Cornus officinalis (CO) is a common traditional Chinese medicine, which has been used in the treatment of DKD and takes beneficial effects in therapy. However, the mechanism of CO in treating DKD is not clear yet. In this study, network pharmacology was applied to illustrate the potential mechanism of CO and the interaction between targets of CO and targets of disease. First, the active ingredients of CO and related targets were screened from the online database. Second, the intersection network between CO and disease was constructed, and protein–protein interaction analysis was done. Third, GO and KEGG analysis were employed to figure out the key targets of CO. Finally, molecular docking was carried out in the software SYBYL to verify the effectiveness of the ingredients and targets selected. According to GO and KEGG analysis, drug metabolism-cytochrome P450, sphingolipid signaling pathway, HIF-1 signaling pathway, TGF-beta signaling pathway, cGMP-PKG signaling pathway, estrogen signaling pathway, and TNF signaling pathway were most closely related to the pathogenesis of DKD. Moreover, NOS3, TNF, ROCK1, PPARG, KDR, and HIF1A were identified as key targets in regulating the occurrence and development of the disease. This study provides evidence to elucidate the mechanism of CO comprehensively and systematically and lays the foundation for further research on CO.Diabetic kidney disease (DKD), one of the most important diabetic complications, is a great clinical challenge. It still lacks proper therapeutic strategies without side effects due to the complex pathological mechanisms.  This rapid growth has had a significant impact on the prevalence rate of DKD in developed countries and has brought heavy medical burden to the society [Diabetes mellitus (DM) is a common clinical disease characterized by hyperglycemia resulting from insulin secretion deficiency or insulin resistance . Accordi society . Therefoα and IL-6 in diabetic patients with albuminuria [β [α [According to the recent guidelines on the DKD treatment, lifestyle measures are generally the first choice of intervention. The treatment of DKD mainly focused on the control of hyperglycemia, blood pressure, and blood lipids. However, many individuals still suffer from DKD progression for the lack of available effective treatment. Thus, further in-depth research on the pathophysiological mechanisms and more novel therapies are urgently required. It is now gradually recognized that the major pathological mechanisms, including kidney inflammation , tubuloiuminuria  and manyuminuria , IL6, annuria [β , p38MAPKnuria [β , and HIFia [β [α . These ria [β [α –16. TradCornus Officinalis Sieb. Et Zucc. (CO) is a widely used traditional Chinese medicine involved in multiple pharmacological activities, such as renal protection, anti-inflammation, antioxidation, and antidiabetes activity [activity . Previouactivity –20. HoweNetwork pharmacology is an emerging approach to drug analysis, which takes multitargets into account rather than a single target . Such a In this paper, network pharmacology was applied to illustrate the action mechanism of CO in treating DKD and the interaction between targets of CO and disease. First, active ingredients and related targets were screened from the online database. Second, the intersection network between CO and disease was constructed, and protein–protein interaction analysis was done. Third, GO and KEGG analysis were employed to figure out the key targets of CO. Finally, molecular docking was carried out to verify the effectiveness of the ingredients and targets selected. The workflow was exhibited in https://tcmspw.com/tcmsp.php) [The compounds of CO were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (msp.php) . Then thmsp.php) . DL is amsp.php) . In thishttps://pubchem.ncbi.nlm.nih.gov/). Related targets of these ingredients were predicted by inputting Canonical SMILES to the Swiss Target Prediction database (https://www.swisstargetprediction.ch/) with a species of “Homo sapiens.” Targets with the probability of 0 were removed from the result.Pubchem ID of filtrated compounds was obtained from TCMSP to find the corresponding Canonical SMILES from Pubchem (https://db.idrblab.net/ttd/), Online Mendelian Inheritance in Man (https://www.omim.org/), DrugBank (https://www.drugbank.ca/), and Disgenet (https://www.disgenet.org/) with “diabetic nephropathy” and “diabetic kidney disease” as search strategies, then the duplicates of targets from the four databases were removed.The DKD targets were collected from the Therapeutic Target Database (https://string-db.org/) to generate a protein–protein interaction network, setting the organism as “Homo sapiens,” and the image was presented by Cytoscape 3.7.2. Finally, we used the tool “network analyzer” to make a topological analysis of disease–drug targets, and the results tables of which were exported to a file. Among the results of a topological analysis, four indexes including degree, betweenness centrality, closeness centrality, and average shortest path length were considered crucial evaluations of the importance of nodes. The number of degrees, betweenness centrality, and closeness centrality has a positive correlation with the importance of nodes, whereas the smaller the average shortest path length, the more vital the node is in the network.The drug targets and DKD-related targets were intersected to obtain a disease-drug target's network, which was presented in a Venn diagram. Subsequently, Cytoscape 3.7.2 was employed to construct the intersection network as well as a compound-target network of CO. Next, we inputted the disease–drug targets into the String database (https://david.ncifcrf.gov/) for gene functional annotation. The results of enrichment analyses of biological process, cellular component, and molecular function were arranged according to “P value” respectively, and the top 10 were selected and merged to form an enrichment GO term diagram. In the same way, the results of the main signaling pathways were obtained by KEGG pathway enrichment analysis, and an enrichment dot bubble diagram was drawn.A gene list of intersection targets was uploaded to David database  database. At the same time, five ingredients were chosen according to the degree, and their molecular structures were obtained in the TCMSP database and then we prepared the proteins by extracting ligand substructures, removing water, and adding hydrogens. Finally, the prepared proteins and molecules were docked in SYBYL-X 2.1.1 software using a semiflexible docking mode to verify the interaction between ligands and receptors . The totThere were 17 active ingredients selected from the TCMSP database according to OB and DL, which were summarized in The 32 overlapping targets and 14 corresponding ingredients were imported to Cytoscape 3.7.2 to obtain a visual network and topological analysis, the result of which was shown in Shown in P < 0.05. The top 10 GO terms of each result were listed together in Gene list was submitted to David database for gene annotation including biological process, cellular component, and molecular function analysis, and obtained 75, 10, and 24 entries, respectively, with P value).” Among these pathways were eight predominant pathways that play important roles in the development of DKD, including Type II diabetes mellitus, drug metabolism—cytochrome P450, sphingolipid, HIF-1, TGF-beta, cGMP-PKG, estrogen, and TNF signaling pathways  is a commonly used traditional Chinese medicine with a sour and astringent taste, which has the effect of tonifying liver and kidney, and invigorating qi and blood. The clinical application showed that CO had beneficial influences on the process of diabetic kidney disease. It could control hyperglycemia and ameliorate renal damage to exert renoprotective function [function , 29. Morfunction .Although CO has been widely used in treating DKD in clinical and gained therapeutic effects, its mechanism is still unclear. In this study, active compounds and targets of CO were selected, and the interaction between drug and disease was analyzed to reveal the mechanism in the treatment of DKD. In the development of pharmaceuticals, herbal products have always played an important role , 32. Theα, and IL-6 in macrophages and T lymphocytes [κB/p65 signaling pathway in macrophage cells [It is well established that the major pathological changes of DKD include tubular inflammation, oxidative stress, autophagy, and, in advanced stages, glomerulosclerosis, atrophy, and interstitial fibrosis. Our studies have shown that the compounds in CO have multiple anti-inflammatory, antioxidant, and antifibrosis properties, protecting cells from damage and thus exerting its renal protective effect. The top five compounds of CO ranked by degree named cornudentanone, mandenol, tetrahydroalstonine, hydroxygenkwanin, and ethyl linolenate. A recent study showed that hydroxygenkwanin (HGK) was involved in anti-inflammation activity by decreasing the secretion of iNOS, NO, TNF-phocytes , 34. It phocytes . Furtherphocytes , liver cphocytes , and nonphocytes . The exige cells . Cornudege cells . Similarge cells . Furtherge cells . Experimα. NOS3, one of the isoforms of nitric oxide (NO), whose abnormalities lead to the onset and development of DKD both in animal models and in the human body [α is a pleiotropic cytokine that acts as a predictive biomarker of renal function in diabetic patients. An increase of TNF, along with its receptors (TNFRs), was observed in patients with DKD [β1 [β1 expression and attenuated endothelial-to-mesenchymal transition (EndMT), which was directly responsible for renal interstitial fibrosis [α, mainly expressed in tubular cells, mediates the adaptive response to hypoxia, thus reaching a balance in terms of oxygen consumption and supply [α is associated with activating fibrotic genes such as α-SMA and fibroblast-specific protein-1 (FSP-1), resulting in an EMT process and eventually leading to renal fibrosis [The disease-drug and PPI network showed the interaction among targets and revealed their significance. According to PPI analysis and references from the biomedical literature database, the following genes were regarded as key targets in treating DKD: NOS3, TNF, ROCK1, PPARG, KDR, and HIF-1man body . There wman body –46. In hman body , 48. Thewith DKD . Moreovewith DKD . Adminiswith DKD . The stu DKD [β1 . ROCK1 k DKD [β1 . In addifibrosis . PPARG wfibrosis . Agonistfibrosis . An incrfibrosis , and it fibrosis . HIF-1α,d supply . Besidesfibrosis , 59. In β and overproduction of reactive oxygen species (ROS) [β, along with its downstream targets, has been long regarded as profibrotic cytokines that directly regulate fibrosis-related pathogenesis such as extracellular matrix (ECM) deposition and epithelial to mesenchymal transition (EMT) [β signaling pathway also has anti-inflammation effects, which might work by activating NF-κB signaling [β pathway reduced ECM deposition and attenuated fibrogenesis [β in the development of DKD. It is widely accepted that cyclic guanosine 5monophosphate (cGMP) could be stimulated by NO and thus activates its downstream targets, especially cGMP-dependent protein kinase (PKG), to exert biological functions [β pathway, reducing TGF-β-induced collagen IV production, ultimately limiting the progression of fibrosis [α could induce and magnify the inflammatory progression by increasing the level of adhesion molecules [α decreased the excretion of urinary albumin, partially indicating the mitigation of insulin resistance [In KEGG analysis, drug metabolism—cytochrome P450, sphingolipid signaling pathway, HIF-1 signaling pathway, TGF-beta signaling pathway, cGMP-PKG signaling pathway, estrogen signaling pathway, and TNF signaling pathway were most closely related to the pathogenesis of DKD. The drug metabolism-cytochrome P450 pathway exerts its biological effects mainly through its derivative products epoxyeicosatrienoic acids (EETs)  and hydres (ROS) . It indies (ROS) . Sphingoes (ROS) . There wes (ROS) , inflammes (ROS) . The stues (ROS) . HIF-1 ses (ROS) . In mostes (ROS) . Howeveres (ROS) . In addies (ROS) . In recees (ROS) . Studieses (ROS) , 73. TGFon (EMT) , 75. In ignaling , 77. Stuogenesis , whereasogenesis , indicatunctions . A studyunctions . A possiunctions . Estrogeunctions . Estrogefibrosis . Studiesfibrosis  and oxidfibrosis . TNF-α colecules  and plasolecules . Furthersistance . Taken tThe clinical application of TCM has a history of thousands of years. Due to the complex chemical composition and the characteristics of multitarget and multipathway, the pharmacological research of TCM is often confronted with unclear composition, vague mechanisms, and disconnection between research basis and clinical application . HoweverBased on network pharmacology analysis, the mechanisms of CO in the treatment of DKD were systematically revealed in a multi-ingredient and multitarget way. A total of 32 overlapping targets between disease and drug were collected, which corresponded to 14 active ingredients selected by the parameters OB and DL. The top three ingredients are cornudentanone, mandenol, and tetrahydroalstonine, and the most important targets of CO on DKD are CYP2C19, PPARG, and UGT2B7. Then we explored the potential targets and pathways involved in treating DKD by GO and KEGG analysis, among which six key targets and seven pivotal pathways were discussed in detail. It indicated that NOS3, TNF, ROCK1, PPARG, KDR, and HIF1A were key targets by which CO regulated the pathogenesis of DKD. Cytochrome P450, sphingolipid, HIF-1, TGF-beta, cGMP-PKG, estrogen, and TNF signaling pathways were considered pivotal pathways that are directly related to DKD. Finally, 5 active components and 10 commonly used targets were docked to reveal the binding affinity between them. It showed that the targets of NOS3, CYP2C19, TNF, ROCK1, and PPARG, and the compounds of mandenol, ethyl linolenate, and cornudentanone have better binding affinity. In conclusion, this study provides evidence to elucidate the mechanism of CO in a comprehensive and systematic way using network pharmacology.


