Sappan Lignum (SL) is effective in treating DPN. Previous reports have shown that SL has a clear hypoglycemic and anti-inflammatory effect. However, the study of SL in the treatment of DPN is still limited and rare.  Diabetic peripheral neuropathy (DPN) is one of the most common chronic complications of diabetes, which seriously affects the physical and mental health of patients.  To investigate the mechanism of SL in the treatment of DPN based on network pharmacology.  The active ingredients of SL were screened by related databases. The compound targets were collected by the target prediction platforms. The DPN-related targets were gathered through disease databases. The intersection targets were obtained by uploading the compound targets and disease targets to Venny 2.1.0, and a compound-target network was constructed by Cytoscape3.7.2. The protein-protein interaction (PPI) relationships were obtained by the STRING11.0 database. Genome Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID6.8 database. Molecular docking of key compounds and core targets was performed by DockThor. P < 0.05), including insulin resistance, HIF-1 signaling pathway, and type II diabetes. The docking results indicated that the main active compounds could stably bind to core targets.  A total of 29 compounds and 51 intersection targets with potential therapeutic effects on DPN were obtained. The compound-target network construction resulted in four key compounds: protostemonine, 3-deoxysappanchalcone, 7,3′,4′-trihydroxy-3-benzyl-2H-chromene, and o-12′-methylergocornine. PPI network analysis yielded 10 core targets: AKT1, MAPK3, CXCL8, TNF, OPRM1, MTOR, STAT3, MAPK8, SIRT1, and HSP90AA1. KEGG analysis resulted in 82 signaling pathways ( SL had the mechanism of multiple ingredients, multiple targets, and multiple pathways in the treatment of DPN. This study provided a scientific basis for further research on the treatment of DPN with SL and its extracts. DPN is one of the most common chronic complications of diabetes mellitus. The main clinical manifestations of DPN are limb pain, numbness, and paresthesia. Severe cases may be associated with peripheral vascular disease and infection-causing foot ulcers or gangrene, resulting in amputation or disability, threatening the quality of life and physical and mental health of patients. Because the pathogenesis has not been fully elucidated, the current clinical treatment of DPN is mainly to relieve clinical symptoms, including control of blood glucose, repair of nerves, antioxidative stress, improvement of microcirculation, and improvement of metabolic disorders .Sappan Lignum (SL) is the dried heartwood of Caesalpinia sappan L. of Leguminosae, which has the effects of activating blood circulation, removing blood stasis, relieving swelling, and relieving pain. Modern studies have shown that SL has pharmacological effects such as anti-inflammatory, antitumor, antiviral, antimicrobial, and antioxidant effects [ effects .SL has definite efficacy in the treatment of DPN, and Chinese herbal compound decoctions containing SL for the treatment of DPN have been widely used in clinical practice. Previous reports have shown that SL had a clear hypoglycemic and anti-inflammatory effect –5. HowevNetwork pharmacology  is a braTherefore, this study, based on network pharmacology, aims to investigate the mechanism of SL in the treatment of DPN. It provides a scientific basis for further research on the treatment of DPN with SL or its extracts and also provides a reference for the secondary development of SL.In this paper, based on network pharmacology, the mechanism of SL in the treatment of DPN was explored. Firstly, the ingredients of SL were searched through TCMID, and its active ingredients were screened by SwissADME. The active ingredients were imported into SwissTargetPrediction or SEA library to predict their targets. TTD, DrugBank, and DisGeNET databases were used to search for relevant targets of DPN. The intersection targets were obtained by uploading the ingredient targets and disease targets to Venny 2.1.0, and a compound-target (gene) network was constructed by Cytoscape3.7.2. The intersection targets were imported into the STRING11.0 database to build a PPI network. GO functional enrichment analysis and KEGG pathway enrichment analysis of intersection targets were acquired from the DAVID6.8 database. Molecular docking validation of key compounds and core targets was performed by DockThor. The workflow is summarized in https://www.megabionet.org/tcmid/) through using “SUMU” as the search term. The SMILE numbers of the ingredients were imported into SwissADME [http://www.swissadme.ch/) platform, which selects ingredients with high gastrointestinal absorption and drug-likeness column containing two or more “Yes” as the active ingredients of SL. The SMILE numbers of the active ingredients were uploaded to SwissTargetPrediction [http://www.swisstargetprediction.ch/) to predict the relevant target. SEA library [http://sea.bkslab.org/) was used as a supplement.The ingredients of SL were obtained from TCMID (wissADME  (http://ediction  (http:// library  (http://http://db.idrblab.net/ttd/), DrugBank (https://www.drugbank.ca/), and DisGeNET (https://www.disgenet.org/) databases, “diabetic neuropathy,” “diabetic peripheral neuropathy,” and so forth were searched as keywords to obtain targets related to DPN, and the collected targets were combined and duplicates removed.In the TTD  (http://https://bioinfogp.cnb.csic.es/tools/venny/) for intersection to obtain the potential targets of SL for the treatment of DPN.The ingredient targets of SL and the DPN-related targets were uploaded to Venny 2.1.0 (The information of the active compounds of SL and the corresponding targets of action was imported into Cytoscape3.7.2 software to construct a compound-target (gene) network. The topological properties of the network were analyzed, and the results were visualized.https://string-db.org/) database to obtain protein interaction relationships, and the results were imported into Cytoscape3.7.2 software to establish a PPI network and obtain visual analysis results.The intersection targets were uploaded to the STRING11.0 (https://david.ncifcrf.gov/) database is an online tool for GO enrichment analysis and KEGG pathway annotation analysis of the target. The intersection targets were imported into the DAVID6.8 database for GO enrichment analysis and KEGG pathway enrichment analysis, and the potential signaling pathways and mechanisms of action of SL in the treatment of DPN were screened using a threshold (P < 0.05).DAVID6.8 (http://www.rcsb.org/pdb/home/home.do). SDF format files of the three-dimensional structures of the compounds were downloaded from the PubChem (https://pubchem.ncbi.nlm.nih.gov/) platform. Finally, compounds and proteins were uploaded to DockThor [https://dockthor.lncc.br/v2/) to perform online molecular docking; then the docking results were obtained and visualized.The screened key compounds were subjected to molecular docking with core target proteins to verify the efficacy of the compounds. PDB format files of core target proteins were downloaded from the Protein Data Bank (PDB) .The intersection targets were imported into the DAVID6.8 database for GO enrichment analysis and KEGG pathway enrichment analysis, and the results were screened using a threshold (P < 0.05), including 155 BP terms, 23 CC items, and 38 MF terms. GO functional enrichment analysis yielded 216 GO terms (P < 0.05). As shown in P value. The top 10 signaling pathways were insulin resistance, HIF-1 signaling pathway, type II diabetes mellitus, acute myeloid leukemia, mTOR signaling pathway, AMPK signaling pathway, FoxO signaling pathway, insulin signaling pathway, Chagas disease (American trypanosomiasis), and nonalcoholic fatty liver disease (NAFLD).KEGG pathway enrichment analysis yielded 82 signaling pathways  [Studies have shown that protostemonine (PSN) is an anti-inflammatory alkaloid. PSN significantly inhibited lipopolysaccharide- (LPS-) induced MAPKs and AKT phosphorylation, iNOS expression, and NO production in macrophages. Meanwhile, PSN markedly attenuated LPS-induced inflammatory cell infiltration and reduced the production of proinflammatory cytokines (TNF-nd IL-6) , 14.It has been experimentally shown that 3-deoxysappanchalcone (3-DSC) played a role in the anti-inflammatory effect by inducing heme oxygenase-1 (HO-1) expression via activating the AKT/mTOR pathway. 3-DSC inhibited the production of NO and IL-6 by LPS-stimulated RAW264.7 cells; and it was considered as a valuable compound for modulating inflammatory conditions . In addiα-glucosidase and COX-II inhibitory activity [7,3′,4′-Trihydroxy-3-benzyl-2H-chromene is a homoisoflavan . Noshitaactivity . Studiesactivity .O-12′-methylergocornine is an ergot alkaloid. It has been shown that ergot alkaloids are vasoactive and can potentially alter arterial blood supply and venous drainage from the bovine foregut . It has The PPI network illustrated that AKT1, MAPK3, CXCL8, TNF, OPRM1, MTOR, STAT3, MAPK8, SIRT1, and HSP90AA1 were the top 10 protein targets; and these targets may be the main targets of SL in the treatment of DPN.AKT1 was one of 3 closely related serine/threonine-protein kinases  called the AKT kinase. AKT was responsible for the regulation of glucose uptake by mediating insulin-induced translocation of the SLC2A4/GLUT4 glucose transporter to the cell surface . AKT plaβ-cell function [DPN is an endocrine system disease with neuropathy and microangiopathy, involving multiple metabolic pathways. The top 10 DPN-related metabolic pathways, obtained by KEGG enrichment analysis of intersection targets, included insulin resistance, type II diabetes, and insulin signaling pathways, indicating that one of the main mechanisms of SL in the treatment of DPN is the regulation of blood glucose. For HIF-1 signaling pathway, hypoxia-inducible factor 1 (HIF-1) is an oxygen-regulated transcriptional activator involved in the regulation of oxygen in the cellular environment. Oxidative stress often causes neuronal dysfunction and cell death. Activation of the HIF-1 signaling pathway is one of the key mechanisms of cellular defense against oxidative stress . For mTOfunction . It has function . For AMPfunction . For Foxfunction .In summary, this study clarified the action mode of multiple compounds, multiple targets, and multiple pathways of SL in the treatment of DPN. A total of 29 active ingredients and 51 relevant targets of SL for the treatment of DPN were selected. The effectiveness of the binding of the top 4 key compounds to the top 10 core target proteins was verified by molecular docking. According to the results of the PPI network and KEGG analysis, it is considered that SL is mainly used to treat DPN by regulating blood glucose, resisting inflammation, resisting oxidative stress, nourishing nerves, and repairing vascular injury.Based on the action mode of multiple compounds, multiple targets, and multiple pathways of traditional Chinese medicine, following the principles of network pharmacology, we systematically explored the mechanism of action of SL in the treatment of DPN. This study provided a scientific basis for further research on the treatment of DPN with SL or its active ingredients, as well as a reference for the secondary development of SL.


