti-CXCL8 , anti-AKT1 , anti-SRC , anti-VEGFA , anti-IL-6 , rat monoclonal anti-MAPK1 , and anti-β-actin  primary antibodies overnight at 4°C and were then incubated with secondary antibodies at room temperature for 2 h, and detailed information is shown in Supplementary LX-2 cells were cultured in high-glucose DMEM medium  and maintained at 37°C in a humidified incubator in an atmosphere of 5% COP < 0.05 was considered statistically significant.Statistical analysis was performed using SPSS 23.0. At least three experiments were performed for each group, and all data are expressed as the mean ± standard deviation values. The mean value of three experiments performed for each group was used. The results of WB analysis were analyzed by one-way analysis of variance, and To construct the compound-putative target network of JHQ, we first analyzed 529 compounds of six herbs in JHQ . Next, wWe analyzed and screened 499 targets and 742 targets of WD-associated liver fibrosis in the GeneCards database and the OMIM database, respectively. Subsequently, 1,187 disease targets were obtained after eliminating the overlap and taking the union of these two sets. Then, we intersected the JHQ and WD-associated liver fibrosis target genes and obtained 113 OGEs with the Venn diagram tool ; we impoGO enrichment analysis revealed 6568 terms, among which 4611 were statistically significant. GO-CC enrichment analysis revealed 484 terms, among which 437 were statistically significant, and top 20 GO-CC terms are shown in Module analysis with the MCODE plugin identified five modules, and we selected the hub module, which had an MCODE score of 26.07 . The hubGO enrichment analysis revealed 1867 terms, among which 1742 terms were statistically significant. GO-CC enrichment analysis revealed 153 terms, among which 60 terms were statistically significant, and top 20 GO-CC terms are shown in To better understand the interactions between JHQ and WD-associated liver fibrosis, we constructed an interaction network of the key herb-compound-gene-pathway interactions. It contains 8 key active components including quercetin, luteolin, obacunone, palmatine, kaempferol, bisdemethoxycurcumin, wenjine, and NSC 122421, 6 core genes including CXCL8, MAPK1, AKT1, SRC, VEGFA, and IL-6 . We propThe MD simulation was performed using the VEGFA-luteolin, IL-6-quercetin, and MAPK1-palmatin complexes docked conformation to analyze the complex conformational dynamics. The stability of the system is checked by calculating the standard deviation of the simulated trajectory (RMSD) over a period of 50 ns. During the initial phase, the RMSD of protein-ligand complexes varies widely. At the end of simulation, the conformations of the three complexes reached equilibrium, and the structures were stable without obvious conformational fluctuations, as detailed in As shown in As a new and comprehensive approach, network pharmacology, based on traditional pharmacology, bioinformatics, chemoinformatics, and network biology, can help researchers analyze the role of traditional Chinese medicine (TCM) in human biological networks from a global perspective. Because of the advantages of network pharmacology research strategies, new and innovative methods for the development of TCM have been opened.In this study, using network pharmacological analysis, we found that JHQ contains a variety of active components that can inhibit WD-associated liver fibrosis development through multiple targets, biological processes, and related pathways. First, we searched databases for information on herbs and their associated targets and identified 103 active components  and 527 potential targets. These compounds were found to have enhanced liver regeneration, of epithelial-mesenchymal transition (EMT) inhibition, and antioxidative stress abilities. Quercetin, for example, ameliorates liver fibrosis by elevating the expression of the antioxidant transcription factor Nrf2 and alleviating the oxidative stress caused by excess copper in vivo , 36. Lutα) signaling pathway to stimulate proliferation and activation of HSCs [β) signaling cascades, thus accelerating the development of liver fibrosis [Immediately after this search, we obtained 1187 WD-associated liver fibrosis targets from the GeneCards and OMIM disease databases and identified 113 OGEs corresponding to active ingredients and disease targets. Then, module analysis of the 113 key OGEs was performed using MCODE and the CytoNCA plugin to obtain 6 core genes , which might play important roles in the effects of JQH against WD-associated liver fibrosis. It has been reported that IL-6 and CXCL8 levels are related to the degree of liver fibrosis and can promote the process of liver fibrosis by inducing HSCs activation and regulating apoptosis –43. It h of HSCs –49. Othefibrosis –53. VEGFfibrosis –57. The Next, by KEGG pathway enrichment analysis, we found that the 6 core target genes were mainly enriched in the EGFR tyrosine kinase inhibitor (EGFR-TKI) resistance, Kaposi sarcoma-associated herpesvirus (KSHV) infection, and human cytomegalovirus (HCMV) infection pathways. EGFR-TKIs can promote hepatocyte proliferation, cell cycle progression in hepatocytes, and angiogenesis through a variety of mechanisms involved in induction of HSCs proliferation and activation by core targets to promote the process of liver fibrosis , 58, 59.Finally, to further validate the reliability of the target prediction results, molecular docking analysis and MD simulation were performed on the selected key compounds and core genes, and the results predicted that complexes had good binding affinity, indicating their intrinsically high biological activity. WB also showed that JHQ had a significant suppressive effect on the expression of the 6 core target proteins, suggesting that one of the possible mechanisms by which JHQ exerts its beneficial effects against WD-associated liver fibrosis is via the action of these active compounds on the 6 core target proteins.In summary, the present study shows that the molecular mechanism of JHQ in the treatment of WD-association liver fibrosis was mainly achieved by active ingredients, such as quercetin, kaempferol, and triptolide. JHQ reduced the expression of CXCL8, MAPK1, AKT1, SRC, VEGFA, and IL-6 via EGFR tyrosine kinase inhibitor resistance, Kaposi sarcoma-associated herpesvirus infection, and human cytomegalovirus infection signaling pathways, thereby restricting the liver inflammation, neoangiogenesis, and activated HSCs in liver fibrosis. These findings could provide further insights into the mechanisms of CHM-based therapy in WD-associated liver fibrosis and assist in screening potential therapeutic targets in the future.