To investigate the mechanism of the Aucklandiae Radix–Amomi Fructus (AR–AF) herb pair in treating gastric cancer (GC) by using network pharmacology and experimental verification. Using the traditional Chinese medicine system pharmacology database and analysis platform (TCMSP), the major active components and their corresponding targets were estimated and screened out. Using Cytoscape 3.7.2 software, a visual network was established using the active components of AR–AF and the targets of GC. Based on STRING online database, the protein interaction network of vital targets was built and analyzed. With the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server, the gene ontology (GO) biological processes and the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways of the target enrichment were performed. AutoDock Vina was used to perform molecular docking and calculate the binding affinity. The mRNA and protein expression levels of the hub targets were analyzed by the Oncomine, GEPIA, HPA databases and TIMER online tool, and the predicted targets were verified by qRT–PCR in vitro. Eremanthin, cynaropicrin, and aceteugenol were identified as vital active compounds, and AKT1, MAPK3, IL6, MAPK1, as well as EGFR were considered as the major targets. These targets exerted therapeutic effects on GC by regulating the cAMP signaling pathway, and PI3K-Akt signaling pathway. Molecular docking revealed that these active compounds and targets showed good binding interactions. The validation in different databases showed that most of the results were consistent with this paper. The experimental results confirmed that eremanthin could inhibit the proliferation of AGS by reducing the mRNA expression of hub targets. As predicted by network pharmacology and validated by the experimental results, AR–AF exerts antitumor effects through multiple components, targets, and pathways, thereby providing novel ideas and clues for the development of preparations and the treatment of GC. The incidence of gastric cancer ranks second among malignant tumors in China, and the mortality rate is third2. Although advances in surgical methods, radiotherapy, chemotherapy and neoadjuvant therapies have significantly improved the survival rate of GC patients, the outlook for patients with advanced GC remains disappointing due to the poor prognosis and limited treatment options3. Thus, traditional Chinese medicine, a complementary and alternative approach, is considered in the treatment of GC.There are approximately 400,000 new gastric cancer cases in China each year, including 350,000 deaths4. Molecular docking and molecular dynamics use the three-dimensional (3D) structure of the target protein to study the specific structural features and interactions between the ligand and the protein. The main purpose of molecular docking is to reveal the main binding pattern of the ligand to the known 3D structure of the receptor. Therefore, this method can identify the correct position and orientation of the ligand in the protein binding pocket and predict the affinity between the ligand and protein5.Recently, electronic technology has proven to be a more effective and rational method for developing drugs. The combination of network pharmacology and molecular docking is considered to be rapid, inexpensive, and effective in drug development6. Studies have shown that TCM has advantages in improving the quality of life of GC patients7. Chronic atrophic gastritis is one of the precancerous lesions of GC, and Aucklandiae Radix–Amomi Fructus (AR–AF) is the main herb pair for the treatment of chronic atrophic gastritis10. AR invigorates the stomach, promoting qi and relieving pain. It is mainly used for the treatment of gastrointestinal diseases such as stagnation of qi in the spleen and stomach, diarrhea, tenesmus, poor food accumulation, vomiting and diarrhea9. Modern pharmacological research shows that AR has the effect of relieving smooth muscle spasms, lowering blood pressure, and exhibiting antibacterial activity. It also has certain anticancer, immune, and anti-inflammatory activities11. Terpenes, phenylpropanoids, lignans, flavonoids, and other compounds contained in the woody. It has a certain inhibitory effect on breast cancer12, liver cancer13, and prostate cancer14. Costanolactone induces apoptosis by activating the mitochondrial pathway in vitro and in vivo and inhibits the viability of BGC-823 cells in a concentration-dependent manner15. The IC50 values of cosanolide were 32.80 and 23.12 μM, respectively. AF has the functions of dispelling dampness and appetizing, warming the spleen and relieving diarrhea, regulating qi and relieving fetuses10. Modern pharmacological studies have shown that the main functional components include volatile substances such as bornyl acetate, camphor, limonene, and camphor, which can protect gastric mucosa, improve gastrointestinal function, relieve pain, relieve diarrhea, and promote the secretion of digestive juice16. The extract of flavonoids in AF at a concentration of 450 mg/kg has the best tumor suppressive effect and has an inhibitory effect on transplanted tumors S180 and H22, with a tumor suppression rate of 54.40%17.It has been verified that traditional Chinese medicine (TCM) has the characteristics of “multiple components, multiple targets, and multiple pathways” in the treatment of various diseases18. The AR-AF herb pair has potential therapeutic effects on GC; however, the associations between AR-AF and GC have not been thoroughly studied and require further research. The present study was designed to delve into the mechanisms of AR-AF on GC using network pharmacology methods as an attempt to be referenced for subsequent pharmacological studies and clinical treatments of GC. The protocol of our study procedures is provided in Fig. The development of network pharmacology provides new methods for elucidating multiple mechanisms of drug action19 (http://lsp.nwu.edu.cn/tcmsp.php). As TCMSP suggests, the molecules with OB ≥ 30%20 and DL ≥ 0.0521 were preserved to display relatively better pharmacological properties and then screened out as candidate compounds for subsequent analysis. The PubChem database was used to obtain the structure data files (SDF) of the above candidate compounds and imported them into the SwissTargetPrediction database, taking the targets with a prediction score greater than 0 as the candidate targets. In addition, the “component-target” Network was constructed.All constituents of the AR-AF herb pair were retrieved from the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP)https://www.disgenet.org/home/)22, GeneCards (https://www.genecards.org/)23, and OMIM (https://omim.org/)24 with “gastric cancer” as the keyword. All the targets were limited to “homo sapiens”.Protein targets associated with GC were provided by DisGeNET (https://string-db.org/cgi/input.pl)25 to delve into protein–protein interactions, and “Organism” was set as “Homo sapiens”. A PPI interactive network was built and then visualized by Cytoscape software 3.7.226. The NetworkAnalyzer tool was used for topology analysis. The three parameters of degree, betweenness centrality (BC), and closeness centrality (CC) were taken as reference standards, the top ten targets were selected as hub targets. The DAVID database (https://david.ncifcrf.gov/)27 was adopted to conduct GO and KEGG enrichment analyses.The intersection of the two (the targets of GC and AR–AF) was taken to obtain the target set of AR-AF for the treatment of GC. Then the target set was imported into STRING (https://www.Oncomine.org)28 was used to compare the differential expression of hub targets in GC tissues and normal gastric tissues. The genetic information of the correlation between mRNA expression and DNA methylation was conducted by cBioPortal tool 29. The hub targets were input into the online tool GEPIA (http://gepia.cancer-pku.cn/index.html)30 to verify their mRNA expression level, pathological stages and overall survival (OS) in TCGA-STAD. Immunohistochemistry (IHC) was performed in the HPA (https://www.proteinatlas.org/)31.Oncomine (32 (https://www.rcsb.org/) was used to search the PDB files of the hub target proteins. The resolution of the crystal structures to achieve and carry out molecular modeling is 2.5–3.0 A. The PDB IDs corresponding to the hub target proteins selected in this study are AKT1 (PDB ID: 1UNQ33), MAPK3 (PDB ID: 6GES34), IL6 (PDB ID: 4O9H To be published), MAPK1 (PDB ID: 6OPH35), EGFR (PDB ID: 7JXP To be published), SRC (PDB ID: 6E6E36), TNF (PDB ID: 7KPA37), CXCL8 (PDB ID: 6WZM38), CASP3 (PDB ID: 6X8K To be published), and APP (PDB ID: 6ITU39). The SDF files of the main compounds with high degree were downloaded from the PubChem database (https://www.ncbi.nlm.nih.gov/pccompound/) as candidate docking medicinal ingredients, and the docking simulation calculation of molecules and target proteins was carried out by the molecular docking software AutoDock Vina 1.5.640. The protein was processed using AutoDock Vina 1.5.6 software by separating the protein, adding nonpolar hydrogen, calculating the Gasteiger charge, assigning the AD4 type, and setting all the flexible bonds of the small molecule ligands to be rotatable. The receptor proteins were set as rigid docking, and a genetic algorithm was selected with the maximum eval number as the medium. Before docking, ChemBioDraw 3D was used to generate 3D chemical structures and minimize energy for all selected ligands. Grid box parameters can be seen in Supplementary Table 41.The PDB database42. In this paper, models with an RMSD ≤ 2 Å were considered.The best docking poses were