Depression is a severe disabling psychiatric illness and the pathophysiological mechanisms remain unknown. In previous work, we found the changes in extrinsic coagulation (EC) pathway proteins in depressed patients compared with healthy subjects were significant. In this study, we screened differentially expressed proteins (DEPs) in the EC pathway, and explored the molecular mechanism by constructing a protein-protein interaction (PPI) network. The DEPs of the EC pathwaywere initially screened by isobaric tags for relative and absolute quantification (iTRAQ) in plasma samples obtained from 20 depression patients and 20 healthy controls, and were then identified by Enzyme-linked immunosorbent assays (ELISAs). Ingenuity Pathway Analysis (IPA) software was used to analyse pathway. The differentially expressed genes (DEGs) were identified by analyzing the GSE98793 microarray data from the Gene Expression Omnibus database using the Significance Analysis for Microarrays  statistical method. Cytoscape version 3.4.0 software was used to construct and visualize PPI networks. The results show that Fibrinogen alpha chain (FGA), Fibrinogen beta chain (FGB), Fibrinogen gamma chain (FGG) and Coagulation factor VII (FVII) were screened in the EC pathway from depression patient samples. FGA, FGB, and FGG were significantly up-regulated, and FVII was down-regulated. Thirteen DEGs related to depression and EC pathways were identified from the microarray database. Among them NF-κB Inhibitor Beta (NFKBIB) and Heat shock protein family B  member 1 (HSPB1) were highly correlated with EC pathway. We conclude that EC pathway is associated with depression, which provided clues for the biomarker development and the pathogenesis of depression. As an etiologically heterogeneous condition , depressTo date, much progress has been made in the study of the potential etiology of depression, such as the involvement of monoamine neurotransmitters  , brain-dIn previous work , we adophttp://www.hprd.org/) contains manually curated scientific information pertaining to the biology of most human proteins, is the largest human PPI database for document mining. Studying PPI networks through bioinformatics can reveal protein interactions that are important for the discovery of the target proteins and the pathogenesis of the disease [The Human Protein Reference Database (HPRD)  were recruited from the psychiatric center of the Third Hospital in Daqing City, Heilongjiang Province, China. 20 healthy controls were recruited at Harbin Medical University-Daqing Campus. Patients were diagnosed according to the Fourth Edition of the Diagnostic and Statistical Manual of Mental Disorders by a psychiatrist using the Mini International Neuropsychiatric Interview . The depWe were approved prior to the study by the Medical Ethics Committee of Harbin Medical University (China), and an informed consent agreement was obtained from all participants before study initiation. This study adheres to the latest version of the Declaration of Helsinki.2.2.Blood samples (5 ml) were drawn from the antecubital vein between 7:00 and 9:00 a.m. and incubated at room temperature for 20 min for blood coagulation, followed by centrifugation at 2000 × g for 20 min at 4°C. Then, the supernatants were stored at −80°C until analysis .2.3.www.ingenuity.com). For the disease analysis results, the EC pathway was selected as the research target. In this experiment, we used IPA software to draw the pathway chart, and the DEPs were briefly analyzed.In previous works , 153 DEP2.4.Commercially available sandwich ELISA kits  were used to detect the content of DEPs such as FGA (abx515963), FGB (abx516323), FGG (abx253654), FVII (EKC33175), FX(abx514145), FXII(abx151465), FII (abx151106), SERPINC1 (abx514280), PROC(abx350615) and PROS1(abx518986) in 40 plasma samples. The protocol was followed according to the manufacture’s instructions. A microplate reader  was used to determine the absorbance of the standards and samples at 450 nm. The intra- and inter-day variations were < 8%, and the concentrations of the samples were calculated according to a standard curve.2.5.http://www.ncbi.nlm.nih.gov/ geo). A total of 128 specimens, including 64 control group and 64 depressions without anxiety disorder, were evaluated in this data set [The gene expression profile of GSE98793 was obtained from the National Center for Biotechnology Information (NCBI) GEO database .2.7.The PPI network in the HPRD database was downloaded and visualized using Cytoscape version 3.6.0. The DEGs and DEPs were mapped into the PPI network, first neighbors of both them were extracted to form a sub-network. The nodes with degree≤5 in the subnetwork were discarded, thus forming a PPI network map related to the DEPs in the EC pathway of depression patients. Then the molecular functions of the DEGs were analyzed using the ClueGO plug-in. The experimental design is shown in 2.8The ELISA data are expressed as the mean ± SD, and p < 0.05 was considered statistically significant. Between-group comparisons were analyzed by the Student’s t-test. All statistical analyses were performed using Graph Pad Prism .3.By studying the differentially expressed proteins of the exogenous coagulation pathway in the plasma samples of depression patients, we found that FGA, FGB, FGG and FVII in the EC pathway have all changed significantly. The PPI results showed that NFKBIB and HSPB1 had the highest correlation with the differentially expressed proteins in the EC pathway.3.1.The detailed demographic and clinical characteristics of the recruited subjects are summarized in 3.2.The EC pathway is the main pathway of blood coagulation. When the body experiences inflammation or tissue damage, tissue factor (TF) and the activated FVII (FVIIa) form TF-FVIIa complex. Then, the complex binds to activated FXa in the presence of phospholipid membranes and calcium ions, and further binds to FVa, forming a TF-FVIIa-FXa-FVa complex through the thrombin cascade, which changes the prothrombin to thrombin. Under the action of thrombin, fibrinogen becomes a fibrin monomer, which further generates blood clots and completes the clotting process. When the body is in the coagulation state for a long time, thrombin will activate the production of anticoagulant substances such as protein C (PROC), protein S (PROS), antithrombin III (ATIII), tissue factor pathway inhibitor (TFPI), etc., and regulate the balance of coagulation and anticoagulant system through negative feedback.In our previous work, we used the iTRAQ method to find 10 DEPs in the EC pathway: FGA, FGB, FGG, FVII, FX, FXII, FII (PT), SERPINC1 (AT-III), PROC and PROS1. In this study, the pathway diagram was created using IPP software . Down-re3.3.After preprocessing, 153 DEGs  in the depressive patient chip were screened out. Then the 153 DEGs and 4 DEPs  were mapped into the PPI network from HPRD database, first neighbors of both them were extracted to form a PPI sub-network. In the sub-network, each node represents a protein or gene, and each line represents an association between them. Nodes that were not associated with other nodes were deleted, and nodes with a degree of association less than or equal to 5 were also deleted. The results of the PPI sub-network diagram were shown in As shown in the figure, there were 13 DEGs interacting with the DEPs found in the EC pathway in the PPI network, with 5 up-regulated  and 8 down-regulated . The results were shown in The molecular functions of the 13 DEGs were analyzed using the ClueGO plug-in to reveal possible mechanisms of action . We foun4.Based on the previous work, this article screened four key proteins in the EC pathway of depression patients by iTRAQ and ELISA. Among them, the study on the correlation between FVII, FGA and depression has been reported ,47, but We found that compared with healthy people, the EC pathway was abnormal in patients with depression. In particular, the changes of F FVII, FGA, FGB and FGG in pathways were very obvious. FGA, FGB and FGG were up-regulated and FVII was down-regulated. FGA, FGB, and FGG are the three polypeptide chains that make up fibrinogen. As key proteins in the end of EC pathway, an increase in FGA, FGB and FGG indicate that the pathway is activated and the body has developed a hypercoagulable state. This result is consistent with previous literature reports. Literature showed that increased depressive symptoms were significantly associated with fibrinogen levels  and therFVII is one of the promoters of the EC pathway. It is an essential vitamin K dependent factor and a serine protease in hemostasis . In prevWe also conducted further research on the key proteins found using bioinformatics methods. We use the protein data in the HPRD database as the basis because there are more than 30,000 interacting protein data in the database and it is the most comprehensive text mining database related to human genes and proteins. By mapping the DEPs of the EC pathway we screened and the DEGs of depression screened by the microarray into the PPI network, the first-neighbors was extracted and the subnetwork was establishment, forming a PPI network related to both depression and the EC pathway. In the network we can discover the potential relationship between these DEPs and DEGs, thus demonstrating the relevance of the EC pathway and depression, and provide clues for further study of the mechanism of action. In the PPI subnetwork we constructed, we found that there are multiple DEGs that interact with the DEPs. Here we focus on the first two genes that are ranked far ahead of the relationship, namely NFKBIB and HSPB1.NFKBIB (NF-κB Inhibitor Beta), also known as ikappabeta or iκbβ. The protein encoded by this gene belongs to the NF-κB inhibitor family and inhibits NF-κB by forming complexes and trapping them in the cytoplasm . When th2+ influx, phosphorylation of intracellular proteins and activation of related signaling pathways. Thus, it can participate in inflammatory response and induce the inflammatory process and the initiation of exogenous coagulation. Therefore, we boldly speculated the results of this experiment: the interaction between up-regulation of NFKBIB and over activation of NF-κB forms a vicious circle, eventually triggering inflammation, and inflammation activates the EC pathway, both of which contribute to involve in the mediation of depression.The PPI results of this study show that abnormalities of EC pathways in depression are highly correlated with the up-regulation of NFKBIB. As mentioned earlier, up-regulation of NFKBIB leads to over-activation of NF-κB, and activation of NF-κB is a central link in the inflammatory response, leading to increased secretion of cytokines and inflammation. The inflammation hypothesis has been widely recognized by the public as one of the hypothesis of the onset of depression . The infHSPB1  Member 1), also known as HSP27, is a member of the heat shock protein family and is a small-molecule heat shock protein commonly found in eukaryotic cells and serves as an ATP-independent molecule partner . It has GlueGO results showed that the molecular functions of the 13 DEGs screened in the PPI network map were mainly concentrated on Kappa-type opioid receptor (KOR) binding. A large number of studies have shown that KOR system is involved in the pathophysiology of affective disorders, drug addiction and depression. The use of KOR agonists in animals and humans can produce anxiety and depression-like effects  whereas Depression has always been a research hotspot in the medical field. In addition to using bioinformatics technology , we also5.In conclusion, we screened 4 DEPs  in the EC pathway from depression patients’ plasma and provided potential biomarkers for the diagnosis of depression. Furthermore, we speculate that NFKBIB and HSPB1 play an important role in the relationship between these differential proteins and depression.


