Background: Diabetes is an increasingly prevalent global disease caused by the impairment in insulin production or insulin function. Diabetes in the long term causes both microvascular and macrovascular complications that may result in retinopathy, nephropathy, neuropathy, peripheral arterial disease, atherosclerotic cardiovascular disease, and cerebrovascular disease. Considerable effort has been expended looking at the numerous genes and pathways to explain the mechanisms leading to diabetes-related complications. Curcumin is a traditional medicine with several properties such as being antioxidant, anti-inflammatory, anti-cancer, and anti-microbial, which may have utility for treating diabetes complications. This study, based on the system biology approach, aimed to investigate the effect of curcumin on critical genes and pathways related to diabetes. Methods: We first searched interactions of curcumin in three different databases, including STITCH, TTD, and DGIdb. Subsequently, we investigated the critical curated protein targets for diabetes on the OMIM and DisGeNET databases. To find important clustering groups (MCODE) and critical hub genes in the network of diseases, we created a PPI network for all proteins obtained for diabetes with the aid of a string database and Cytoscape software. Next, we investigated the possible interactions of curcumin on diabetes-related genes using Venn diagrams. Furthermore, the impact of curcumin on the top scores of modular clusters was analysed. Finally, we conducted biological process and pathway enrichment analysis using Gene Ontology (GO) and KEGG based on the enrichR web server. Results: We acquired 417 genes associated with diabetes, and their constructed PPI network contained 298 nodes and 1651 edges. Next, the analysis of centralities in the PPI network indicated 15 genes with the highest centralities. Additionally, MCODE analysis identified three modular clusters, which highest score cluster (MCODE 1) comprises 19 nodes and 92 edges with 10.22 scores. Screening curcumin interactions in the databases identified 158 protein targets. A Venn diagram of genes related to diabetes and the protein targets of curcumin showed 35 shared proteins, which observed that curcumin could strongly interact with ten of the hub genes. Moreover, we demonstrated that curcumin has the highest interaction with MCODE1 among all MCODs. Several significant biological pathways in KEGG enrichment associated with 35 shared included the AGE-RAGE signaling pathway in diabetic complications, HIF-1 signaling pathway, PI3K-Akt signaling pathway, TNF signaling, and JAK-STAT signaling pathway. The biological processes of GO analysis were involved with the cellular response to cytokine stimulus, the cytokine-mediated signaling pathway, positive regulation of intracellular signal transduction and cytokine production in the inflammatory response. Conclusion: Curcumin targeted several important genes involved in diabetes, supporting the previous research suggesting that it may have utility as a therapeutic agent in diabetes. Diabetes is a prevalent disease that is considered a widespread issue globally. It is known as a set of metabolic illnesses marked by hyperglycemia caused by insulin production or insulin function problems. Based on the International Diabetes Federation (IDF) Atlas, in 2021, it was reported that 27 million people ranging from 20–79 years of age have diabetes. It is expected that this number will increase to 653 million by 2030. Seventy-five percent of people with diabetes are in low- and middle-income countries . DiabeteCurcuma longa, and its features have attracted the attention of many researchers around the world [Curcumin is a traditional medicine derived from he world . Curcumihe world ,15,16,17he world ,20,21,22he world . ConsequIn a recent investigation, virtual screening based on a bioinformatics method has played an important role in the deconvolution and examination of the relationship of medications and diseases ,25,26.With a bioinformatics approach, we aim to investigate the effect of curcumin on critical genes and pathways related to diabetes, which these results might interpret as positive curcumin’s effectiveness in remedying diabetes. In http://stitch.embl.de/ accessed on 20 May 2022), therapeutic target database (TTD) (http://db.idrblab.net/ttd/ accessed on 20 May 2022), and drug-gene interaction database (DGIdb) (https://www.dgidb.org/ accessed on 20 May 2022). STITCH is a platform for the diagnosis of interaction between chemicals and proteins. Those databases are a repository for exploring the relation of protein targets with corresponding drugs, and each of them has different information. Here we used all of them to obtain comprehensive information from curcumin targets. Here for the STITCH database, we considered the high confidence cut-off (0.700), and for the two other databases, we discovered all the targets included, and for all databases, we limited species to Homo sapiens.We first searched interactions of curcumin in three different databases, including the STITCH database (https://www.omim.org/ accessed on 20 May 2022) and the DisGeNET database (https://www.disgenet.org/ accessed on 20 May 2022). DisGeNET is a database that contains a collection of genes associated with