identified as those showing the smallest root mean square deviation (RMSD) between the predicted conformation and the observed X-ray crystallographic conformation. Models with an RMSD ≤ 4 Å were considered reliable and those with an RMSD ≤ 2 Å were considered accuratehttps://cistrome.shinyapps.io/timer/)43 to investigate the effect of somatic cell copy number (CNA) of the hub targets on different kinds of immune cell infiltration and the association between hub targets and the immune infiltrate level.The efficacy of immune checkpoint inhibitors is affected by many factors. To illuminate the underlying mechanism of the immune microenvironment of GC, we used the TIMER online tool  were added for 24 h. The inhibition rate of the cells was determined using the CCK8 kit to explore whether eremanthin had a toxic effect on the cells. Finally, optical density (OD) measurements were performed at 560 nm with microplate reader.AGS cells were cultured in F12K medium  supplemented with 5% fetal bovine serum (FBS)  and 100 U of penicillin G with 100 μg of streptomycin per ml. Cells were incubated at 37 °C under 5% CO2 in a humidified atmosphere. AGS cells were inoculated into 96-well plates at a cell density of 5 × 10According to the molecular docking results, eremanthin with a good docking effect was selected as the main compound for experimental verification. On the basis of network pharmacology, we selected the top five targets, AKT1, MAPK3, IL6, MAPK1, and EGFR to explore the mechanism. Total RNA was extracted from cultured cells using TRIzol according to the manufacturer’s instructions and then reverse transcribed using the RevertAid RT Reverse Transcription Kit . The primer sequences used for qRT–PCR are listed in Table The experimental results were statistically analyzed by GraphPad Prism software. A single-factor ANOVA was used to compare the differences in experimental data among groups, and P < 0.05 revealed significant difference.106 compounds were screened out in total following the OB ≥ 30% and DL ≥ 0.05 criteria, including 46 AR and 60 AF. Supplementary Table A total of 1135 potential targets of AR and 2148 potential targets of AF were obtained from the database prediction and screening. We established a compound-target network with 667 nodes and 3361 edges , ARG-25 (2.1 Å), LYS-14 (2.0 Å), TYR-18 (1.8 Å), and AGR-86 (1.9 Å) to form hydrogen bonds and with ARG-23 (3.0 Å), Glu-17 (2.7 Å), ILE-19 (1.8 Å), and THR-87 (2.5 Å) to form π–π interactions that resulted in binding to AKT1 (1UNQ)  had genetic mutations in these targets Fig. A. An oveThe mRNA levels of MAPK1, CXCL8, and CASP3 were significantly higher in GC tissues Fig. A. The leThe protein expression of IL-6 and TNF between gastric cancer tissue and normal gastric tissue was the same, indicating that there was no difference. In contrast, the other eight hub targets were expressed to different degrees between normal gastric tissue and gastric cancer tissue, which means that there were significant differences. Compared with normal gastric tissue, the expression levels of MAPK3, MAPK1, EGFR, and SRC were increased in GC tissue, while the expression levels of AKT1, CXCL8, CASP3, and APP were decreased in GC tissue Fig. .Figure 1The CNA of the hub target signature significantly affected the fraction of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells (DCs) in GC Fig. . The assEremanthin was observed to inhibit AGS cell proliferation in a concentration-dependent manner. The 24 h IC50 was 25.07 μmol/L, revealing that eremanthin for the AGS cell line showed a significant reduction in IC50 values over time. The cell viability curves are shown in Fig. Several hub targets, including AKT1, IL6, MAPK1, MAPK3, and EGFR mRNA expression levels were validated by PCR. Eremanthin was found to decrease the mRNA expression levels of AKT1, IL6, MAPK1, MAPK3, and EGFR. The results are depicted in Fig. 6. According to the results achieved here, 106 compounds in AR-AF with 12,844 compound targets were identified, suggesting that AR-AF exerted its pharmacological effects on treating GC via multiple targets. Eremanthin, cynaropicrin, and aceteugenol were identified as the vital main compounds with the highest degrees. Eremanthin exerts anticancer activity on human cervical cancer cells by inhibiting the PI3K/AKT signaling pathway47. Studies have reported that cynaropicrin has a good antitumor effect and has a certain inhibitory effect on colorectal cancer48, melanoma49, and thyroid cancer50. Aceteugenol has a good antioxidant effect51. On the whole, it was speculated that AR-AF is a multicomponent herb pair with multitarget therapeutic effects. Associations between these active compounds and GC should be supposed to be deeply investigated.It has been verified that TCM has the characteristics of “multiple components, multiple targets, and multiple pathways” in the treatment of GCGO and KEGG enrichment analyses showed that AR-AF regulates the proliferation, apoptosis, migration and angiogenesis of tumor cells by regulating signaling pathways, including the cAMP signaling pathway and PI3K-Akt signaling pathway, thereby playing a role in treating GC.53. The positive rate of AKT1 expression in GC tissue is significantly higher than that in adjacent tissues, and it participates in the occurrence and development of GC55. MAPK3 is a serine/threonine protein kinase that can respond to a variety of extracellular stimuli. MAPK3 is related to tumor cell development, differentiation, apoptosis, angiogenesis, invasion and metastasis56. Mitogen-activated protein kinase 1 (MAPK1) has been confirmed as an essential oncogene in the progression of GC, and its level is elevated in GC tissues and cells, which can promote the proliferation, migration, and invasion of GC cells57. MAPK3 is a member of the MAPK family. MAPK is an important signal transmitter in cells that can participate in a variety of biological processes, such as cell proliferation, differentiation, and immune defense, by phosphorylating nuclear transcription factors and related enzymes58. IL6 is a pleomorphic cytokine involved in various biological processes, such as inflammation and tumor development60. Elevated IL-6 can stimulate the excessive activation of STAT3, including angiogenesis and tumor metastasis61. Activation of IL-6/STAT3 inhibits the transcription of miR-520f-3p, which amplifies the excessive activation of STAT3 by targeting GP130. Therefore, targeting this pathway may have the potential to treat GC62. EGFR is associated with gastric mucosal hyperplasia and poor prognosis of GC63. SRC is a serine/threonine kinase that plays an important role in the development of many solid tumors64. SRC is highly expressed in GC66. Activated SRC can promote tumor cell proliferation67. TNF-α is mainly secreted by macrophages and participates in inflammation and the immune response. Its effect on tumors is bidirectional; high concentrations of TNF-α can selectively destroy the tumor vascular system and produce specific T cells that have an antitumor effect, while low concentrations of TNF-α bind to the receptor, activate the NF-κB signaling pathway, promote cell proliferation, inhibit cell apoptosis, and promote tumor progression69. CXCL and CXCR are endogenous ligands of chemokines or members of the receptor family and are closely related to a variety of cancers70. Helicobacter pylori (HP) induces macrophages to release CXCL8, thereby promoting the occurrence and development of GC71. CASP3 is an apoptotic gene, and miR-524-5p participates in the development of GC by regulating CASP3, which may provide new prospects for the diagnosis and treatment of GC72. APP is the main target that we screened for the treatment of GC, but there is no clear antitumor effect report, which is worthy of further study. Molecular docking validated that vital active compounds and major targets showed good binding interactions. It is suggested that the AR-AF herb pair may act on the major targets to play a key role in the treatment of GC.In this paper, AKT1, MAPK3, IL6, MAPK1, EGFR, SRC, TNF, CXCL8, CASP3, and APP were identified as hub targets. AKT is a serine/threonine kinase located downstream of PI3K. Activated AKT1 can induces the proliferation and activation of gastric cancer cellsThe results of immune infiltrates showed that the CNA of the hub target signature significantly affected the fraction of B cells, T cells, neutrophils, macrophages and DCs in GC, which suggested that the hub targets can be used as a potential predictor of gastric cancer immunotherapy.The experimental results in this study confirmed that the active compounds of AR-AF inhibited cell proliferation and induced apoptosis of AGS cells, thus delaying the progression of GC, which will provide a preliminary basis for future clinical trials.In this study, eremanthin, cynaropicrin, and aceteugenol were identified as the vital active compounds, and AKT1, MAPK3, IL6, MAPK1, as well as EGFR were considered as the major targets. Molecular docking revealed that these active compounds and major targets showed good binding interactions. AR-AF regulates the proliferation, apoptosis, migration and angiogenesis of tumor cells by regulating THE cAMP signaling pathway, and PI3K-Akt signaling pathway. Experimental studies provided evidence that AR-AF showed therapeutic effects on GC by regulating related target proteins, inhibiting cell proliferation, and increasing the cell apoptosis rate. This study demonstrated potential pharmacological mechanisms of AR-AF acting on GC, and it can be referenced for the clinical application of AR-AF.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.