Exploration of the underlying molecular mechanism of Jinchan Oral Liquid (JOL) in treating children with the respiratory syncytial virus (RSV) pneumonia to provide new evidence for the clinical application.  The active components and target genes of JOL were screened by the TCMSP database. The targets of RSV pneumonia were obtained from the GeneCards, OMIM, DrugBank, and PharmGKB database. Then, we constructed the active component-target network and screened the core genes. The overlaps were screened for PPI network analysis, GO analysis, and KEGG analysis. Finally, result validation was performed by molecular docking.  According to the screening criteria of the ADME, 74 active compounds of JOL were obtained; after removing redundant targets, we selected 180 potential targets. By screening the online database, 893 RSV pneumonia-related targets were obtained. A total of 82 overlapping genes were chosen by looking for the intersection. The STRING online database was used to acquire PPI relationships, and 16 core genes were obtained. GO and KEGG analyses showed that the main pathways of JOL in treating RSV pneumonia include TNF signaling pathway and IL17 signaling pathway. The molecular docking results showed that the active compounds of JOL had a good affinity with the core genes.  In this study, we preliminarily discussed the main active ingredients, related targets, and pathways of JOL and predicted the pharmacodynamic basis and the potential therapeutic mechanisms of RSV pneumonia. In summary, the network pharmacology strategy may be helpful for the discovery of multitarget drugs against complex diseases. Community-Acquired Pneumonia (CAP) is a leading cause of hospitalization in children younger than five years . RespiraTraditional Chinese medicine has a long history of thousands of years. The prominent feature of Chinese medicine treatment is based on syndrome differentiation, and it emphasizes the recovery of overall function. Jinshan Oral Liquid (JOL) is a traditional Chinese compound prescription developed by Children's Hospital Affiliated to Soochow University. JOL comes from “famous doctors and prescriptions.” It is mainly composed of four herbs: honeysuckle (Jin Yin Hua: JYH), Scutellaria baicalensis (Huang Qin: HQ), bupleurum (Chai Hu: CH), and cicada slough (Chan Tui: CT). Our previous study found that JOL is effective and safe in treating children with respiratory syncytial virus (RSV) pneumonia, and it can improve clinical symptoms and shorten the length of hospital stay. However, the mechanisms by which JOL exerts these effects remained unclear.Network pharmacology , 7 is a https://tcmspw.com/index.php). TCMSP, a platform of Chinese herbal medicines, captures the relationships between drugs, targets, and diseases [https://tcmspw.com/load_intro.php?id=43) and adjusted the screening conditions as “OB ≥ 20%” and “DL ≥ 0.1.” The related targets of JOL active ingredients were also obtained from the TCMSP database. Then, the targets were entered into UniProt (https://www.uniprot.org/). Through retrieval and transformation, we finally got the gene symbol of active ingredients.All compounds of the four Chinese medicinal herbs in JOL were collected and integrated by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform , (2) Online Mendelian Inheritance in Man , (30 DrugBank database (https://go.drugbank.com/), and (4) Pharmacogenomics Knowledgebase . The keywords “respiratory syncytial virus pneumonia” were used to obtain the disease-associated targets. All targets of RSV pneumonia were gathered together, and the results were visualized by R 3.6.3.The RSV pneumonia-related target proteins were screened from four sources: (1) the Human Gene Database  database for PPI analysis. The condition was limited to “Homo sapiens” for species. A high confidence level was set at 0.700 so that we could get the appropriate required interaction score.Overlaps between JOL-related targets and RSV pneumonia-related targets were screened by R 3.6.3 to clarify the interaction between JOL and the disease. Then, the overlapping genes were put into STRING ,” “Closeness Centrality (CC),” “Degree Centrality (DC),” “Eigenvector Centrality (EC),” “Local Average Connectivity-based method (LAC),” and “Network Centrality (NC)” were selected to screen the core genes. The critical targets for JOL treatment of RSV pneumonia were screened based on the following criteria: BC ≥ Avg(BC), CC ≥ Avg(CC), CC ≥ Avg(DC), EC ≥ Avg(EC), LAC ≥ Avg(LAC), and NC ≥ Avg(NC). The core targets were screened out after being extracted twice.With the Bioconductor package in R software, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. GO enrichment mainly analyzed the biological process, cellular composition, and molecular function of the targets. KEGG pathway enrichment analyzed the vital biological pathways of the targets.https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index) was used to analyze the docking results. Finally, PyMOL v2.4.0 software was chosen to visualize the result. The conformation with the best affinity and the lowest binding energy was selected as the final docking result.Molecular docking simulation was used to verify the credibility of the study. The structural formula (SDF format) of the active ingredients was downloaded from the PubChem database. Then, we use ChemBioDraw 3D software to create 3-dimensional chemical structures and minimize the energy. The crystal structures of the core genes were obtained from the RCSB PDB database and modified using PyMOL v2.4.0 software, including solvent and organic removal. Before molecular docking, AutoDockTools v1.5.6 software was used to add hydrogen atoms. The core genes were used as receptors, and the active ingredients were used as ligands. AutoDock Vina v1.1.2 was run to perform molecular docking. The online Protein-Ligand Interaction Profiler , Huang Qin , Chai Hu , and Chan Tui  were identified from the TCMSP database. Based on the screening criteria of the ADME, 74 active compounds were retrieved after duplicated targets were eliminated. These active compounds originated from JYH (23 components), HQ (36 components), CH (17 components), and CT (3 components), including five duplicated targets. Details of the active component information are shown in Supplementary Table The active component-target interaction network was constructed by Cytoscape v3.8.0 software . The cirp value of <1.0e-16. Network nodes represented proteins, and the edges represented protein-protein associations. The thickness of the lines between nodes meant the confidence prediction of the interaction, and the thicker the line, the stronger the interaction relationship between proteins.The STRING online database was used to acquire PPI relationships of potential protein targets of JOL as related to the treatment of RSV pneumonia, and the results are shown in . The PPIThe CytoNCA plug-in of Cytoscape v3.8.0 software was used to calculate the topology through network analyzer. As shown in q value < 0.05) are shown in Figures To explore the mechanism of JOL in the treatment of RSV pneumonia, we used R 3.6.3 software to perform GO and KEGG functional enrichment analysis on the 82 drug-disease common genes. GO analysis included three levels: biological process (BP), cellular component (CC), and molecular function (MF). A total of 2475 GO enrichment results were obtained, of which are BP 2305, CC 47, and MF 123. The top ten terms in BP, CC, and MF are, respectively, shown in Figures Molecular docking can simulate the interaction between ligand and receptor and predict the affinity by calculating the binding energy. The small-molecule ligand can spontaneously bind to the macromolecular receptor when the binding energy is lower than zero . When thAs the most common respiratory pathogen, RSV is a cause of morbidity and mortality in those prematurely born infants and children with high-risk factors . Lower rIn the JOL's active component-target network, a total of 180 targets affected by 74 active compounds in the JOL were selected. There were three top important compounds, including quercetin, luteolin, and kaempferol, identified as the potential active ingredients of JOL.As a natural flavonoid, quercetin showed several biological activities, and it could block the adhesion of RSV . StudiesThen, we retrieved 893 RSV pneumonia targets from GeneCards, OMIM, DrugBank, and PharmGKB database. Through target mapping, we found 82 targets in common between active components and the disease targets and built the connections of the “ingredient-target” network.α, CXCL8, and MMP9 in acute RSV infection [α played a vital role in RSV-induced exacerbations in allergic airway disease [By constructing the PPI network, topological parameters for the recognition of essential nodes were calculated. 16 genes were found to be the core genes of JOL in treating RSV pneumonia. These genes were connected with host immunity, oxidative stress, virus, and other pathogenic microorganisms. We focused on the most relevant genes that had previously been reported. Recent studies were performed to evaluate the relative roles of IL-1B, IL-6, TNF-nfection –25. IL6 nfection . The expnfection . TNF-α p disease . CXCL8 h disease , while R disease . MMP9 is disease . ExpressGO and KEGG enrichment analysis revealed that JOL could regulate the progress of immune pathways and virus defense. The ten significant GO (BP) terms indicated that JOL could control the pathogenic microbial stimulation and the oxidative stress process during the treatment of RSV pneumonia. KEGG pathway enrichment results showed potential pathways. Most of the target genes were enriched in the virus-related signaling pathways, TNF signaling pathway, and IL-17 signaling pathway. Several studies –33 have α, IL-6, IL-1B, and IL-8 [κB and MAPK, to inhibit the expression of inflammatory cytokines [Serafini et al. reported that flavonoids could inhibit the expression of proinflammatory cytokines/chemokines such as TNF-and IL-8 . Evidencytokines . Based oWe also performed molecular docking to predict complex interactions between the three active ingredients and the protein targets. The binding energies could help to verify the reliability of the docking results further, and the results demonstrated good binding properties. All the binding energy were less than -7 kcal/mol. The interaction points indicated that quercetin, luteolin, and kaempferol might play an important role in treating RSV pneumonia.We analyzed the potential mechanism by which JOL effectively treated RSV pneumonia based on network pharmacology. A possible association between active ingredients and core genes was discovered through the systematic strategy, which further verified the reliability by molecular docking technology. Therefore, traditional Chinese medicine such as JOL may be an alternative approach for treating RSV infection. But there were still some limitations. Further experimental validation is needed to support our research.