specific diseases. Those data are integrated from a variety of sources. We used only curated data that existed in these databases. OMIM is an open-access human database searching for information about genes and their relation to diseases.Subsequently, we investigated the critical curated protein targets for diabetes on two databases, OMIM (https://string-db.org/ accessed on 20 May 2022) with a high confidence score >0.7 and species limited to “Homo sapiens” and then upload the data to Cytoscape (version 3.9.1) to explore Molecular Complex Detection (MCODE) and hub genes  plugin on Cytoscape)) in the PPI network based on diabetes-related genes. STRING is a comprehensive website that possesses physical and functional protein-protein interactions. The MCODE plugin of Cytoscape with specifics containing degree cut-off = 4, node score cut-off = 0.2, haircut off, k-core = 2, and maximum depth = 100 was performed. The hub genes were selected based on two critical centralities, including Degree as topological algorithms and Betweenness Centrality as centralities, based on the shortest paths.To find important clustering groups and critical genes in the network of diseases, we create a PPI network for all the proteins obtained for diabetes. For this purpose, we first construct a PPI network using a STRING database (https://bioinfogp.cnb.csic.es/tools/venny/ accessed on 20 May 2022). In step two, we assay targets of curcumin with the three top scores of the modular clusters analysis of related genes with diabetes in the PPI network. In the last step, we evaluate the effect of curcumin on hub genes obtained from the PPI network based on diabetes-related genes.In step one of investigating the possible effectiveness of curcumin on protein targets of diabetes, we evaluate the shared protein targets between protein targets of curcumin and gene association with diabetes using Venn diagrams (https://maayanlab.cloud/Enrichr/ accessed on 20 May 2022). GO is represented in three complementary levels, including Biological Process, Cellular Component, and Molecular Function. Likewise, KEGG is a repository database that combines genomic, chemical, and systemic functional data. EnrichR is an engine for seeking gene sets beside thousands of annotated gene sets. It integrated information from numerous high-profile projects to supply synthesised data about gene sets [The biological process and pathway enrichment analysis were accomplished using Gene Ontology (GO) and KEGG based on enrichR . p-value: <1.0 × 10−16 with confidence score >0.7. Next, the analysis of centralities in the PPI network indicated 15 genes with the highest centralities (Betweenness and Degree)  . We illuBesides, MCODE analysis identified three modular clusters based on the cut-off score: 5. The highest score cluster (MCODE 1) comprises 19 nodes and 92 edges with 10.22 scores. TIMP1 has presented the seeds of MCODE 1. Furthermore, FGF2 and TNFRSF1A were the seeds of MCODE 2 and 3, respectively .Screening curcumin interactions in the STITCH (high confidence (0.7)) and DGIdb databases identified 158 protein targets. The Venn diagram of genes related to diabetes and the protein targets of curcumin showed 35 shared proteins. Furthermore, the Venn diagram indicated that ten hub genes of diabetes are significant targets of curcumin . The detMoreover, we demonstrate that curcumin has the highest interaction with MCODE1 among all MCODs. It could interact with eleven genes, including TP53, CASP3, STAT3, PPARG, MMP9, HIF1A, MMP2, IL6, VEGFA, FN1, and LEP in MCODE1. Curcumin also has interacted with five genes of MCODE2 and MCODE3 .p-value pathways included the Advanced glycation end (AGE)-Receptor for Advanced Glycation End (RAGE) signaling pathway in diabetic complications, Hypoxia-inducible Factor (HIF)-1 signaling pathway, Phosphatidylinositol 3-kinase-serine/Threonine Protein Kinase (PI3K-Akt) signaling pathway, Tumor Necrosis Factor (TNF) signaling, and Janus Kinase-signal Transducer And Activator Of Transcription (JAK-STAT) signaling pathway . Compared to the two datasets collection, we achieved 35 shared protein targets. Additionally, the biological processes and pathways of 35 shared proteins are suggested that were primarily concerned with inflammatory process and response. The biological pathways that were shown were mainly involved in the AGE-RAGE signaling in diabetic complications, HIF-1, and the PI3K-Akt signaling. By constructing a PPI network from a curated gene collection of diabetes, we reported three top MCODEs and fifteen hub genes. Of fifteen hub genes, ten genes, including TP53, EGFR, STAT3, PPARG, IL6, CASP3, VEGFA, NOS3, PPARA, and FN1, were the targets of curcumin. The results also demonstrated that curcumin is closely associated with three MCODE clusters, especially MCODE1.In our study, the AGE-RAGE signaling pathway in diabetic complications with a set of genes including IL6, NOS3, CASP3, MMP2, IL1B, STAT3, FN1, BCL2, CCL2, ICAM1, and VEGFA is significantly linked with diabetes and curcumin. In this way, curcumin influencing several genes in this pathway might control diabetes. The AGE-RAGE signaling pathway has been extensively researched in a variety of disease conditions, especially diabetes. The AGE-RAGE signaling cascade has been shown to contribute to increased