PaintOmics 4 has several notable updates that improve and extend analyses. Three pathway databases are now supported: KEGG, Reactome and MapMan, providing more comprehensive pathway knowledge for animals and plants. New metabolite analysis methods fill gaps in traditional pathway-based enrichment methods. The metabolite hub analysis selects compounds with a high number of significant genes in their neighbouring network, suggesting regulation by gene expression changes. The metabolite class activity analysis tests the hypothesis that a metabolic class has a higher-than-expected proportion of significant elements, indicating that these compounds are regulated in the experiment. Finally, PaintOmics 4 includes a regulatory omics module to analyse the contribution of trans-regulatory layers  to regulate pathways. We show the performance of PaintOmics 4 on both mouse and plant data to highlight how these new analysis features provide novel insights into regulatory biology. PaintOmics 4 is available at   Multi-omics approaches have become popular in the study of a wide range of biological domains, with multi-omics datasets being now commonly obtained by individual investigators as well as large consortia . MoreoveThree major strategies may be applied for the interpretation of multi-omics data. Overrepresentation and enrichment analyses are widely used in genomics and transcriptomics analyses . These mAn alternative approach is to use multi-partite networks, where nodes depict molecular entities and edges indicate regulatory or covariance information that can be extracted from the integrative statistical analysis . A varieA third option is to leverage existing biological knowledge represented in pathway maps to project multi-omics data and visualise them within a highly interpretable format. Examples are kaPPA-view 4 , MapMan4Pathway-based visualization methods also have limitations. First, they lack the flexibility to incorporate significant features suggested by the statistical analysis since absent in the available map. Moreover, interpretation is limited by the pathway boundaries decided by curators and by the amount and identity of the molecular features captured in the maps . Some ofNext to KEGG, PaintOmics 4 adds Reactome and MapMan to the list of pathway databases supported by the application  24,28).,28.24,28PaintOmics 3 provided the Regulatory Omics option designed to upload data on features such as microRNA-seq, acting as regulators of gene expression. PaintOmics 4 extends this functionality to accept any type of trans-acting element operating on genes, transcripts or proteins and includes filtering functions to extract meaningful regulatory relationships. In addition to microRNA-seq, transcription factors (TF) and splicing factors (SF), detected by RNA-seq, RNA-binding proteins identified by CLIP-seq, etc., can be analysed with this option. The Regulatory Omics option takes a trans-regulatory-feature data matrix with expression or activity values for regulators in the conditions of the study. The regulator-gene/protein mapping file is provided by the user, together with an optional list of significant deferentially expressed regulators. PaintOmics 4 filtering options include thresholds for positive or negative correlation to select the expected regulatory relationships. Applying these criteria, regulatory features will be mapped to their targeted features and their corresponding pathways. A pathway enrichment score is calculated either based on the number of regulators mapping to each pathway or on the number of regulated genes present in the pathway. Enriched pathways for the Regulatory Omics modality represent biological processes that are significantly impacted by that regulatory layer.0p in the dataset, the proportion of DEG linked to the metabolite is significantly higher than 0p. The hub analysis evaluates genes directly connected in the network (step 1) or genes associated with the metabolite through up to 3 intermediate nodes (steps 2 to 4). P-values are corrected for multiple testing . RET is a tyrosine kinase receptor essential for embryonic development  which were downregulated during differentiation   Figure . KEGG reThe Synthesis of Chorismate pathway was found enriched by the PaintOmics 4 MapMan analysis. Visual inspection of this pathway indicated a general downregulation in the BRL3ox mutant under drought conditions Figure . This paAnother significant MapMan pathway was synthesis of lignin, which shows upregulation in treated BRL3ox plants compared to the WT Figure . This paWhile multi-omics studies have increased in number, scope, sample size and diversity of measured omics modalities, and a wide range of integrative statistical data analysis tools have been proposed, the biological interpretation of these data is still a major challenge. PaintOmics 4 addresses this problem by projecting processed values of multi-omics features onto pathway maps. However, the success of a pathway analysis strategy depends on the amount and identity of the features captured in the database and on their distribution among pathway definitions. Different biological pathway tools focus on different types of organisms, cellular processes, and types of reactions, thereby offering different but complementary views of the biology. Our use cases showed that, by implementing different databases in PaintOmics 4, complementary information about the experiment is gained, and interpretation of the data is improved. To the best of our knowledge, PaintOmics 4 is the only tool to combine these three pathway databases under one analysis.cis and trans regulatory relationships can be directly linked to the mechanistic representation of the biological process captured by the pathway, thereby facilitating the understanding of the multi-layer component of the multi-omics study.Another distinctive aspect of PaintOmics 4 is its versatility in representing virtually any omics modality on pathways. This implies that omics layers that act as direct or indirect regulators of pathway activities, such as epigenetic marks or microRNAs, can be interpreted from the perspective of the pathways that they impact. PaintOmics 4 not only provides enrichment analysis for these layers, but further improves interpretability by showing the relationship between the regulatory feature and regulated gene on the pathway maps. Importantly, and in contrast to other methods that do accept regulatory omics data for pathway views  PaintOmia priori hypotheses of metabolite relevance for the study may dictate the type of metabolomics assay to be run (our STATegra and BLR3ox use cases are examples of this). In such a case, a large fraction of the measured compounds may show significant changes, and this jeopardises any enrichment analysis strategy. To still be able to evaluate if the targeted metabolite types are affected in the experiment, we introduced the Metabolite Class Activity analysis, where we test the hypothesis of the measured metabolite class having a high proportion of compounds with significant changes. Another type of question that multi-omics studies involving metabolomics may pose is the link between the metabolite change and the regulation of the expression of the genes, proteins or other compounds that could modify their metabolite levels. This is relevant, for example, when looking for metabolite biomarkers or targets of metabolic control. This question can be addressed by a gene-metabolite bipartite network analysis  and/or, many metabolites present in the metabolic network are not measured in the metabolomics experiment. Moreover, analysis  or by a analysis . Howeveranalysis . In PainIn summary, PaintOmics 4 is a web server for the multi-layered biological interpretation of multi-omics data that includes a wealth of resources for a comprehensive and interactive analysis. Future developments will address the growing applications of the multi-omics paradigm to assist precision medicine and single-cell analyses.https://paintomics.org/. PaintOmics 4 code and documentation are available at https://github.com/ConesaLab/paintomics4.PaintOmics 4 web server is available at gkac352_Supplemental_FilesClick here for additional data file.


Metabolism of an organism underlies its phenotype, which depends on many factors, such as the genetic makeup, habitat, and stresses to which it is exposed. This is particularly important for the prokaryotes, which undergo significant vertical and horizontal gene transfers. In this study we have used the energy-intensive Aromatic Amino Acid  biosynthesis pathway, in a large number of prokaryotes, as a model system to query the different levels of organization of metabolism in the whole intracellular biochemical network, and to understand how perturbations, such as mutations, affects the metabolic flux through the pathway - in isolation and in the context of other pathways connected to it. Using an agglomerative approach involving complex network analysis and Flux Balance Analyses (FBA), of the Tryptophan, Tyrosine and Phenylalanine and other pathways connected to it, we identify several novel results. Using the reaction network analysis and Flux Balance Analyses of the Tryptophan, Tyrosine and Phenylalanine and the genome-scale reconstructed metabolic pathways, many common hubs between the connected networks and the whole genome network are identified. The results show that the connected pathway network can act as a proxy for the whole genome network in Prokaryotes. This systems level analysis also points towards designing functional smaller synthetic pathways based on the reaction network and Flux Balance Analyses analysis. Biochemical pathways in cells underlie cellular functions, and hence its phenotype. These are regulated by many direct and indirect, and hardwired and transient factors. Evolution of multi-step biochemical pathways in any species depends upon how natural selection shapes the evolution of a set of enzyme-coding genes catalysing the constituent chemical reactions, such that the required end-product is made . HoweverTo study how pathways regulate their function with respect to each other, we chose the highly branched aromatic amino acid  biosynthesis pathway as an example. This pathway is responsible for the production of three aromatic amino acids; Tryptophan, Tyrosine and Phenylalanine–all requiring high energy for their synthesis. The TTP pathway has been studied previously for its role in the production of secondary metabolites , and itsTraditionally, specific pathways such as, the Tryptophan biosynthetic pathway, have been studied in depth both experimentally and theoretically using mathematical models . Howeverin the context of each other is rarely done across species. Hence in order to study the contextual influence of the inter-connected pathways, we use complex network analysis on the TTP pathway reactions network in 29 Bacteria and Archaea. Several FBA and network models have shown how various reactions are connected and used smaller subsystems to improve production or for finding new drug targets. But in these networks, the pathways present in one particular organism were studied, for example the network for disease associated pathway cluster for Huntington disease  , a constraint-based approach to model organisms based on mass-energy balance, and flux limitations  are usedE. coli , L. lact. lactis , S. coelor A3(2) , G. oxyd oxydans , etc. WeThe TTP Pathway: A reconstructed common TTP pathway model for Bacteria and Archaea is shown in The directed reaction networks were constructed for the TTP pathway for 29 organisms , and theE. coli and N. pharaonis TTP networks as examples for the Bacterial and Archaeal organisms. The major difference between the two networks lies in the input region, since in many Archaea the DKFP pathway provides the precursors for the formation of 3-dehydroquinate, whereas in Bacteria it is from the Pentose phosphate pathway and Glycolysis (see S. thermophilus) and 25 (for E. coli), and the number of edges between 30 (S. thermophilus) and 50 (E. coli).The TTP reaction pathway is a linear network  with verysis see . The numThe difference in the number of nodes between the organisms is because there are multiple reactions that provide different paths for the production of the same metabolite. Due to this, the number of connected pathways also differs across the organisms under study. For further analysis, only those reactions and pathways are chosen that are common across all the 29 organisms .Synechocystis sp. PCC 6803) and 4 (E. coli), which further shows how sparsely connected the network is. Based on these properties, the Bacteria and Archaea networks do not differ much. Amongst the Bacteria, the Proteobacteria tend to have higher number of nodes and edges. The Gamma-proteobacteria, E. coli and P. putida has the highest number of nodes, edges and average degree for their TTP pathway network  (ue <.05) . The higp-value <.05). Bacterial networks have significantly higher number of nodes compared to the Archaeal networks in few pathways, but in map01210 and map00680 they are significantly more in Archaea .Bacterial networks show larger variation (std dev range: 1.67–16.18) in node numbers than Archaeal networks (std dev range: 1.34–8.49), and the main contributor to this are the Proteobacteria. Except for TTP, and the other 7 out of 17 connected pathways , the rest of the connected networks differ significantly between Bacteria and Archaea . Furthermore, addition of sparsely connected map00340, map00970 and map00270 decreases the average degree of the combined networks . There are significant differences in the APL between Bacteria and Archaea in map01120, map01210, map01230, map00010, map00680, map00240 and map00030.ionately , except These results indicate that as the metabolic networks expand, due to addition of nodes, the network properties do not change proportionately–they depend on the connection point to the TTP pathway, and the topology of the added pathway. They also differ between and within Bacterial and Archaeal species for the same connected pathway even though the basic TTP pathway do not differ much between the two types of Prokaryotes.Local properties of connected networksconnected networks to TTP pathway network not only changes the global properties of the combined pathway networks, but also the properties of the individual TTP network nodes.The addition of the Degree: The comparison of Degree across the different connected networks show that there is no consistent difference between Bacterial and Archaeal networks. The 3 out of 15 common reactions showed no variations in degree, while 5 out of 15 have significantly different Degree in connected networks (>2 std dev) across different organisms in the connected pathways. Addition of certain pathways such as the map01230 introduces fairly large variations in the Degree for E. coli. : The CC of the TTP pathway reactions also change due to the addition of the connected networks : BC of a node is an important property, as it signifies the central position of the node in the network in terms of transfer of information from all other nodes. There is a general decrease in this network parameter for most connected pathways across all TTP nodes. However, the addition of map01230 and map00970 also significantly alter the BC of the common reactions (z-score >3) across all organisms. R00674 show almost no variation in its BC among the combined pathways of different organisms, since it is at the terminal end of the network. The analysis of the change in BC in TTP nodes showed that, across organisms, most of the variation is observed in the map00030. The addition of this pathway to TTP changes the topology of the combined network in such a manner that it induces changes in the BC in several nodes. The reaction node R01073 in TTP pathway shows considerable increase in BC on addition of map00030 and map00340 due to the addition of pathways that are linear. BC of the terminal reactions, such as R00674 and R02722 in the TTP pathway, increases significantly due to the addition of the connected pathways which occur in very few cases. BC of a node being an important property in terms of transfer of information from all other nodes, our results show that only those pathways change the BC of the TTP nodes, which change the topology of the combined network based on where the added pathway is connected to the TTP network ( network .Combined Connected Network (CCN) is the combined network of the TTP pathway with all the connected pathways added together. It gives an integrated view of the TTP pathway embedded in the metabolic network of the 17 reaction pathways directly connected to it for each of the organisms under study. The question addressed here is how the network properties of the individual nodes of the TTP pathway network change in such a combined network, because of the change in the topology and connectivity patterns in the CCN. Combining the connected pathway networks to TTP added new nodes and edges to the TTP pathway. As will be shown below, some of these additions significantly change the topological properties of the TTP pathway reactions . The number of Edges, though not additive, is also quite large (1479). This indicates, as is seen in etc.).The “hubs” of the network parameters - Degree, Betweenness Centrality and Closeness Centrality - are reaction nodes in the network with the highest value of the respective parameters. We use a cut off for selecting Hubs as the “Nodes in top 20%” of each of the measures. The CCN have few Degree hubs, since these networks are characterized by a large number of Nodes with low degrees, and very small number of Nodes with high degrees, and non-redundant routes for metabolism. There are 66 reaction nodes in the CCN that are found as hubs common to all organisms. Most of them are either Betweenness Centrality or Closeness Centrality hubs . Since tE. coli , in order to understand the influence of different reactions on the working of the TTP pathway. The flux analysis (see Methods section), for the TTP pathway was done on the complete genome scale models of E. coli  and M. b barkeri . The gen−1h−1. The Tryptophan section has the least amount of flux passing through it: .0418 for E. coli, and .0013 for M. barkeri. The Phenylalanine section (.1296 for E. coli and .0041 for M. barkeri), and Tyrosine section (.1018 for E. coli and .0035 for M. barkeri) have higher fluxes (E. coli and M. barkeri is given in Production of aromatic amino acids (TTP) in the cell is a high energy consuming process . This enr fluxes . The lisE. coli and M. Barkeri are differentFluxes through the TTP pathway for E. coli. In M. barkeri, it takes the reaction TRPS1 to produce the same metabolite Tryptophan.1) In both the organisms, fluxes through the Input and Shikimate section are higher than the rest of the sections, because the flux is undivided in these sections. At Chorismate synthase reaction (CHORS), the flux is distributed between the two branches depending on the coefficients of Trp and Phe-Tyr in the biomass equation. All the flux passes through TRPAS2 of the Trp section in E. coli, the Archaea M. barkeri has a lower flux. It may be noted that the growth rate for E. coli is higher than that of M. barkeri, which also shows up in the differences in the media and biomass equations of the two organisms. Out of 1339 unique reactions (as mentioned in E. coli) present in the whole FBA, deletion of 175 reactions was found to be adversely affecting the production of the aromatic amino acids. We reduced the efficiency of the E. coli TTP pathway genes to find the effect of such changes in the production of the aromatic amino acids. A 100% reduction (deletion) of the TTP pathway genes is shown in (2) Compared to Bacteria shown in . Deletioshown in . Suggestshown in .M. barkeri, out of the 815 reactions present in the FBA model, the deletion of 250 reactions shows adverse effect on the production of TTP. Many of these pathways are common between E. coli and M. barkeri, but some of them are unique to either Bacteria or Archaea, as the metabolism of these two organisms are different–in some cases. For example, the pathway for Glycerophospholipid biosynthesis pathway influences TTP production in E. coli, while the Methanofuran B biosynthesis and Methanogenesis pathways influences TTP production in Archaea M. barkeri.In M. barkeri because there are alternative reactions for some reactions, which provide other routes for producing the same metabolite. This indicates that the TTP pathway is more robust in this organism in terms of random gene/reaction deletions. Deletion of genes involved in all reactions, except ANS, ANS2, TRPS1, TRPS2, TRPS3, leads to no flux through the TTP pathway  affects the most, since the flux for the synthesis of Phe and Tyr pass through it. Reduction to 90% of the flux through the reaction has interesting results, for example, when the flux through CHORM is .0072 (.0077 in WT), the flux through the input and Shikimate section is .0084 (.009 in WT), through the Tryptophan section is .0012 (.0013 in WT) and through Phenylalanine is .0039 (.0042 in WT) and Tyrosine is .0033 (.0035 in WT) (3) Reducing single gene efficiency does not significantly affect TTP production in  pathway . The rea pathway . In the 5 in WT) .E. coli or M. barkeri. The Common hubs are the hubs that were identified to be common across all the 29 organisms that were used in the network analysis. The reactions that are common between Network hubs and the essential reactions from FBA mostly belong to Purine and Pyrimidine biosynthesis, Threonine and Lysine biosynthesis and TTP pathway.Deletion of hubs can cause a network to lose its structural and functional integrity . Our results  yielded The Network analysis of the CCN can predict some of the important nodes obtained from FBA analysis. It may be kept in mind that the CCN takes into account only 18 pathways and the reactions present in them, and gives equal weightage to all the reactions and connections. Whereas, in the genome scale FBA, the flux does not flow through all the reactions equally, and hence those reactions and the connections are not reflected in the essential reactions. This indicates that a reduced collection of connected networks can be used to find essential reactions. The list of common hubs across the 29 organisms can be used as a reference list for further studies for finding reactions essential for functioning of TTP pathway and to increase its productivity, since they provide similar result to organism-specific hubs. The list of Network hubs that were shown to be essential by the FBA analysis is given in The important role of “context” has been of long-standing empirical and theoretical interest in biological systems because of their multi-scale and interacting modular structures. Understanding context representations and its interaction with functional outcome in behaviour is an area of immense interest to both neurobiologists and in psychology . In an ido the topological features  of a metabolic pathway vary when it is embedded in the larger network of other connected pathways, and does this variation affect the pathway function? We approached to answer this query from a different perspective using two systems biology methods - topological properties  and metabolic activity  - of the aromatic amino acid biosynthesis (TTP) pathway in many species of Bacteria and Archaea. This pathway consists of quite high energy consuming reactions in the cell. It takes an equivalent of 52, 50 and 74.3 high-energy phosphate bonds for the production of Phenylalanine, Tyrosine and Tryptophan, respectively , which respond to the production of the end products, were mutated to increase the production of the aromatic amino acids. Here we have looked at the TTP pathway individually, as well as, when embedded at the larger metabolic network in Bacteria and Archaea. Such studies require various sources of genetic and biochemical information, such as, stoichiometry, structure of reaction pathways