Codonopsis pilosula) is a traditional Chinese medicine commonly used in the treatment of OS worldwide. However, the molecular mechanisms of Dangshen in OS remain unclear. Hence, in this study, we aimed to systematically explore the underlying mechanisms of Dangshen in the treatment of OS. Our study adopted a network pharmacology approach, focusing on the identification of active ingredients, drug target prediction, gene collection, gene ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and other network tools. The network analysis identified 15 active compounds in Dangshen that were linked to 48 possible therapeutic targets related to OS. The results of the gene enrichment analysis show that Dangshen produces a therapeutic effect in OS likely by regulating multiple pathways associated with DNA damage, cell proliferation, apoptosis, invasion, and migration. Based on the network pharmacology approach, we successfully predicted the active compounds and their respective targets. In addition, we illustrated the molecular mechanisms that mediate the therapeutic effect of Dangshen in OS. These findings may aid in the development of novel targeted therapies for OS in the future.Osteosarcoma (OS) is the most common type of primary bone tumor in children and adults. Dangshen ( Osteosarcoma (OS) is the most common type of cancer of the bones. It is a malignant tumor that primarily affects the long bones , but it can also start in other bones. OS is rarely diagnosed in patients under five years of age, and the bimodal age-incidence curve peaks during the second decade of life (10–20 years old) and late adulthood (>40 years old) , with a Codonopsis pilosula). Dangshen belongs to the family of Campanulaceae, a precious plant that grows at altitudes of 2000 meters in southern China [In recent years, some hospitals have assessed the efficacy of traditional Chinese medicines as long-term treatments for OS. In some cases, orally administered Shenqi could decrease the growth, metastasis, and the number of chemotherapy-related side effects significantly in patients, especially those who received systemic chemotherapy , 11. Thern China . The dryrn China . Dangshern China . Dangshern China , 16.DNA damaging therapies are widely used as trigger molecules to study the signaling pathways of OS . There hhttp://tcmspw.com/tcmsp.php) database [http://www.megabionet.org/tcmid/) [A flowchart of the study design is shown in database  and Trad/tcmid/) . TCMSP p/tcmid/) . The dru/tcmid/) . Therefo/tcmid/) . We searhttps://pubchem.ncbi.nlm.nih.gov/) is an open chemistry database of the National Institutes of Health (NIH) [PubChem (th (NIH) . This dath (NIH) . We impohttp://www.lilab-ecust.cn/pharmmapper/check.html) is a freely accessed web server that uses the pharmacophore mapping approach to identify potential small-molecule targets [PharmMapper  and GeneCards (https://www.genecards.org/). OMIM is an authoritative and comprehensive database of human genes and genetic phenotypes [The human genes associated with OS were gathered from OMIM  is a database that contains known and predicted protein-protein interactions, and it collects the information using bioinformatics strategies [Homo sapiens,” and the PPIs with confidence scores >0.4 were selected for this study.The therapeutic targets were imported into the STRING database to obtain their interaction relationship. STRING (rategies . The spehttp://biogps.org) database. BioGPS is an extensible and customizable genetic annotation portal that enables researchers to acquire distributed genetic annotation resources [Data about the organ targets were collected from the BioGPS (esources . To revehttp://www.cytoscape.org/) was used to present the networks mentioned above, which is a software program for network visualization [The network of active compounds and therapeutic targets was constructed by linking the compounds and therapeutic targets to understand the complex interactions between the compounds of Dangshen and the therapeutic targets of OS. The network of therapeutic targets and organs was established by linking therapeutic targets and their distribution in organs to clarify the relationship between the therapeutic targets and organs with increased expression of the target. The therapeutic targets' PPI network was built by linking the therapeutic targets to their interacting targets. Next, Cytoscape version 3.7.2  to clarify the possible biological mechanisms [https://www.kegg.jp/) is a database for extracting biological information about functional classification, annotation, and enriched pathways of various genes [To learn more about the role of therapeutic targets involved in the biological process (BP), cell component (CC), and molecular function (MF), we used the Gene Ontology (GO) database , cell component (CC), and molecular function (MF) for the 48 therapeutic targets. The top ten biological processes, cell components, and molecular functions are shown in Figures p < 0.05. These 69 pathways involve human diseases, pathophysiological mechanisms, and signaling pathways. The top ten significantly enriched signaling pathways include the p53 signaling pathway, PI3K-Akt signaling pathway, neurotrophin signaling pathway, FoxO signaling pathway, Wnt signaling pathway, ErbB signaling pathway, TGF-β signaling pathway, HIF-1 signaling pathway, sphingolipid signaling pathway, and MAPK signaling pathway. Many therapeutic targets are involved in these signaling pathways. KEGG pathway enrichment analysis was performed to explore the underlying mechanisms of Dangshen against OS further. As shown in Supplementary Codonopsis pilosula) is a well-known herbal medicine, and traditional Chinese medicine (TCM) preparation, based on Dangshen, which has shown high efficacy in the treatment of OS [Osteosarcoma (OS) is the most common primary bone tumor found in the clinic . It is cnt of OS , 11. HowAfter applying the screening methods, we identified 15 active compounds and 48 potential therapeutic targets. The active compounds of Dangshen likely treat OS by regulating these targets. We identified two active compounds, stigmasterol and luteolin, that have been studied previously for their efficacy against OS. Stigmasterol is a phytosterol, which has been shown to exert anticancer, antipyretic, and immune-modulating properties –44. PrevFrom the therapeutic target-PPI network, the following targets showed larger degree values: TP53, HSP90AA1, CCND1, AR, ERBB2, MDM2, IGF1R, DICER1, CCNE1, and SOD2. These targets may play a major role in the therapeutic effect of Dangshen against OS. Over 70% of OS cases show structural variants or mutations in the TP53 gene . TP53 isNext, we performed the GO enrichment analysis and KEGG pathway enrichment analysis of the therapeutic targets. Based on the GO terms, the therapeutic targets showed a strong correlation with the biological processes, such as the G1/S transition of mitotic cell cycle, cell cycle G1/S phase transition, regulation of cell cycle arrest, and the intrinsic apoptotic signaling pathway; cell components, such as cell leading edge, ruffle, ruffle membrane, and endocytic vesicle; and molecular functions, such as p53 binding, disordered domain specific binding, histone deacetylase binding, damaged DNA binding, and ATPase binding. Hence, the mechanism of action for Dangshen may include biological processes, molecular functions, and various cellular components. For example, imbalanced cell cycle regulation is characteristic of tumor cells, and functional defects in cell cycle checkpoints lead to genetic changes that lead to tumor development and progression , 63. In β-catenin signaling is closely related to the formation, metastasis, and apoptosis of many cancers [β-catenin signaling was recently observed in OS [Based on the KEGG terms, the therapeutic targets for Dangshen against OS were primarily associated with the p53 signaling pathway, PI3K-Akt signaling pathway, FoxO signaling pathway, Wnt signaling pathway, and ErbB signaling pathway. P53 plays a critical role in cell cycle checkpoints regulation, DNA damage, and prevention of nonmalignant cells from developing malignant phenotypes , 71. In  cancers . The upred in OS . As suched in OS . The WIFed in OS . Previoued in OS . Hence, Network pharmacology is an analytical method still in development worldwide. However, the method has some inherent flaws. For example, it heavily relies on existing resources of the databases, so it cannot analyze the compounds, targets, or mechanisms that have not been previously explored. Moreover, its predicted active ingredients, targets, and mechanisms of action are all purely theoretical, and there is a lack of experimental verification. Therefore, further clinical investigations are needed.In this study, we explored the therapeutic mechanisms of Dangshen against OS through a network pharmacology approach. The therapeutic properties of Dangshen against OS arise from the regulation of biological pathways involved in the proliferation, apoptosis, invasion, and migration of cells, along with DNA damage. We believe these findings demonstrate the importance of understanding traditional Chinese medicines. The current study relied on data mining and analysis, and further clinical investigations are needed to verify the therapeutic mechanisms of Dangshen against OS.