fibrosis, increased RAGE expression, and higher oxidative stresses ,29. By bp-value: 9.71 × 10−16). This pathway, with a set of genes comprising CDKN1A, IL6, NOS2, NOS3, STAT3, BCL2, HMOX1, HIF1A, TLR4, EGFR, and VEGFA, was observed. The activation or inhibition of the HIF-1 signaling pathway indicated involvement with insulin resistance, β-cell dysfunction, and glucose intolerance [Another significant pathway found in enrichment analysis was the HIF1 signaling pathway . This might aid in the decrease in insulin resistance variables and treat gestational diabetes . The PI3Our study discovered fifteen hub genes from a network analysis of diabetic protein interactions that ten genes indicated are the target of curcumin. Three genes, including STAT3, EGFR, and TP53, were the highest targets in diabetes that strongly curcumin interact with them.STAT3  activation has been informed to be implicated in the progression of diabetic insulin resistance by regulating set genes involved in glycolipid metabolism and insulin sensitivity ,48. In t2, which demonstrates the potential rapid biomarker and possible therapeutic targets for individuals with type 2 diabetes [EGFR  is a transmembrane tyrosine kinase receptor belonging to the Erythroblastosis Oncogene B (ErbB) family presented on different mesenchymal, epithelial, and neuronal cells . A studydiabetes . Other pdiabetes . It alsodiabetes . Other rdiabetes . Likewisdiabetes . In our TP53 (Tumor protein p53) is considered the security of the genome with primary activity as a tumour suppressor. It regulates a vast of signaling pathways related to suppressing oncogenic transformation . VariousIn summary, we explored critical diabetes-related genes and pathways that curcumin potentially could interact with. Based on the first obtained result, 35 genes responsible for debates are firmly the target of curcumin. Moreover, our analysis identified 15 vital hub genes involved in the progress and forming of diabetes, and that curcumin strongly regulates 10 genes of these 15 genes. Further study also indicated that curcumin is closely associated with three MCODE clusters, especially MCODE1. According to our results, this interaction and association of curcumin with the related genes with diabetes were in line with ameliorating diabetes based on the literature. We believe that further clinical trials researching will better show the positive effect of curcumin on the therapeutic of diabetes.


Curcumin shows great effects of inhibiting tumor cell proliferation, inducing apoptosis, inhibiting tumor metastasis, and inhibiting angiogenesis on a variety of tumors. However, the biological activity and possible mechanisms of curcumin in the treatment of retinoblastoma have not been fully elucidated. This study explored the potential therapeutic targets and pharmacological mechanisms of curcumin against retinoblastoma based on network pharmacology and molecular docking. R 4.1.0 software was used for Gene Ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of intersection targets. The molecular structures of curcumin and core target proteins were obtained from PubChem and PDB databases, and the two were preprocessed and molecularly docked using AutoDockTools and PyMOL software.  The genes corresponding to curcumin targets were screened from the HERB, PharmMapper, and SwissTargetPrediction databases. Protein-protein interaction (PPI) networks were constructed for the intersecting targets in the STRING database. Cytoscape 3.7.0 was used for network topology analysis and screening of important targets. −1, among which the binding energy of RB1 and CDKN2A to curcumin was less than −5 kJ mol−1 with significant binding activity.  Through network data mining, we obtained 504 curcumin targets and 966 retinoblastoma disease targets, and 44 potential targets for curcumin treatment of retinoblastoma were obtained by mapping. Three core targets were obtained from network topology analysis. 462 biological processes, 21 cellular compositions, and 34 molecular functions were obtained by GO enrichment analysis. KEGG pathway analysis revealed 94 signaling pathways, mainly involving chemical carcinogenesis-receptor activation, chemical carcinogenesis-reactive oxygen species, viral carcinogenesis, Th17 cell differentiation, etc. The molecular docking results indicated that the binding energy of curcumin to the core targets was less than 0 kJ mol Based on molecular docking technology and network pharmacology, we initially revealed that curcumin exerts its therapeutic effects on retinoblastoma with multitarget, multipathway, and multibiological functions, providing a theoretical basis for subsequent studies. Retinoblastoma is the most common intraocular malignancy in children, which accounts for 2.5% to 4% of all childhood cancers and has an incidence of 1/18,000 to 1/14,000 [Curcumin is a yellow phenolic pigment with low toxicity, wide medicinal source and low price extracted from the rhizome of Curcuma longa of the ginger family, such as turmeric and tulip, which has a wide application prospect and value in clinical treatment , 3. CurcNetwork pharmacology is a systems biology approach to study the development of diseases, understand drug-organism interactions, and guide new drug discovery. Therefore, this study aims to