and alternative routes of reactions, along with genes and genomes of different organisms. The results presented highlight the fact that functioning of a biochemical reaction in the cell is intimately connected to its “context” , and the topology of its connectivity to the larger set of reactions - both in the pathway and in the larger biochemical network.The control of the production of aromatic amino acids is traditionally done by means of metabolic engineering in organisms such as utamicum . In thesBased on these analyses we are able to arrive at several conclusions. The Network analysis was undertaken to analyse the changes in network properties of TTP pathway reaction network in isolation and in combination with other pathways directly connected to it through sharing of metabolites as incoming or outgoing reactants. The TTP pathway, which is a predominantly linear and a sparse network, shows a low average degree in all organisms. The nodes in the centre of the network possess high Betweenness and high Closeness Centrality values, while the nodes at the extremities show the opposite characteristics. Out of the many pathways that are connected to the TTP pathway, the 17 pathways that were common among the 29 organisms were considered in this study. The network analysis with all connected pathways in all the organisms showed that - changes in the properties of the 15 TTP reaction network nodes not only depended on the topology of the added network, but also on the nodes to which the pathway was added. The Complete Combined Network (CCN), consisting of the TTP pathway and all the 17 connected networks, showed that the properties of the TTP nodes is not the same when considered in the context of the larger connected network. Nodes with low Degree, Betweenness Centrality or Closeness Centrality, either acquire more connections, or by virtue of the new connections that alter the resulting topology, change their network properties, and become hubs in the CCN. The different Degree, Betweenness Centrality and Closeness Centrality hubs were found for the CCN for all the organisms, and the common hubs were ascertained from them. Hence, analyzing pathways in isolation, and in combination with other networks, gives varying properties to the nodes in the network. How these changes in network topology and parameters of the TTP nodes influence the chemical activity leading to end product formations was analyzed using the Flux Balance Analysis.E. coli and M. barkeri as representatives from the two phyla. The flux through TTP is very low in both the organisms with M. barkeri being lower than E. coli. In silico gene deletion studies of TTP pathway genes showed that fluxes in M. barkeri is more resistant to random attack than E. coli, due to the presence of isozymes. In both the organisms, the deletion or reduction of efficiency of the gene for Phenylalanine and Tyrosine production greatly affected the overall flux though the network. Deletion of reactions in the whole network showed that many pathways such as, Glycolysis, Histidine metabolism, etc, affect the production of these aromatic amino acids in both the groups of organisms. There are also differences in the pathways, affecting TTP between Bacteria and Archaea, due to their differences in metabolism, such as the Methanogenesis pathway.The Flux Balance Analysis was done to study the flow of metabolites through the metabolic reaction network of the TTP pathway, and to compare it between Bacteria and Archaea, by taking A comparison between the network analysis and flux balance analysis of the isolated TTP and CCN of TTP pathways showed that many of the important reaction nodes or “hubs” (in terms of higher network parameters) in the TTP network were common with the essential reactions found by FBA. This points towards identifying a smaller set of reaction steps that can be used for experimental manipulation of the TTP pathway in the cell. This combined Network-FBA approach can be used to predict important reaction steps before attempting any engineering of any pathway for increase or suppression of functionality. Until now, whole genome metabolic networks have been studied by breaking them down into modules using network science . This st29 Archaeal and Bacterial species  were conInput section, where the genes for the enzymes that catalyze the reactions for the conversion of the initial precursors to 3-dehydroquinate is present. In bacteria, the pathway begins from Erythrose-4-phosphate and Phospho-enol-pyruvate. In many archaea, due to the absence of the oxidative Pentose Phosphate Pathway in several archaeal species (Shikimate section of the pathway (Green substrates) which consists of five steps, in which dehydroquinate gets converted to chorismate. The third section is the Tryptophan section (Orange substrates), which converts Chorismate, the end product of Shikimate section to Tryptophan. The last section is the Phenylalanine and Tyrosine section (Purple and Pink substrates), which consist of genes for the enzymes that sequentially convert Chorismate to Phenylalanine and Tyrosine  were selected for the study. The details of forming the reaction networks and the list of organisms is given in E coli whole genome model (M. barkeri whole genome model (E. coli model (iAF1260) consists of 1261 metabolism associated genes, 2382 reactions, and 1668 metabolites. The M. barkeri model (iMG746) consists of 746 metabolism associated genes, 815 unique reactions and 718 metabolites. Both the models were simulated in minimal media. The FBA analysis was carried out using Cobrapy .26.0 (Flux balance analysis was conducted on me model , as a reme model , as a repy .26.0 , Cobra p


Sanjie Zhentong Capsule (SZC) is gradually becoming widely used in the treatment of endometriosis (EMs) and has demonstrated an excellent curative effect in the clinic. However, the active components and mechanisms of Sanjie Zhentong Capsule (SZC) in the treatment of endometriosis (EMs) remain unclear, and further research is needed to explore the effects of Sanjie Zhentong Capsule (SZC).in vitro experiments such as CCK-8, flow cytometry and RT-PCR assays were carried out to preliminarily verify the potential mechanisms.First, a drug target database of Sanjie Zhentong capsule (SZC) was established by consulting the TCMSP database and related literature. An endometriosis (EMs) disease target database was then established by consulting the GeneCards, OMIM and Drug Bank databases. The overlapping genes of SZC and EMs were determined, and protein-protein interactions (PPIs), gene ontology (GO) and Kyoto Gene and Genome Encyclopedia (KEGG) analyses were performed to predict the potential therapeutic mechanisms. Molecular docking was used to observe whether the key active ingredients and targets predicted by network pharmacology had good binding energy. Finally, Through the construction of a pharmacological network, we identified a total of 28 active components in SZC and 52 potential therapeutic targets. According to GO and KEGG enrichment analyses, the effects of SZC treatment may be related to oxidative stress, steroid metabolism, apoptosis and proliferation. We also experimentally confirmed that SZC can regulate the expression of steroid hormone biosynthesis-related genes, inhibit ectopic endometrial stromal cell (EESC) proliferation and oxidative stress, and promote apoptosis.This study explored the potential mechanism of SZC in the treatment of EMs through network pharmacology and experiments, providing a basis for further future research on SZC in the treatment of EMs. EMs lesions are benign in essence, but they are characterized by a propensity for recurrence and strong invasiveness, similar to malignant tumors. The incidence of this disease in women of childbearing age is up to 10% .The treatments for EMs include mainly surgery and medical treatment . LaparosIn traditional Chinese medicine (TCM), EMs is considered a form of blood stasis in women, and the symptoms of EMs are classified as “dysmenorrhea”, “gynecologic abdominal lumps” and “infertility” . The appPanax notoginseng, Fritillariae Thunbergii Bulbus, and Coicis Semen. SZC softens and dissipates knots, eliminates blood stasis and relieves pain (Sanjie Zhentong Capsule (SZC) is a traditional Chinese medicine consisting of the original powder of four natural plants products, Resina Draconis, ves pain –15. In rves pain , and SZCves pain . Our preves pain . Other sves pain  and exhives pain .However, due to the diverse etiology of EMs and the lack of studies on the mechanisms of SZC, the active ingredients and potential mechanisms of SZC in the treatment of EMs should be studied to optimize the use of SZC in clinical drug regimens.The effects of TCMs, especially compound preparations, often involve multiple components, targets, and pathways. Therefore, it is difficult to study the mechanisms of TCMs. In recent years, the emergence of network pharmacology has relieved the limitations of “single component–single target–single pathway “ research in the TCM field to some extent.The concept of network pharmacology was first proposed in 2007 by Hopkins , a pharm22.1To elucidate the possible chemical compositions of SZC, we obtained information on the components of SZC through the TCMSP database and established a SZC compound database. Resina Draconis, one of the main components of SZC, is not included in the TCM-related databases. We obtained its compounds and structural formula from the literature in PubMed and the China National Knowledge (CNKI) database and used PubChem to obtain the 3D structure. For some components, no relevant structural formula was available in PubChem, so we used ChemDraw to draw the structural formulas for subsequent screening of potential active ingredients.2.2http://www.swissadme.ch/) with the following criteria: (1) “high” gastrointestinal (GI) absorption and (2) “Yes” for at least 2 of the 5 DL characteristics.With the established SZC database, we further screened the potential active components of SZC with good metabolism according to the absorption, distribution, metabolism and excretion (ADME) characteristics of the drug. For the compounds that were listed in the TCMSP database, we set a drug oral bioavailability (OB) ≥ 30% and druglikeness (DL) ≥ 0.18 as the screening criteria. Since Resina Draconis is not contained in the various drug databases, we obtained the chemical structures of related compounds by referring to the literature. Potential active ingredients were screened using SwissADME (2.3www.swisstargetprediction.ch/), the species was set to Homo sapiens, and the probability was set to >0.2.To obtain the target genes corresponding to the potential active ingredient of SZC, its 3D structures were imported into SwissTargetPrediction (2.4www.genecards.org/), OMIM database (www.omim.org/) and DrugBank database (go.drugbank.com/) with the keyword “endometriosis”. These repositories are all human gene databases that provide comprehensive information on predicted human genes and are often applied in network pharmacology. After obtaining the targets corresponding to the potential active ingredients of SZC and the EMs, the overlapping targets were obtained by drawing a Venn diagram online (bioinfogp.cnb.csic.es/tools/venny/index.html) and the EMs therapeutic target database was established.To obtain potential therapeutic targets for SZC in the treatment of EMs, we first obtained the relevant targets in EMs from the GeneCards Database (2.5cn.string-db.org/), and the organism was set as Homo sapiens, and the confidence was set to ≥0.4, to obtain a protein–protein interaction (PPI) network. Cytoscape v3.9.0 software was used for topological analysis. In order to further screen the key targets, two parameters, “degree” and “betweenness centrality” (BC), were used in the topological analysis to evaluate PPIs that were positively correlated with the importance of the target in the SZC mechanism for EMs treatment.Protein−protein interaction (PPI) analysis was performed on the therapeutic