To investigate the pharmacological mechanism of Zhizhu pill (ZZP) against gastroesophageal reflux disease (GERD), network pharmacology in combination with molecular docking was applied in this study.  Active compounds of ZZP and target genes related to GERD were identified through public databases. Subsequently, the obtained data were used as a basis for further network pharmacological analysis to explore the potential key active compounds, core targets, and biological processes involved in ZZP against GERD. Finally, the results predicted by network pharmacology were validated by molecular docking.  Twenty active components of ZZP were identified to act on 59 targets related to GERD. Enrichment analysis revealed that multiple biological processes including response to oxygen levels, response to oxidative stress, and response to reactive oxygen species were involved in the GERD ZZP treatment with ZZP. ZZP had an impact on the prognosis of GERD mainly through the HIF-1 signaling pathway, PI3K-Akt signaling pathway, and pathways in cancer. Further analysis identified the key components and core targets of ZZP against GERD, of which nobiletin, didymin, luteolin, and naringenin were key components, and PPARG, MMP9, JUN, TP53, PTGS2, EGFR, MAPK3, CASP3, AKT1, and VEGFA were the core targets. Molecular docking verified the stable bonds formed between the key components and the core targets.  The results of this study predict that the therapeutic effects of ZZP in GERD are mediated at least in part via PPARG, MMP9, JUN, TP53, PTGS2, EGFR, MAPK3, CASP3, AKT1, and VEGFA. These results may be useful in providing an experimental basis and new ideas for further research on ZZP in GERD. Gastroesophageal reflux disease (GERD) is a gastrointestinal motility disorder in which gastric contents reflux into the esophagus or oral cavity leading to symptoms or complications, with heartburn and regurgitation as typical symptoms , 2. In UAtractylodes macrocephala Koidz)), is originated from Shang Han Zabing Lun  and has been widely used for the treatment of functional gastrointestinal diseases. Evidence confirmed that the ZZP was beneficial for GERD treatment [Chinese herbal medicine (CHM) has been widely used for symptom management of GERD in China . Zhizhu reatment . NeverthDue to the “multicomponent” and “multitarget” characteristics of CHM formula, it is difficult for traditional experimental methods to reveal the comodular association mechanism of CHM-component-gene and disease. Network pharmacology is an innovative approach to elucidate the synergy and potential mechanisms of component-target and target-disease networks –14, and Atractylodes macrocephala Koidz. Oral bioavailability ≥30% and drug-like ≥0.18 were identified as criteria for screening drug compounds [The Traditional Chinese Medicine Systems Pharmacology (TCMSP)  was applompounds . UniProthttps://www.drugbank.ca/), TTD (https://bidd.nus.edu.sg/group/cjttd/), PharmGKB (https://www.pharmgkb.org/), and GeneCards (https://www.genecards.org/) were applied to identify target genes related to GERD. A correlation score ≥10 was established as a screening criterion for GeneCards target genes [DrugBank  with interaction score as 0.400 [PPI analysis was performed through STRING (as 0.400 . The Cytas 0.400  was applhttps://pubchem.ncbi.nlm.nih.gov/) [https://www.rcsb.org/) [Based on the core targets of ZZP against GERD that have been identified by cytoHubba, molecular docking was performed with Discovery Studio 2019 to validate the compound-target correlation. The structures of key compounds and core macromolecular protein target receptors related to GERD were downloaded from PubChem (ih.gov/)  and RCSBsb.org/) , respectAtractylodes macrocephala Koidz were identified, respectively. According to the oral bioavailability ≥30% and drug-like ≥0.18, 21 active compounds of ZZP were identified finally, of which 17 belonged to Aurantii Fructus Immaturus and 4 to Atractylodes macrocephala Koidz. Furthermore, 117 targets corresponding to these 21 active compounds were identified. Details of the 21 active compounds and 117 targets are presented in Supplement A.Through TCMSP, 65 compounds of Aurantii Fructus Immaturus and 55 compounds of By searching databases, 1613 target genes related to GERD were obtained, of which 1283 were downloaded from the GeneCards, 200 from PharmGKB, 118 from DrugBank, and 12 from TTD. After removing the duplicates, 1476 target genes related to GERD were finally obtained. 