Systemic lupus erythematosus (SLE) is a refractory immune disease, which is often complicated with osteonecrosis of the femoral head (ONFH). Curcumin, the most active ingredient of Curcuma longa with a variety of biological activities, has wide effects on the body system. The study is aimed at exploring the potential therapeutic targets underlying the effect of curcumin on SLE-ONFH by utilizing a network pharmacology approach and molecular docking strategy.  Curcumin and its drug targets were identified using network analysis. First, the Swiss target prediction, GeneCards, and OMIM databases were mined for information relevant to the prediction of curcumin targets and SLE-ONFH-related targets. Second, the curcumin target gene, SLE-ONFH shared gene, and curcumin-SLE-ONFH target gene networks were created in Cytoscape software followed by collecting the candidate targets of each component by R software. Third, the targets and enriched pathways were examined by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Eventually, a gene-pathway network was constructed and visualized by Cytoscape software; key potential central targets were verified and checked by molecular docking and literature review. P value ≤ 0.05. 20 related signaling pathways were identified by KEGG analysis, when corrected according to a Bonferroni P value ≤ 0.05. Molecular docking showed that the top three genes  have good binding force with curcumin; combined with literature review, some other genes such as TNF, CCND1, CASP3, and MMP9 were also identified.  201 potential targets of curcumin and 170 related targets involved in SLE-ONFH were subjected to network analysis, and the 36 intersection targets indicated the potential targets of curcumin for the treatment of SLE-ONFH. Additionally, for getting more comprehensive and accurate candidate genes, the 36 potential targets were determined to be analyzed by network topology and 285 candidate genes were obtained finally. The top 20 biological processes, cellular components, and molecular functions were identified, when corrected by a  The present study explored the potential targets and signaling pathways of curcumin against SLE-ONFH, which could provide a better understanding of its effects in terms of regulating cell cycle, angiogenesis, immunosuppression, inflammation, and bone destruction. Systemic lupus erythematosus (SLE) is a chronic autoimmune disease, which prevalence ranges from 20 to 150 cases per 100,000 population and appears to be increasing as the disease is recognized more readily and survival increases –9. The phttp://pubchem.ncbi.nlm.nih.gov), which is an open chemical database that provides information on compound structures and descriptive data.The 2D chemical structure of curcumin  was obtahttp://www.swisstargetprediction.ch/) is a web-based tool, on-line since 2014, to perform ligand-based target prediction for any bioactive small molecule [http://herb.ac.cn/) was used to get curcumin targets for a comprehensive collection of drug targets, which is a high-throughput experiment- and reference-guided database of traditional Chinese medicine [http://www.megabionet.org/tcmid/) and TCMSP (https://tcmspw.com/tcmsp.php) databases [https://www.uniprot.org/) for screening. In order to increase credibility, we only need human targets which have been annotated and reviewed, and the potential targets of curcumin were identified by eliminating duplicate and nonstandard targets.The Swiss target prediction database  using the phrase “Systemic lupus erythematosus”, “SLE”, “Osteonecrosis of Femoral Head”, and “ONFH” as the keyword. The targets were also supplemented with the OMIM database (https://omim.org/) [The SLE and ONFH-related targets were identified in the GeneCards database (im.org/) . A Venn https://string-db.org/), which is including direct and indirect interactions between proteins and calculates a confidence score for all protein interactions [The curcumin target network and SLE-ONFH network were constructed and visualized using Cytoscape 3.8.0 software –33; theyractions , 35. Theractions , 37.Shared targets of curcumin, SLE, and ONFH were obtained by running the Venn script as described above, which was installed in the R 4.0.2 software. They were uploaded to Cytoscape software. The PPI data were obtained from the Database of Interacting Proteins (DIP™), Biological General Repository for Interaction Datasets (Bio GRID), Human Protein Reference Database (HPRD), IntAct Molecular Interaction Database (IntAct), Molecular INTeraction database (MINT), and biomolecular interaction network database (BIND) using the plugin BisoGenet of Cytoscape software .After uploading the common gene above, the nodes with topological importance in the interaction network were screened by calculating Degree Centrality (DC), betweenness centrality (BC), closeness centrality (CC), Eigenvector Centrality (EC), local average connectivity-based method (LAC), and Network Centrality (NC) with the Cytoscape plugin CytoNCA. At the heart of Cytoscape is a network. A simple network diagram consists of nodes and edges. Each node represents a gene. The node-node connection represents the interaction between these nodes. DC believes that the greater the number of neighbors of a node, the greater its impact. CC was used to calculate the importance of nodes. BC is a kind of centrality measure. EC is an indicator in ranking the importance of nodes in the network. The closer the EC of a node is to the network radius, the closer the node is to the network center. LAC is often used to determine the importance of proteins by assessing the relationship between proteins and their neighbors. NC is a method based on the edge clustering coefficient, which considers not only the centrality of nodes but also the relationship between nodes and adjacent nodes –41. ThesP value ≤ 0.05 was set as the default option. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis method is similar to the GO analysis above.GO analysis with the biological process, cellular component, and molecular function was carried out by R software. The result is obtained by running the script installed on R software including “colorspace,” “stringi,” “ggplot2,” “BiocManager,” “clusterProfiler,” and “enrichplot.” The top 20 items were selected and visualized; a Bonferroni-corrected The gene-pathway network was constructed based on the significantly enriched pathways with genes that regulated these pathways. The topological analysis of 20 pathways and 285 genes was carried out with BC. The squares represented target genes, and the V-shapes represented pathways in the network.Molecular docking simulations were used to explore the potential interaction between the top 3 genes of the network above. The SDF format of curcumin was downloaded from the PubChem database; Chem 3D software was used to convert the SDF format into mol2 format file; then, the RESB database was used to get the PDB format structure of the top 3 target proteins. After that, solvent molecules and ligands were removed by Pymol software and saved as pdbqt format. Docking simulations were performed in Autodock1.1.2 software, and Discovery Studio 2020 was used to visually analyze the docking conformation at last , 44.100, 152, and 11 target genes of curcumin were identified by searching the Swiss, HERB, and STITCH databases, respectively, and 240 genes were selected as curcumin target candidate genes through running R software to remove duplicate genes. In order to ensure credibility, the 240 target genes were inputted into the STRING database; by setting the highest confidence (0.9000) and selecting the condition “Homo sapiens,” 201 targets were finally identified after hiding disconnected nodes in the network. The PPI network of them was saved as “tsv” file and visualized by Cytoscape software according to the degree . This neAfter removing the duplication, we obtained 4338 SLE-related targets and 300 ONFH-related targets from the GeneCards and OMIM databases, respectively. In order to improve the comprehensiveness and credibility of the data, these targets were all included in the current study. Finally, 170 intersection genes related to SLE-ONFH were obtained by running the Venn map script installed in R software, and 36 genes related to SLE, ONFH, and curcumin were also received, as shown in According to the network pharmacology analysis method, 36 overlapping genes for curcumin, SLE, and ONFH were defined as potential genes  for curcTo reveal the mechanisms underlying curcumin's effects on SLE-ONFH and obtain candidate genes more comprehensively and accurately, topological analysis of 36 potential genes was performed through the plugin BisoGenet and CytoNCA of Cytoscape; a network consisting of 3653 nodes and 87728 edges is presented in P value < 0.05). The results demonstrated many targets involved in multiple BPs associated with mRNA catabolic process, regulation of DNA-binding transcription factor activity, and regulation of cell cycle phase transition, which confirmed the correlation with the gene transcriptional activity in SLE-ONFH. The CC results suggested that most targets are localized to the focal adhesion, cell-substrate junction, and nuclear chromatin. The MF results showed that many targets are associated with ubiquitin protein ligase binding and DNA-binding transcription factor binding.GO analysis of 285 candidate targets was analyzed based on biological process (BP), cellular component (CC), and molecular function (MF). 2,364 GO terms were finally enriched, 1,973 in biological process, 200 in cellular component, and 191 in molecular function. The top 20 terms are shown in P value < 0.05) are identified and shown in The pathways that are significantly influenced by curcumin in the process of treating SLE-ONFH were identified by KEGG pathway analysis; the analysis method is similar to the GO analysis by running the corresponding script in R software. The top 20 significant pathways , according to the network analysis. The results revealed that they can interact with curcumin; the combination of curcumin with them is shown in SLE is an autoimmune disease with multiple organ involvement and multiple antibodies. It is an urgent problem in the field of medicine. High disease activity of SLE is closely related to osteonecrosis especially ONFH, which has become the main cause of disability in SLE patients. In the whole process of disease development, immunity, inflammation, apoptosis, and angiogenesis are important factors –54. Becaβ, is associated with angiogenesis and vascularization and facilitates inflammation and joint damage [Curcumin has been found to be useful in the treatment of SLE and ONFH, but the central genes and significant pathways of curcumin against SLE-ONFH are not clear. Through the target interaction network, we found 36 potential targets as described in Results; there were 16 targets  with connectivity greater than the median degree. The TP53 gene is at the core of the whole network, which means that it is a central target. p53 protein is a tumor suppressor that inhibits the growth of aberrant cells; functional p53 is believed to sense DNA damage and, subsequently, to induce DNA repair, growth arrest, or apoptosis of the aberrant cell , 57. On t damage . CXCL8 it damage . FGF2 ist damage . SERPINEt damage .At present, the treatment of SLE depends on the organ involved in the disease. Patients with mild symptoms can use low-dose corticosteroids; however, moderate and severe SLE may require higher doses of corticosteroids or other immunosuppressive agents. The adverse effects and limitations of hormones and immunosuppressive agents make TCM become a candidate therapeutic drug because of their unique advantages. The characteristic of SLE is that the complement system is activated; curcumin could inhibit the complement cascade. Studies have found that curcumin inhibits the increase of matrix protein, glial fibrillary acidic protein, and vimentin in the hippocampus of lupus mice . As ment


Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with extensive genetic and aetiological heterogeneity. While the underlying molecular mechanisms involved remain unclear, significant progress has been facilitated by recent advances in high-throughput transcriptomic, epigenomic and proteomic technologies. Here, we review recently published ASD proteomic data and compare proteomic functional enrichment signatures with those of transcriptomic and epigenomic data. We identify canonical pathways that are consistently implicated in ASD molecular data and find an enrichment of pathways involved in mitochondrial metabolism and neurogenesis. We identify a subset of differentially expressed proteins that are supported by ASD transcriptomic and DNA methylation data. Furthermore, these differentially expressed proteins are enriched for disease phenotype pathways associated with ASD aetiology. These proteins converge on protein–protein interaction networks that regulate cell proliferation and differentiation, metabolism, and inflammation, which demonstrates a link between canonical pathways, biological processes and the ASD phenotype. This review highlights how proteomics can uncover potential molecular mechanisms to explain a link between mitochondrial dysfunction and neurodevelopmental pathology. Autism Spectrum Disorder (ASD) is a lifelong neurodevelopmental condition that is characterized by heterogeneous genetic origins and a poorly understood aetiology . AlthougWe propose that the integration of ASD proteomic data with large scale ASD transcriptomic and DNA methylation (DNAm) data may yield insight into the molecular mechanisms of ASD. Despite the aetiological and genetic complexity of ASD, this underlying heterogeneity is thought to converge on a limited number of biochemical pathways . IntegraThis review integrated data from large-scale quantitative meta-analyses from both transcriptomic and DNAm data, as well as from ASD proteomic datasets published over the past five years. Overall, data were collated from 19 studies and meta-analyses from ASD cohorts published between 2017 and 2021 . The pubhttps://david.ncifcrf.gov/conversion.jsp. Accessed on 8 March 2021)  against the Molecular Signatures Database (MSigDB) , using til 2021) ,36. Eachil 2021) . These ehttps://maayanlab.cloud/Enrichr/. Accessed on 12 April 2021) [https://www.networkanalyst.ca/NetworkAnalyst/home.xhtml. Accessed on 12 April 2021) [The proteomic datasets were examined to identify a subset of differentially expressed proteins that were also implicated in transcriptomic and DNAm data in ASD. After conversion to NCBI Entrez gene IDs, this subset of proteins was annotated with respect to significantly enriched Hallmark canonical pathways, ClinVar disease pathways and transcription factor protein–protein interactions (TF-PPIs) using the Enrichr gene list analysis tool suite (il 2021) ,39. TF-Pil 2021) , using til 2021) . This neAfter collating the data from 19 different studies , we deteThe four additional canonical pathways that were consistently implicated across molecular datasets were the xenobiotic metabolism, p53, adipogenesis and coagulation pathways. Both the xenobiotic metabolism and adipogenesis pathways were implicated in enrichment signatures from brain tissue using all three molecular approaches and were also implicated in studies of younger children using proteomic ,15, tranIt is important to note that four of six proteomic datasets included in this review are blood-based datasets, which means that they provide limited insight into which proteins might be differentially expressed in the brain. Therefore, this review compared the canonical pathways implicated in blood-based proteomic profiles with those enriched in proteomic, transcriptomic and DNAm data from brain tissue in order to highlight canonical pathways that could have functional implications in ASD. The coagulation pathway was the canonical pathway that was most consistently enriched in the ASD proteomic data. This pathway was implicated in five out of six proteomic datasets, including three datasets derived from blood samples in children between 18 months and 15 years ,15,16 asWe also used a second approach to explore how the proteomic data articulated with the transcriptomic and DNAm data associated with ASD. We identified a subset of 121 proteins that were implicated in ASD using all three molecular approaches . Of thesThe subset of 121 proteins that were implicated using all three molecular approaches was also enriched for five ClinVar disease pathways associated with neurological, immunological, and metabolic diseases . NotablyMolecular research of ASD requires a “multi-omics” approach to comprehensively characterise the convergent biological processes associated with its complex genetic architecture. Proteomic, transcriptomic, and epigenomic approaches are well-established as ways to investigate changes in gene regulation, expression, and function in ASD. DNA methylation is widely recognized as an epigenetic regulator of gene expression that contributes to ASD aetiology . TranscrDespite these limitations, proteomics can be an essential component of a multi-omic molecular approach to ASD aetiology. Protein expression studies can be used to investigate biologically relevant protein interaction networks and signalling pathways  and can We found that the canonical pathways involved in metabolism, redox homeostasis, inflammation and proliferation are consistently supported by proteomic, transcriptomic and DNAm data in ASD . This reThe common canonical pathways highlighted above are each established as molecular mechanisms contributing to ASD aetiology. In addition, in vitro and in vivo model systems have demonstrated that these pathways play an essential role in the regulation of neurodevelopment, and converge on a link between mitochondrial dysfunction, neurogenesis, and inflammation. Notably, the functional enrichment signatures of most of the ASD omics datasets included in this review are not representative of early embryogenesis, due to the challenges associated with accessing relevant tissue samples or databanks. Therefore, the datasets included in this review provide limited insight into the mechanisms that might be dysregulated during neurodevelopment. However, this review highlights canonical pathways that are dysregulated in functional enrichment signatures obtained using three different molecular approaches from a wide range of tissue types and individuals of different ages. Therefore, these common pathways may point to common underlying mechanisms in ASD, which has potential relevance in the context of neurodevelopment. While this discussion does not aim to demonstrate a direct link between these molecular mechanisms and disease aetiology, it is useful to consider how the canonical pathways implicated in this review are established as aspects of ASD aetiology, and to review the implications of the dysregulation of these pathways during neurodevelopment.Firstly, a role for mTORC1 signalling is well described in ASD ,58. MutaWe also observed a consistent enrichment of the adipogenesis pathway in ASD molecular data, which implicates the same networks that regulate stem cell metabolism and differentiation. Adipogenesis is regulated by the “opposite interplay” between WNT/B-catenin signalling and peroxisome proliferator-activated receptor gamma (PPARGy), each of which negatively regulates the other . WNT sigOn the other hand, PPARGy acts as a master regulator of lipid metabolism and regulates target genes necessary for differentiation, fatty acid transport, carbohydrate metabolism, and energy homeostasis . MitochoA dysregulation of the signalling between mitochondrial metabolism and neurogenesis has significant implications for neuronal development and function. Firstly, mitochondrial oxidative phosphorylation is one of the primary producers of intracellular reactive oxygen species (ROS) and disruptions to mitochondrial metabolism can lead to oxidative stress . IncreasMany of the canonical pathways highlighted in this review are implicated as mechanisms involved in the response to oxidative stress and gliosis during neurogenesis. Clinical data has reported mTOR signalling as a mechanism that connects mitochondrial disease to gliosis , with boGliosis is characterized by highly reactive microglia and astrocytes, leading to an overproduction of glial-specific fibrillary acidic protein (GFAP) and inflammatory cytokines . This reOur analysis of recent ASD proteomic profiles further supports a role for neuroinflammation in ASD. The complement and coagulation cascades were implicated in three and five out of six ASD proteomic studies respectively, which is supported in independent reviews of ASD proteomic data ,92,93. BCollectively, the canonical pathways highlighted in this review are consistent with known pathways that to contribute to ASD aetiology, as well as pathways that have been implicated in independent reviews of ASD molecular data. Importantly, our review also considers the interactions between these canonical pathways, particularly in the context of neurodevelopment. The latter is often overlooked when the relevance of each canonical pathway is evaluated in isolation. Previous reviews have comprehensively described the role of mTORC1 signalling in protein synthesis, neuronal differentiation, migration, and patterning , but theThe genetic and phenotypic complexity of ASD makes it challenging to identify the molecular mechanisms that contribute to its aetiology. Proteomic approaches can yield novel insight into the dysregulation of biological processes and signalling networks in ASD. Despite the power of proteomics, there are currently limitations to this approach, both in terms of sample acquisition, processing, and analysis, and in accurately capturing variation between different tissue types and developmental stages. In light of these caveats, we integrated recently published ASD proteomic profiles with large-scale meta-analyses of whole-genome transcriptomic and DNAm data to compare the functional enrichment signatures of disparate molecular datasets. Our analysis demonstrates that ASD proteomic, transcriptomic and DNAm data consistently support the dysregulation of mTORC1 signalling, oxidative phosphorylation, adipogenesis and the response to inflammation and oxidative stress. The mTORC1-, WNT- and PPARGy-signalling pathways are each well-established regulators of neurodevelopment that are also implicated in ASD and neurodevelopmental pathology. The mTORC1 signalling pathway regulates oxidative phosphorylation via PPARGC1A, which operates in concert with PPARGy to regulate mitochondrial respiration and fatty acid oxidation. These pathways play an essential role in the tight coupling between stem cell proliferation, differentiation, and mitochondrial metabolism during neurogenesis. If this coupling is disrupted, this can induce metabolic and oxidative stress in NSCs. Both metabolic and oxidative stress are known to disrupt neurogenesis, promote gliosis and induce microglial activation. This proposes a link between mitochondrial dysfunction, oxidative stress, and gliosis, each of which are well-established features of ASD aetiology. Given the central role played by glial networks in neuronal migration and synaptogenesis, gliosis is implicated as a mechanism involved in neuroinflammation and the neuropathophysiology associated with ASD. In summary, this review demonstrates that ASD molecular data converges on canonical pathways involved in mitochondrial function, neurogenesis and neuroinflammation, highlighting how these three key aspects of ASD aetiology interact, and the relevance of these interactions in the context of neurodevelopment.