targets to screen out key therapeutic targets, The therapeutic targets obtained previously were imported into the STRING database (2.6via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. GO analysis enables multifaceted description of the functions of a gene in three aspects: molecular function (MF), biological process (BP) and cellular component (CC). The KEGG database integrates genetic information for systematically analysis and further elucidation of gene functions, covering biological functions, pathways and other aspects. We used R language packages  to perform GO and KEGG pathway enrichment analyses of the previously predicted therapeutic targets. We visualized the results with a bubble chart (p<0.05) to further understand the expression of potential target proteins and the enrichment of the differentially expressed genes in biological functions and pathways.To further elucidate the therapeutic mechanism of SZC in EMs, we explored the biological functions of the drug and the possible pathways involved 2.7https://www.rcsb.org/) and UniProt (www.uniprot.org/): matrix metallopeptidase 9 , Estrogen Receptor 1, cytochrome P450 (CYP) family 19 subfamily A member 1 , androgen receptor , AKT serine/threonine kinase 1 , estrogen receptor 1 , SRC proto-oncogene, non-receptor tyrosine , prostaglandin-endoperoxide synthase 2 , CYP family 3 subfamily A member 4 , epidermal growth factor receptor , and AKR1C3 (1ZQ5). The software program AutoDock 1.5.6 was used to perform molecular docking between target proteins and active ingredients, and the binding energy from the molecular docking experiments was used as a docking score to assess the protein−ligand binding potential. Of the targets and ligands, combinations with a selection value of ≤-5 were considered to have moderate-to-tight binding potential. The smaller the binding energy is, the better the molecules bind.We performed molecular docking of the key active ingredients and therapeutic targets predicted by network pharmacology for preliminary validation. Molecular docking is an important technique for structure-based drug design and screening in which the interactions between ligand molecules and receptor molecules are investigated and their affinities and binding modes are predicted, which are commonly used in drug research. After we obtained the crucial predicted targets of SZC for the treatment of EMs through PPI network analysis, we further verified them through molecular docking. The structures of the following crucial target proteins for SZC therapy were taken from the PDB database , kumatakenin , quercetin , liquiritigenin , and SZC . The obtained reagents were dissolved in DMSO.2.9The protocol used in the present study was approved by the Human Research Ethics Committee of the Obstetrics and Gynecology Hospital, Fudan University, and all participants signed informed consent forms in compliance with the code of ethics set by the World Medical Association (The Declaration of Helsinki). We collected 20 ectopic endometrial specimens from women aged 20-45 years with ovarian EMs at the Obstetrics and Gynecology Hospital of Fudan University. None of these patients received relevant treatment in the three months prior to surgery. All cases were confirmed by pathology. Patient medical records were obtained through face-to-face interviews. The EMs specimens collected in this study were in the cell proliferation stage, and tissue specimens assessed as stage I-II according to the American Society for Reproductive Medicine (ASMR) criteria were collected for research. Approval was granted by the Ethics Committee of the Institutional Review Board Ethics Committee of the Obstetrics and Gynecology Hospital of Fudan University (2019-103).2.102 incubator at 37°C. Immunohistochemistry was performed on isolated cells. When >98% of cells were stained vimentin-positive, and <2% were stained CK-positive, the isolation and purification of EESCs were considered successful for further experiments.Human ectopic endometrial stromal cells (EESCs) were routinely isolated by previously described methods . Endomet2.11The viability of EESCs following treatment with SZC and its components was assessed by the Cell Counting Kit-8 (CCK-8) assay. Cells were seeded in 96-well plates; treated with quercetin , 7,4’-dihydroxyflavone , liquiritigenin , kumatakenin  and SZC ; and incubated at 37°C for 24 hours. After discarding the medium, the cells were incubated with 10 μl of CCK-8 reagent  per well for 30 min at 37°C. The OD value of each well at 450 nm was assessed with a microplate reader (Bio-Rad). Subsequent apoptosis and ROS assays to test the effects of drug treatment concentrations were carried out based on the CCK-8 assay results.2.12An Annexin V-FITC/Propidium Iodide (PI) Apoptosis Detection Kit  was used to evaluate the apoptosis of EESCs treated with SZC and related compounds. Cells were seeded in 6-well plates, treated with quercetin (20 µM), 7,4’-dihydroxyflavone group (20 µM), liquiritigenin group (20 µM), kumatakenin group (20 µM) and SZC group (200 µg/ml), and incubated at 37°C for 24 hours. Then, the cells were washed with cold phosphate-buffered saline (PBS) and digested with a trypsin-EDTA solution. The cells were resuspended in ice-cold PBS for cell counting and diluted with 195 μl of Annexin V-FITC binding buffer. Subsequently, 5 μl of Annexin V-FITC and 10 μl of PI were added, and then the cells were mixed well at room temperature and incubated for 15 minutes without light. The proportion of apoptotic cells was detected with a Beckman Coulter flow cytometer.2.13® reagent . The RNA was reverse-transcribed into cDNA utilizing a Prime Script RT Reagent Kit . RT−PCR was performed using SYBR Premix Ex Taq , and the results were analyzed using a ABI Prism 7900 Fast Sequence Detection System (Thermo Fisher Scientific). Each sample was analyzed in three replicate wells, and the fold change in the transcriptional expression of the above genes was calculated using the 2−ΔΔCt method. The relative mRNA expression levels were normalized to those of ACTB. The primer sequences of these genes are provided in RT−PCR was used to validate the potential therapeutic targets which predicted by network pharmacology. EESCs were seeded in 6-well plates and divided into control group and SZC group, The SZC group was treated with 200 µg/ml SZC and incubated at 37°C for 24 h. Total RNA was extracted form the EESCs using the TRIzol2.14The intracellular reactive oxygen species (ROS) levels were measured using a Reactive Oxygen Species Assay Kit , DCFH-DA was diluted to 10 μM in serum-free medium and added to cells treated with SZC and related compounds (at the same concentration as in apoptosis), which were incubated at 37°C for 20 min and washed with serum-free cell culture medium. After this, the mean fluorescence intensity of the cells was detected with a Beckman Coulter flow cytometer.2.15Data analysis was performed with GraphPad Prism v9.3.1. All results are expressed as the mean ± SD and were assessed using one-way ANOVA test and an unpaired t-test, with p<0.05 indicating statistical significance.33.1Homo sapiens and a probability >0.2, we ultimately obtained 125 potential drug targets and their corresponding 28 potential active ingredients after deduplication. There were 2 targets (7%) of Fritillariae Thunbrgii Bulbus, 3 (11%) of Coicis Semen, 2 (7%) of Panax Notoginseng, 19 (67%) of Resina Draconis, 1 (4%) of Panax Notoginseng and Resina Draconis, and 1 (4%) of Panax Notoginseng and Fritillariae Thunbrgii Bulbus.By consulting the TCMSP database and related literature, we screened out a total of 40 bioactive components of SZC according to their ADME characteristics , biological processes (BP) and cellular components (CC) terms identified in the GO enrichment analysis and the top 20 pathways among the KEGG enrichment pathways were screened and analyzed to explore the possible mechanisms by which SZC exerts therapeutic effects on EMs.3.3.1As shown in 3.3.2We screened the top 20 pathways based on the gene ratios in the KEGG enrichment results, as shown in 3.4Molecular docking was performed with the top 10 potential therapeutic targets based on the degree value and betweenness centrality for 7 active ingredients by AutoDock 1.5.6. The results showed that all had moderate binding potential  levels in the endometrium, a pathological feature of EMs . PreviouThe AR gene, located on the X chromosome, is associated with the hormonal regulation of EMs , 49, andThe estrogen-metabolizing activities of CYP enzymes have been implicated in EMs . CYP19A1The MMPs are a family of zinc-dependent endopeptidases known to regulate the migration, invasion and proliferation of various cell types . As a meStudies have shown that EGFR, a tyrosine kinase receptor, can promote the differentiation and proliferation of EESCs , 63. FurAKT is a serine/threonine kinase implicated in cell cycle control, apoptosis evasion, cell proliferation, and metabolic processes . StudiesPrevious research has suggested that EMs may be an epigenetic disease related to the PTGS2 gene, and genetic polymorphisms in PTGS2 are associated with a high risk of EMs , 70. PTGAccording to the results of GO and KEGG enrichment analyses, the mechanism of SZC in the treatment of EMs involves mainly the following potential biological processes and pathways: oxidative stress, steroid metabolism, apoptosis and proliferation.Estrogen dependence and progesterone resistance are major endocrine features of EMs. Estrogen production and progesterone resistance in endometriotic lesions promote apoptosis and inflammation and reduce immune function , 74. OneROS are known as the main factors in EMs pathophysiology. Cell proliferation, damage and apoptosis are controlled by regulation of the levels of ROS. Studies have shown that the expression level of ROS-related indicators are higher, while the levels of antioxidant are lower, in ectopic endometrium tissue than in normal endometrium tissue . AccordiWhen the levels of ROS are appropriately increased, some natural flavonoids can inhibit AKT activation, promote Beclin-1 expression, increase caspase-3 activity, and then promote apoptosis , 84. In via the PI3K/AKT/mTOR signaling pathway, such as EGFR and VEGF, to promote cell proliferation and inhibit apoptosis (Apoptosis plays a crucial role in maintaining tissue homeostasis, a program that eliminates dysfunctional or excess cells. According to previous studies, inhibition of apoptosis and hyperproliferation of EESCs are the main reasons for the progression of EMs , 86. Baspoptosis . Previoupoptosis , 64. SRC5in vitro experiments. We found that quercetin, kumatakenin, 7,4’-dihydroxyflavonoids, etc., may be the main active components of SZC, and we identified ESR1, AKT1, PTGS2, EGFR, SRC, AR, MMP9, CYP19A1, and CYP3A4 as key therapeutic targets. GO and KEGG pathway enrichment analyses of the therapeutic targets suggested that SZC exerts its therapeutic effects mainly by affecting oxidative stress, steroid metabolism, apoptosis and proliferation. Furthermore, we verified the good binding affinity between predicted potential key components and targets by molecular docking. Finally, our in vitro experiments verified that SZC can play a therapeutic role in EMs by regulating the expression of steroid hormone biosynthesis-related genes, inhibiting EESC proliferation and oxidative stress, and promoting apoptosis.In this study, we explored the potential mechanism of SZC in the treatment of EMs through network pharmacology analysis, molecular docking, and The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Human Research Ethics Committee of the Obstetrics and Gynecology Hospital, Fudan University. The patients/participants provided their written informed consent to participate in this study.LW and YW conceptualized the study. RG and ZY led the experimental design and wrote the manuscript. RG, ZY, and YW performed the experiments. LW and YW corrected the manuscript. All authors contributed to the article and approved the submitted version.