1476 target genes are detailed in Supplement B.The Venn diagram  identifiAccording to the results of enrichment analysis, the biological processes were mainly enriched in response to oxygen levels, response to oxidative stress, response to reactive oxygen species, response to nutrient levels, cellular response to chemical stress, aging, muscle cell proliferation, response to drug, reproductive structure development, and response to toxic substance. With KEGG analysis, pathways related to cancer, endocrine resistance, lipid and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, human cytomegalovirus infection, HIF-1 signaling pathway, and PI3K-Akt signaling pathway were most significantly enriched. More details of the enrichment results are presented in With the PPI network constructed by STRING, 58 nodes and 1250 interactions were observed. The first screening by cytoHubba yielded a network of 25 nodes and 542 interactions, and the second screening yielded a dense region network with 10 nodes and 90 interactions. PPARG, MMP9, JUN, TP53, PTGS2, EGFR, MAPK3, CASP3, AKT1, and VEGFA were identified as core targets. Details are presented in Molecular docking was performed based on the ten core targets and the four corresponding key active compounds. According to the results of molecular docking, all LibDock scores over “80” , suggestGERD is among the most frequent reasons for outpatient gastroenterology consultation . The firThrough public databases, 20 active compounds of ZZP were found to act on 59 target genes related to GERD, which further validated the “multicomponent” and “multitarget” characteristics of ZZP. Based on these findings, it was reasonable to adopt a network pharmacology approach to elucidate the component-target and target-disease network synergies and intrinsic mechanism of ZZP for GERD treatment. To the best of our knowledge, this was the first study to use a network pharmacology approach to reveal the molecular mechanism of ZZP for GERD treatment.With network pharmacology, multiple biological processes including response to oxygen levels, response to reactive oxygen species, and response to oxidative stress were found to be involved in the treatment of GERD with ZZP. These findings suggested that ZZP might play a role in the treatment of GERD mainly by regulating oxidative stress. A new view of the pathogenesis of GERD was recently reported, describing it as an inflammatory disease characterized by increased production of cytokines, chemokines, and reactive oxygen species (ROS), as well as disruption of the endogenous antioxidant defense system . The forβ, and IL-6 and increasing the levels of IL-4 and IL-10 [Based on the findings of network pharmacology, further analysis was conducted to identify the key active compounds and core targets. Nobiletin, didymin, luteolin, and naringenin were identified as the key active compounds of ZZP against GERD. It has been found that didymin is beneficial to prevent the generation of ROS as well as lipid peroxidation products and the release of inflammatory cytokines and chemokines, thereby protecting the digestive tract , 29. Thend IL-10 . For narnd IL-10 . Recent nd IL-10 . Thus, tPPARG, MMP9, JUN, TP53, PTGS2, EGFR, MAPK3, CASP3, AKT1, and VEGFA were predicted as the core targets of ZZP against GERD. For PPARG, it belongs to oxidative stress genes and is involved in the regulation of ROS production and inflammatory responses . MMP9 isWith molecular docking analysis, all LibDock score of key components to core targets were over “80,” indicating that ZZP can effectively bind to specific proteins in GERD targets. These binding results further validate that the predicted results of network pharmacology are credible.Limitations must be acknowledged. First, the upregulation and downregulation of predicted targets cannot be clarified by a network pharmacology approach and is therefore not conducive to an accurate understanding of the mechanism of components acting on targets. Second, only compounds of ZZP in TCMSP were analyzed, which might have caused the absence of some components and therefore limited the accuracy of the results. Third, limited by the deficiencies of systems biology, multidirectional pharmacology, computational biology, and network analysis, this study provided only preliminary predictions, and the results have not been verified in clinical and basic experiments. Thus, further pharmacological experimental validation is still necessary.The results of this study predict that the therapeutic effects of ZZP in GERD are mediated at least in part via PPARG, MMP9, JUN, TP53, PTGS2, EGFR, MAPK3, CASP3, AKT1, and VEGFA. These results may be useful in providing the foundation for subsequent experimental investigation and may offer ideas for the multidimensional and multilevel research of CHM formulae.