Background: The systems pharmacology approach is a target prediction model for traditional Chinese medicine and has been used increasingly in recent years. However, the accuracy of this model to other prediction models is yet to be established.Objective: To compare the systems pharmacology modelwithexperimental gene chip technology by using these models to predict targets of a traditional Chinese medicine formulain the treatment of primary liver cancer.Methods: Systems pharmacology and gene chip target predictions were performed for the traditional Chinese medicine formula ZhenzhuXiaojiTang (ZZXJT). A third square alignment was performed with molecular docking.Results: Identification of systems pharmacology accounted for 17% of targets, whilegene chip-predicted outcomes accounted for 19%.Molecular docking showed that the top ten targets (excludingcommon targets) of the system pharmacology model had better binding free energies than the gene chip model using twocommon targets as a benchmark. For both models, the core drugs predictions were more consistent than the core small molecules predictions.Conclusion:In this study, the identified targets of systems pharmacology weredissimilar to those identified by gene chip technology; whereas the core drug and small molecule predictions were similar. Ligustrum (NZZ), Curcumaerhizoma (EZ), Prunella vulgaris (XKC), Hedyotisdiffusa (BH), and Glycyrrhizae radix (GC). According to theory, TCM formulasconsist of sovereign, adjuvant, assistant, and guide drugs. We applied this principle and assignedNZZ and EZasthe sovereign drugs, XKC and BH asthe adjuvant drugs, and GC as the assistant and guide drug. These five drugs made upthe formula for treating primary liver cancer and we named itZhenzhuXiaojiTang (ZZXJT). We initially administered ZZXJT toa murineH22 hepatocarcinomamodel. TheZZXJT group showedH22 cell degeneration and necrosis and the number of blood vessels was reduced. Additionally, many autophagosomes in H22 cells were observed by transmission electron microscopy. Thesefindings revealed that ZZXJT may induce programmed H22 cell death and inhibit primary liver cancer development  has a long history of clinical application in China andforms a complete and independent system of diagnosis and treatment. TCM is efficaciousin the treatment of many diseases, including cancer . In the elopment .HoupuDahuang decoction, HoupuSanwu decoction, and Xiaochengqi decoction include the herbsMangnolia officinalis, Rheum palmatum, and Citrus aurantium but the pharmacological effects and indications of the three formulas differ. HoupuDahuang decoction is mainly used to treat cough and exudative pleurisy, while the HoupuSanwu decoction is primarily used to treat paralytic ileus and Xiao Chengqi decoction is mainly used to treat adherent ileus, chronic gastritis, and intestinal paralysis  . We screUsing “liver cancer” as the keyword, potential liver cancer-related targets were mined from the OMIM databaseHomo sapiens” and confidence level to 0.4. This network was imported to Cytoscape software (version 3.70) and core potential proteins were analysed by adjustingvisualisation parameters according to the “combined degree” values of individual nodes. The functions of the target proteins involved in the biological process were described.To further clarify the mechanism and relevance of the interaction between ZZXJT and hepatocellular carcinoma (HCC), intersecting targets between ZZXJT (drug), and HCC (disease) were obtained by drawing Venn diagramsp < 0.01 and mode of analysisas custom analysis. The following analyses were then carried out: Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO). GO has three subdivisions that were conducted, namely, molecularfunction (MF), biologicalprocess (BP), and cellularcomponent (CC). KEGG pathway analysis yielded the top 20 results and BP, CC, and MF yielded the top 10 results. Results were displayed in bubble plots, created using ImageGPWe selected Metascape database2 culture flask in DMEM medium  supplemented with 5% fetal bovine serum and 1% antibiotics (penicillin/streptomycin). When the cells placed under the culture flask about 70–80%, the original culture medium was discarded, and the culture flask was rinse with PBS gently. Then trypsinization was added in the flask. When the intercellular space of cells enlarged, fresh culture medium was put in the flask to finish trypsin digestion. After centrifugation, the culture medium was extracted carefully on the cell upper layer, and then the cells were made a single cell suspension by the fresh culture medium. The cells were cultured in a constant temperature (37°C) with a volume fraction 5% CO2.The cells were routinely cultured in 25 cm2 cell culture flasks of SMMC-7721 cells were cultured together for 24 h. Three flasks were used as the control group  and the remainder as the drug group, in which 20% ZZXJT-containing serum was added to each flask . RNA was extracted from samples using the TRIzol reagent , according to the manufacturer’s instructions. We measured the A260/A280 ratio using a NanoDrop 2000 spectrophotometer . The required reagents were then left at room temperature for 30 minand the sample and RNA ladder were placed on ice. A 550 µl RNA 6000 Nano gel matrix  was placed in a centrifuge tube and centrifuged at 1500 g for 10 min at room temperature. We then added 1 µl of dye to 65 µl of the gel, shook it well, and centrifugedit at 13000 g for 10 min at room temperature to make the gel-dye mix. Next, we added 9 µl of the gel-dye mix to the GN-genechipClariom™ S Array, human gene chip  by pressingthe gel-dye mix into the gene chip capillary with a piston. 5 µl of RNA 6000 Nano maker was added to the sample well and ladder well. Finally, we added 1 µl of denatured ladder into the Agilent 2,100 instrument  to assess the RNA integrity number (RIN) and the 28S/18S ratio. Data were analysed using the Agilent 2,100 Expert software. Quality control standards were as follows: A260/A280 ratio of 1.7–2.2, RIN≥7.0, and 28S/18S > 0.7.Six 25 cmin vitro transcription. The synthesised cRNAs were then purified and quantified. Single-stranded cDNA was synthesised in the second cycle. cRNA was hydrolyzed by RNase H and single-stranded cDNA remained. After the second cycle of single-stranded cDNA was purified, its concentration was measured. The purified ss-cDNA was transformed into dUTP residual fragments and broken DNA strands, which were covalently linked to biotin, Affymetrix proprietary DNA labelling reagent, to complete cDNA fragmentation and labelling, using. The gene chip was removed and hybridisation and washing were performed.The first and second strands of the cDNA were synthesised. Labelling cRNA was synthesised by The obtained gene chip microarray data were combined with Dataset 1, and a Venn diagram was drawn to determine the intersectinggene chip-predicted targets and HCC targets. The intersecting targets were submitted to the STRING database and the PPI network model was constructed by setting“Organism” to “Homo sapiens” and confidence level to 0.4. This network was imported to Cytoscape software (version 3.70) and core potential proteins were analysed by adjusting visualisation parameters according to the “Combined degree” values of individual nodes. The functions of the target proteins involved in the biological process were described.p < 0.01 and the analysis mode as custom analysis. KEGG pathway analysis and GO analysis at MF, BP, and CC levels, among others, were then performed.The gene chip-predicted targets of ZZXJT and relevant targets of HCC were entered into the Metascape database. Screening criteria were set as follows: statistical difference as −∆∆ct.The total RNA of drug group and control group was extracted with Trizol . Use Prime Script first Stand cDNA Synthesis Kit to reverse transcribe RNA into cDNA according to the instructions. Adopt SYBR pre-mixed ExTaq kit , PCR amplification was conducted at 40 cycles of denaturation at 95°C for 30s, after pre-denaturation treatment at 95°C for 5 min, annealing treatment 58°C for 30 s and extension at 72°C for 30 s. The temperature at the end was 4°C. GAPDH was used as the internal reference gene to detect the relative expression levels of AKT, FOXO1, FOXO3, and FOXO4. The primers of these genes were displayed in The resulting sets of data from Ligustrum (NZZ) andCurcumaerhizoma (EZ) were integrated. The mol2 file corresponding to these active ingredients was downloaded from the TCMSP database for later use.According to the TCM theory of sovereignand adjuvant drugs, the active ingredients of sovereigndrugs in We downloaded the protein crystal structures corresponding to the common target genes of the two models from the RCSB PDB databaset-test method was used.AutoDock Vina (version 1.5.6) was the software used for molecular docking . AccordiA total of 126 chemical components were initially extracted from ZZXJT and 103 active components were identified after ADME screening and normalisation of the UniProt protein database, including poriferosterol, glycyrol, and hederagenin. As shown in HCC-associated targets from the OMIM database and GeneCards database were merged, resulting in a total of 1,067 targets (after duplicates were removed).The PPI network from the systems pharmacology modelhas a total of 145 nodes , which rp < 0.01), pathways related to HCC were PI3K-AKT signalling pathway, apoptosis, and transcriptional misregulation in cancer. The enrichment analysis of ZZXJT in relation to HCC targets was conducted using the Metascape data platform, which resulted in 462 KEGG pathways, 755 GO molecular functions, 5670 GO biological processes, and 426 GO cellular components. Only the top 20 results from each group were considered. In the KEGG pathway analysis , EGF, MAPK1, CCND1, and CYCS. These five targets were most strongly associatedwith this model and can therefore be considered as the core five targets of ZZXJT activityin HCC, as shown in p < 0.01), pathways related to cancer were the PI3K-AKT pathway, FOXO pathway, p53 pathway, and viral carcinogenesis. The enrichment analysis of ZZXJT and HCC-related targets was conducted using the Metascape platform, which resulted in 461 KEGG pathways, 686 GO molecular functions, 5114 GO biological processes, and 484 GOcellular components. Only the top 20 resultsdisplayedby KEGG were considered. In KEGG pathway analysis (p < 0.05). FOXO pathway related genes  mRNA expression gradually upregulated (p < 0.05).In KEGG pathway analysis, pathways related to cancer were the PI3K-AKT pathway and FOXO pathway. As shown in Using Cytoscape software, a total of 25 nodes with 147 edge lines were obtained. Darker nodes and larger circles represent a greater degree of connectivity. Visualisation results showed that the top five targets, namely, CCND1, vascular endothelial growth factor A (VEGFA), MAPK1, EGF, and FOS, had the highest connectivity values. This indicated that these five targets were the most highly correlated in this model and can therefore be considered as the core five targets for ZZXJT action inHCC in this model. The intersecting targets were further analysed using the Metascape data platform, resulting in eight KEGG pathways. The distribution and significance of the proteins involved in each pathway were analysed. BP, CC, and MF analyses were not performed because of the small number of common targets and the lack of statistical significance of less than 20 pathways .A total of 10 small molecules were involved in docking, among which the smallmolecules of the sovereign drug , and the other drugs were numbered EZ1, NZZ1, NZZ2, NZZ3, and NZZ4. The five multidrug common small molecules were numbered A1, A2, A3, B1, and C1.The two models had 25 common targets. One targetprotein structure was removed as it was not searched. The final total of common targets was 24, corresponding to the PDB numbers shown in −1) ranging from-10 to 0. The 10 small molecules were individually docked with the target protein crystals, and 440 docking results were obtained, comprising 240 for the common target gene proteins and 100 each for the systems pharmacology model and the gene chip model, respectively. VMD software was used to visualise the docked conformation stack plots according to the obtained data to visualise the three optimal strips in each group. The action profiles and visualisations of candidate compounds with each target gene protein are shown in p > 0.05) in binding free energy for the common target gene proteins (mean = −7.05)and the systems pharmacology model (mean = −7.04). There was a significant statistical difference (p < 0.05) in the binding free energy between the common target gene proteins (mean = −7.05) and the genechip model (mean = −6.74).The heatmap  displaysIn this study, the targets of HCC were predictedusing a gene chip model and systems pharmacology model. The results of this paper have been analysed and discussed from the perspective of the contrasts and similarities between the targets predicted by thegene chip and systemspharmacology models, respectively.It is evident from the results that the system pharmacology approach neglects the relationship between the activity of a drug’s small molecules and the dosage of the drug. Numerous relationshipsbetween small molecules andtargets may be predicted by the systems pharmacology approach, but this method may lead to errors since it simply uses superimposition based on past research results. This error may be augmented in formulation research, which focuses on the relationship between drug dosage, and drug compatibility. The results of this study showed that thecore targetspredicted by systems pharmacology and gene chip accounted for only 17 and 19% of targets, respectively. The similaritiesintargets identified by the two models differed. The top 10 core targets identified by the system pharmacology model only accounted for 2 common targets. Counting the top 10 targets without including the common targets would increase this to 13 targets. The common targets in the top 10 core targets predicted by the gene chip model accounted for 5 targets. Counting the top 10 targets without including the common targets would increase this to 22 targets. Itcan therefore be concluded that the two models were poorly similar based on the target aspect. In terms of KEGG pathway analysis, the top identicalpathwayof the two models was pathways in cancer and the top fiveincluded the PI3K-AKT signalling pathways. While there were common KEGG pathways, except for the inclusion of pathways in cancer, the two modelsdid not share the same pathways, and so the use of KEGG pathway analysis was limited. In conclusion, the gene chip modelis more accurate in target predictions than the systems pharmacology model . Thismayp > 0.05), while those of genechipsand co-targets were statistically significant (p < 0.05). The mean values were larger than the mean values of the common targets. From the docking data of 10 small molecules docked with two model core targets, it can be derived that, because the numerical value is smaller, and the binding is more stable (Molecular docking was introduced in this comparative study as a reference prediction model. The binding free energy was used as a benchmark and the top 10 targets from the two models (without co-targets) were individually aligned. Results showed that the docking scores of system pharmacology and co-targets were not statistically significant , hencemore data is required to firmly establish the reliability of using systems pharmacology predictions in TCM formula research. This can be addressed in future studies.Since predictions by the systems pharmacology approachare poorly similarto the gene chip technology predictions, we can therefore conclude thatsystems pharmacology has low model confidence and is less reliable. However, the consistency between the core drug predictions versus the core small-molecule predictions was greater in the systems pharmacology model.