Taraxacum mongolicum (TM) is a widely used herb. Studies have reported that TM exhibits growth-inhibitory and apoptosis-inducing on multiple tumors, including hepatocellular carcinoma (HCC). The active ingredients, targets, and molecular mechanisms of TM against HCC need to be further elucidated.We identified the active ingredients and targets of TM via HERB, PubChem, SwissADME, SwissTargetPrediction, and PharmMapper. We searched HCC targets from GeneCards, Comparative Toxicogenomics Database (CTD), and DisGeNET. Then, the intersection of drug targets and disease targets was uploaded to the STRING database to construct protein-protein interactions (PPI) networking whose topology parameters were analyzed in Cytoscape software to screen hub targets. Next, we used Metascape for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and we employed AutoDock vina, AMBER18 and PyMOL software along with several auxiliary tools for molecular docking and molecular dynamics (MD) simulation. Finally, based on the in silico findings, cellular experiments were conducted to investigate the effect of TM on HSP90AA1 gene expression.in vitro assay showed that TM inhibited the proliferation of HepG2 cells and the expression of HSP90AA1 gene.A total of 228 targets and 35 active ingredients were identified. Twenty two hub targets were selected through PPI networking construction for further investigation. The enrichment analysis showed that protein kinase binding, mitogenactivated protein kinase (MAPK) and phosphatidylinositol 3-kinase (PI3K)/Akt signaling pathways were mainly involved. Molecular docking and MD simulation results supported good interaction between HSP90 protein and Austricin/Quercetin. The This study is the first to use network pharmacology, molecular docking, MD simulation and cellular experiments to elucidate the active ingredients, molecular targets, and key biological pathways responsible for TM anti-HCC, providing a theoretical basis for further research. Liver cancer is the second leading cause of cancer-related deaths globally, remains a global health challenge . The epiTM is a widely used herb with the characteristics of extensive pharmacological action, good security, high nutritional and medical value, which is worthy of in-depth study and development application. The theory of traditional Chinese medicine (TCM) holds that TM is bitter and sweet in taste, neutral in property, and effective in clearing liver fire, which is specifically used to treat liver meridian and the corresponding organs. The clinical usage of TM in liver diseases is instinctive and comes from popular knowledge and experience. The first evidence of its therapeutic use is in the Arabian medicine in the X and XI centuries to treat diseases of the liver and spleen . The hepBased on the theory of system biology, network pharmacology covers high throughput omics data analysis, computer virtual computing, and network database retrieval. It is commonly used in biological systematic network analysis and is a new discipline capable of predicting drug targets from a holistic perspective and increasing the efficiency of drug discovery . NetworkWe applied network pharmacology, molecular docking, MD simulation and cellular experiments to identify active ingredients, molecular targets, and key biological pathways responsible for TM anti-HCC, providing a reference for the follow-up basis research. The detailed workflow is shown in http://herb.ac.cn/) database, a high-throughput experiment- and reference-guided database of TCM. HERB not only contains data from SymMap, the Traditional Chinese Medicine Integrated Database (TCMID), the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and TCM-ID databases, but also inherently adds herbal species and corresponding targets, making it the most comprehensive herbal bioinformatics database now (https://pubchem.ncbi.nlm.nih.gov/) (http://www.swissadme.ch/) platform for pharmacokinetics and drug-likeness assessment (http://www.swisstargetprediction.ch/) platform, PharmMapper Server (http://www.lilab-ecust.cn/pharmmapper/) and HERB database. High levels of the predictive performance of SwissTargetPrediction are based on the similarity principle, through reverse screening, within a sizeable collection of 376342 compounds   were uplsessment . Among tompounds . PharmMaompounds . All obthttps://www.genecards.org/), Comparative Toxicogenomics Database (CTD) (http://ctdbase.org/), and DisGeNET (https://www.disgenet.org/home/) databases, with the search term “Hepatocellular Carcinoma” “Adult Hepatocellular Carcinoma” and species selected as humans. Only targets marked with “M” or “T” symbols were accepted in the retrieval results of CTD. “M” indicates the target may be a biomarker or play a role in the etiology of HCC, and “T” indicates the target may be a therapeutic target of HCC. The intersection of HCC disease targets and the predicted targets of active ingredients was selected in Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/index.html). The intersection targets obtained will be compared with differentially expressed genes in TCGA Liver Cancer Dataset .HCC disease targets were collected from GeneCards  (https:/se (CTD)  (http://DisGeNET  (https:/https://www.string-db.org/) to build a PPI network, with the species limited to “Homo sapiens” and a confidence score ≤ 0.9. Topological parameters of the PPI network were analyzed by the Network Analyzer tool in Cytoscape 3.8.2 software. The higher quantitative values, especially the degree and betweenness centrality in topological parameters, indicate the greater importance of nodes. Targets with the degree and betweenness centrality values above twice the mean were classified as hub targets. Similarly, the interworking network of hub targets and active ingredients was constructed and analyzed by Cytoscape. Active ingredients with the degree greater than the mean were classified as key ingredients.STRING covering 24+ million proteins from 5090 organisms is a database for constructing known and predicted protein-protein interactions (PPI) networks . Cytoscahttps://metascape.org/gp/index.html#/main/step1), one of the effective and efficient tools for functional enrichment analysis, evades confounding data interpretation issues by absorbing most redundancies into representative clusters better than other ways  database before docking. Firstly, ligands, non-protein molecules, and water molecules were removed from the 3D structures by PYMOL2.5.0 software, then hydrogenation, charge addition, and residues repair were conducted in AutoDock Vina, and finally, they were saved in PDBQT format for use as receptors for docking. Likewise, the 3D structures of the active ingredients were converted to PDB format by OpenBabel3.1.1 software, then hydrogenated and energy minimized in AutoDock Vina, and finally, they were transformed into PDBQT format for use as ligands for docking. Molecular docking was carried out at Deepsite (https://www.playmolecule.com/deepsite/) predicted binding sites by AutoDock Vina, and the docking results were visualized by PyMOL.AutoDock Vina, a new generation of docking software from the Molecular Graphics Lab, generally improves the average accuracy of the binding mode predictions and speeds up operations using a terse scoring function compared to widely-used and most cited AutoDock 4 . AutoDoc−1, system pressure 1 atm, integration step 2 fs, and the trajectory data was saved every 10 ps. Furthermore, the binding free energy of proteins and ingredients was calculated using MM/GBSA.The AMBER18 software package was used for MD simulations, and ingredients were parameterized with ANTECHAMBER module and GAUSSIAN09 software . FF14SB The human HCC cell lines HepG2 were purchased from the Chinese Academy of Sciences. HepG2 cells were cultured in DMEM  containing 10% FBS  and 1% penicillin/streptomycin at 37 °C in the cell incubator with a humid atmosphere containing 5% CO2. Cell operations were performed on an ultra-clean workbench at all times.3 cells/well after 80% confluence of the recovered cells. Treatment with various concentrations of TM extract  for 24 h in a 5% CO2 incubator at 37 °C after the cells attached to the wall completely. Then 10ul CCK-8 buffer  was added to each well and incubated for 1 h. A negative control well was set up without seeding cells. Each group was equipped with six auxiliary holes. TM extract (20:1) was purchased from Shaanxi Ivy Bioengineering Co. The absorbance value of each well was measured using a continuous spectrum scanning microplate reader  at a wavelength of 450 nm.The HepG2 cells were seeded in a 96-well plate at a density of 5 × 102 . One microgram of the total RNA was used for cDNA synthesis following the HiScript III RT SuperMix for qPCR  instruction. Measurement of the relative expression of mRNA by RT-qPCR using TB Green® Premix Ex Taq™ II . β-Actin gene was served as the internal reference and the 2Ct−ΔΔ method was adopted for the data analysis. The primer sequences of β-Actin and HSP90AA1 were synthesized by Tsingke Biotechnology Co., Ltd  was applied for total RNA isolation according to the product instruction. The quality of total RNA was accredited by ScanDropt-test was used for comparisons between two groups, the one-way ANOVA was used for comparisons between multiple groups, and the difference was statistically significant at p < 0.05.Data were presented as mean ± standard deviation (SD). Statistical analysis was performed using GraphPad Prism 9.0 software. The two-tailed unpaired Student’s A total of 72 components of TM were searched from HERB database, of which 35 active ingredients were identified by SwissADME . The numA total of 1437 HCC disease targets were collected, of which 778 from GeneCards, 509 from CTD, and 150 from DisGENET. After the duplicates were removed, 1,223 targets were obtained. A total of 228 intersection targets were identified by VENNY2.1.0, as shown in The PPI network was co-constructed by STRING and Cytoscape , which cThe ingredient–hub target network comprising 58 nodes and 210 edges  was consq-value) are shown in Metascape was used to elucidate the biological process (BP), cell composition (CC), and molecular function (MF) annotation of the 22 hub targets. A total of 883 GO terms were obtained, including 807 of BP, 36 of CC, and 40 of MF. The top 20 terms ranked by log  hub targets to dock with the key ingredients using AutoDock Vina, the PDB IDs and structures of proteins are shown in 50) value of TM on HepG2 cells was 6.911 mg/ml .It is observed microscopically that TM-treated HepG2 cells grew slowly and did not adhere firmly to the wall after 24 h of culture compared to the control group . CCK8 as11 mg/ml . Based oTM is a widely used herb. Previous studies have shown its treatment potential targeting various malignant tumors, including HCC, but the detailed underlying effector mechanism remains unclear. This study combined network pharmacology analysis and computer virtual verification to provide a specific anti-HCC mechanism of TM, with the aim of providing a basis for further research and clinical application of TM anti-HCC.In this study, we recognized 22 hub targets and 14 key ingredients by network analysis. Majority of the hub targets belong to MAPK and PI3K/Akt signaling pathways, such as P53, AKT1, MYC, EGFR, MAPK3, VEGFA, EGF, IL-6, RAS. The function of the MAPK signaling pathway is to transmit extracellular signals to the nucleus, thus regulating cell proliferation, differentiation, apoptosis, and autophagy. The MAPK pathway remains activated in approximately 90% of liver cancers, which is known to be the crucial pathway in proliferation and metastasis of carcinoma cells . The MAPin vivo and in vitro by regulating multiple pathways such as PI3K/Akt, MAPK/ERK, and JAK/STAT ethanone, D-Ascorbic acid, Ethyl caffeate, Caffeic acid, Taraxinic acid, Methyl caffeate, Austricin, and Vanillin, exhibit significant anti-HCC effects. For example, Quercetin induces apoptosis in hepatocellular carcinoma cells JAK/STAT . The incJAK/STAT . Linolenuced HCC . The cytuced HCC .The molecular docking results indicate that Quercetin, Sonchuside A, Ethyl caffeate, Taraxinic acid, and Austricin exhibited superior targeting ability to the hub targets. CCND1, PTEN, RAS, and HSP90 may be critical targets of TM anti-HCC based on binding energy. CCND1, PTEN, and HSP90 are proteins in the PI3K/AKT pathway, PTEN and HSP90 in the upstream, while CCND1 belongs to the downstream effector protein. PTEN is an oncogene with protein phosphatase activity and lipid phosphatase activity, and has been correlated with tumor growth, metastasis and chemoresistance . PTEN nein vitro and 18 N-terminal inhibitors of HSP90 have been developed with clinical evaluation (The HSP90 protein consists of an N-terminal domain, an middle domain and a C-terminal domain. The N-terminal domain is a dimeric structure containing an ATP binding site. The middle domain and the C-terminal domain are the binding regions for substrate proteins and helper molecular chaperones. HSP90 are characterized by a distinct ‘Bergerat fold’ in the N-terminal ATP-binding domain . Occupanaluation . In thisin vivo/in vitro experiments. This study provides a holistic view of the potential pharmacological mechanisms for TM anti-HCC, establishing the foundations for further study on the optimization of experimental designs for more reliable results.In summary, the active ingredients of TM and their molecular targets in HCC were successfully unveiled by network pharmacology, molecular docking, MD simulation, and cellular experiments. A total of 14 key ingredients and 22 hub targets were identified. MAPK and PI3K/AKT signaling pathways were found to be potentially primarily responsible for TM anti-HCC. The interaction between Austricin/Quercetin and HSP90 is important for the mechanism of TM anti-HCC. TM may serve as a promising complementary and alternative drug for HCC but needs further 10.7717/peerj.13737/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.13737/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.13737/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.13737/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.13737/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.13737/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.13737/supp-7Supplemental Information 7Click here for additional data file.10.7717/peerj.13737/supp-8Supplemental Information 8Click here for additional data file.10.7717/peerj.13737/supp-9Supplemental Information 9Click here for additional data file.