We aimed to predict the targets and signal pathways of Xiao-Chai-Hu-Tang (XCHT) in the treatment of colorectal cancer (CRC) based on network pharmacology, just as well as to further analyze its anti-CRC material basis and mechanism of action.We adopted Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and Traditional Chinese Medicine Integrated Database (TCMID) databases to screen the active ingredients and potential targets of XCHT. CRC-related targets were retrieved by analyzing published microarray data (accession number GSE110224) from the Gene Expression Omnibus (GEO) database. The common targets were used to construct the “herb-active ingredient-target” network using the Cytoscape 3.8.0 software. Next, we constructed and analyzed protein-to-protein interaction (PPI) using BisoGenet and CytoNCA plug-in in Cytoscape. We then performed Gene Ontology (GO) functional and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses of target genes using the R package of clusterProfiler. Furthermore, we used the AutoDock Tools software to conduct molecular docking studies on the active ingredients and key targets to verify the network pharmacological analysis results.We identified a total of 71 active XCHT ingredients and 20 potential anti-CRC targets. The network analysis revealed quercetin, stigmasterol, kaempferol, baicalein, and acacetin as potential key compounds, and PTGS2, NR3C2, CA2, and MMP1 as potential key targets. The active ingredients of XCHT interacted with most CRC disease targets. We showed that XCHT’s therapeutic effect was attributed to its synergistic action . Our GO enrichment analysis showed 46 GO entries, including 20 biological processes, 6 cellular components, and 20 molecular functions. We identified 11 KEGG signaling pathways, including the IL-17, TNF, Toll-like receptor, and NF-kappa B signaling pathways. Our results showed that XCHT could play a role in CRC treatment by regulating different signaling pathways. The molecular docking experiment confirmed the correlation between five core compounds  just as well as PTGS2, NR3C2, CA2, and MMP1.In this study, we described the potential active ingredients, possible targets, and key biological pathways responsible for the efficacy of XCHT in CRC treatment, providing a theoretical basis for further research. Colorectal cancer (CRC) is the third most common global malignancy in the world and the fourth leading cause of cancer-related death . In receRadix bupleuri (Chai-Hu), Pinellia ternata (Ban-Xia), Scutellaria baicalensis (Huang-Qin or Chinese skullcap root), Zizyphus jujube (DA-Zao or jujube fruit), Panax ginseng (Ren-Shen or ginseng root), Zingiber officinale (Sheng-Jiang or ginger rhizome), and Prepared Liquorice Root (Zhi-Gan-Cao). Previous studies showed that XCHT could treat tumors by enhancing immune regulation, anti-angiogenesis, and tumor cell apoptosis  Affymetrix Human GenomeU133 Plus 2.0 Array platform. The dataset contained 34 samples, including 17 primary colorectal cancer tissue samples and 17 matched normal tissue samples. The script was run using the Strawberry Perl-5.30.2.1 (Perl) software, and the gene probe names were annotated as gene symbols and grouped. Then, R packages (“limma” and “pheatmap”) were used to analyze GSE110224. Differentially expressed genes (DEGs) between the normal and colorectal cancer tissues were detected on the basis of the criteria adjusted P-value of p < 0.05 and |logic| > 1 and were visualized using a volcano plot.The microarray expression profile dataset GSE110224 was downloaded from the National Center for Biotechnology Information (NCBI) GEO (www.cytoscape.org/). Taking disease, key targets, herbs, active ingredients as nodes, the corresponding relationship above all was constructed with Microsoft Excel, then imported into Cytoscape 3.8.0 to construct the interaction network of XCHT in the treatment of CRC. Then, the Analyze Network plug-in in Cytoscape was used to calculate and sort the topological parameters (degree) of the network.The XCHT action targets and the CRC DEGs were taken as an intersection, and the intersection targets of these two parts were defined as the key targets of XCHT in the CRC treatment. The herb-active ingredient-target interaction network was visualized using the Cytoscape 3.8.0 software  values in the top 30% were selected for subnetwork construction using CytoNCA. Then, genes with the top 30% highest value of betweenness centrality (BC) in the subnetwork were identified as key genes and formed the core network.Go biological function analysis is mainly used to describe the functions of gene targets, including molecular function (MF), cellular components (CC), and biological processes (BP). KEGG enrichment analysis allows the obtention of the signaling pathways enriched by the common targets of XCHT and CRC. GO and KEGG Pathways Enrichments were constructed using the R packages  based on the p-value cutoff = 0.05 and q-value cutoff = 0.05 criteria.www.rcsb.org) and prepared in AutoDockTools-1.5.6 by removing water molecules, adding charge, and parameterizing. The 3D structures of active ingredients were downloaded from the TCMSP Database and prepared in AutoDockTools by computing atomic partial charges and parameterizing. The docking site was set in a cubic box in the center of the initial ligand, and a grid map of each atom type in the box was computed. The AutoDockTools-1.5.6 software was used to simulate the molecular docking of potential targets and components. The best scoring conformer of each compound was analyzed and visualized in AutoDockTools-1.5.6 and PYMOL.Molecular docking was performed using AutodockTools-1.5.6. The atomic coordinates of the crucial target were retrieved from the Protein Data Bank (PDB) , cellular components (CC), and molecular function (MF). 20 potential XCHT targets for CRC were analyzed using R packages. The top 20 terms of BP and MF, just as well as 6 terms of CC are shown in Figs Among Figs The top 11 pathways of the KEGG enrichment analysis are shown in Five active ingredients  and four potential target genes  were selected through network analysis, with high node scores and confidence. Molecular docking studies were conducted to identify the interactions between the active XCHT ingredients and CRC-related potential target genes at the molecular level. The AutoDockTools-1.5.6 software was used for molecular docking. The docking score is shown in In addition, 5-FU, a positive control drug, was the main drug for colorectal cancer chemotherapy. Studies have shown that the anti-tumor effect of 5-FU has a certain correlation with PTGS2, NR3C2, CA2 and MMP1 –22. We oXCHT is derived from the Treatise on Febrile and Miscellaneous Diseases. Previous studies have shown its treatment potential targeting various malignant tumors, including CRC, but the detailed underlying effector mechanism remains unclear.In this study, we analyzed the "herb-active ingredient-target" network and found that quercetin, stigmasterol, kaempferol, baicalein, and acacetin had a high correlation in the network, exhibiting significant anti-colon cancer effects –26. For PTGS2, also known as COX-2, is typically induced by inflammatory stimuli and expressed at high levels in Colorectal Cancer Patients . There aAfter PPI network analysis, CCNB1 and SPP1 were defined as the critical genes. CCNB1, also known as cyclin B1, and high expression of CCNB1 may be associated with poor survival in patients with CRC . The expThrough GO enrichment analysis, it is found that the anti-CRC targets of XCHT mainly involve the regulation of neuroinflammatory response, collagen catabolic process, and embryo implantation to play the role of anti-CRC, which reflects the way of traditional Chinese medicine in the treatment of diseases. The results of the KEGG enrichment analysis suggest that besides the enrichment in cancer-related pathways, such as prostate cancer, bladder cancer. The targets of the main compounds of XCHT are also concentrated in tumors, signal pathways, and other pathways closely related to CRC, such as Prostate cancer, IL-17 signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, NF-kappa B signaling pathway, etc. Relevant studies have shown that IL-17 inhibits CD8+ CTLs and Tregs’ infiltration by signaling in colorectal tumor cells, thereby promoting CRC development . Tumor nAccording to the " Herb-Active ingredient-Target " network results, we selected 5 active ingredients and four key target genes to docking, and the docking results that these compounds could bind stably to the active pocket of the key protein, which indicates that XCHT may treat CRC by inhibiting PTGS2, NR3C2, CA2, MMP1.From the perspective of network pharmacology, this study preliminarily elaborates on the potential active ingredients, possible targets, and critical biological pathways of XCHT in CRC treatment, providing a theoretical basis for further experimental verification. Given the limitations of network pharmacology, the pharmacological mechanism of XCHT in the treatment of CRC is only predicted by mining data, and the effects of the content of various compounds, the interaction between compounds and the metabolic process of herbs in vivo were ignored, which needs to be further verified through pharmacological and clinical studies.In summary, this study demonstrated the potential XCHT effector mechanism in treating CRC based on a network pharmacology method. We revealed that quercetin, stigmasterol, kaempferol, baicalein, and acacetin played a critical role in colorectal cancer by affecting PTGS2, NR3C2, CA2, and MMP1. Besides, our molecular docking studies also showed that the main active XCHT components  could dock well with PTGS2, NR3C2, CA2, and MMP1, providing an important basis for further investigation. However, this study also has certain limitations as pharmacological and clinical research still need to further validate our findings.S1 Fig(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.