investigate the targets and pathways of curcumin and its possible molecular mechanisms of action in the treatment of retinoblastoma by using network pharmacology and molecular docking methods and to provide a reference basis for subsequent studies. The study scheme design is shown in http://herb.ac.cn/), PharmMapper (http://www.lilab-ecust.cn/pharmmapper/), and SwissTargetPrediction (http://www. swisstargetprediction.ch/?) databases. Download microarray data GSE24673, GSE97508, and GSE110811 for retinoblastoma from the GEO database (https://www.ncbi.nlm.nih.gov/) and screen for differentially expressed genes using the GEO2R online tool   . The abohttps://string-db.org/), and TSV format files were saved, which were imported into Cytoscape 3.7.0 software, and the Cyot NCA plug-in was used to further analyze the topology of the regulatory network and screen out the core target genes.Protein-protein interaction (PPI) networks were constructed using the online database STRING  function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using the ClusterProfiler package in https://www.rcsb.org/). Also, download the 3D structure of the curcumin molecule from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and convert the format using Open Babel software. With the help of AutoDockTools, the molecular structure of curcumin was optimized, the structure of core gene protein was dehydrated and hydrogenated, and the molecular and protein preservation format was changed to PDBQT format. Molecular docking verification was performed using Auto Dock Vina software, and finally the obtained results were visualized using PyMOL.The core target genes screened by topological analysis were molecularly docked to curcumin. The protein 3D maps of the core target genes were downloaded from the PDB database  and visualized the top 30 pathways  signaling pathway. Therefore, STAT3 may be a key target of reactive oxygen species in regulating tumor invasion and metastasis.The KEGG enrichment results suggested that the chemical carcinogenesis-reactive oxygen species signaling pathway plays a crucial role, indicating that this signaling pathway is a key link in the treatment of retinoblastoma with curcumin. Reactive oxygen species are produced in the process of oxidative phosphorylation and play an important role in cell and tissue proliferation, differentiation, and apoptosis. Numerous studies have shown that reactive oxygen species are closely associated with the development of retinoblastoma, uveal melanoma, age-related macular degeneration, age-related cataract, dry eye, pterygium, and other ocular-related diseases . CompareIn conclusion, this comprehensive network-based pharmacological analysis suggests a number of testable speculations on the potential molecular mechanisms of curcumin in the treatment of retinoblastoma and predicts RB1, STAT3, and CDKN2A as potential therapeutic targets. This study provides a basis for further experimental studies and ideas to investigate the mechanism of curcumin in the treatment of retinoblastoma, and further studies and validation are needed.


Methods: Components of CS ethanol extract (CSEE) were analyzed and identified using UPLC-Q-Orbirap HRMS. The candidate compounds of CS and its relative targets were extracted from the Traditional Chinese Medicine Systems Pharmacology, Swiss-Target-Prediction, and TargetNet web server. The Therapeutic Target Database, Genecards, Online Mendelian Inheritance in Man, and DisGeNET were searched for NAFLD targets. Binding affinity between potential core components and key targets was established employing molecular docking simulations. After that, free fatty acid (FFA)-induced HepG2 cells were used to further validate part of the network pharmacology results. Results: Six genes, including Caspase 3 (CASP3), phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit α (PIK3CA), epidermal growth factor receptor (EGFR), and amyloid β (A4) precursor protein (APP) were identified as key targets. The mitogen-activated protein kinase (MAPK) signaling pathway was found to associate closely with CS’s effect on NAFLD. Per molecular docking findings, toralactone and quinizarin formed the most stable combinations with hub genes. About 0.1  and 0.2  mg/ml CSEE decreased lipid accumulation in vitro by reversing the up-regulation of CASP3, EGFR, and APP and the down-regulation of PIK3CA. Conclusion: CSEE can significantly reduce intracellular lipid accumulation by modulating the MAPK signaling pathway to decrease CASP3 and EGFR expression.Background: Multiple studies have assessed the role of Cassiae semen (CS) in regulating lipid metabolism. However, the mechanism of action of CS on non-alcoholic fatty liver disease (NAFLD) has seen rare scrutiny. Objective Cassia obtusifolia L. or C. tora L, was first documented in Shennong Bencao Jing. According to the 2020 edition of the Chinese Pharmacopoeia [Cassiae semen (CS), the dried mature seed of the legume acopoeia , CS protacopoeia . CS atteacopoeia  and reduacopoeia . In modeacopoeia . One invacopoeia , suggestacopoeia  and impracopoeia . HoweverLipid metabolism disorders may be the cause of many diseases, including non-alcoholic fatty liver disease (NAFLD), a clinicopathological syndrome diagnosed through macrovesicular fat accumulation in more than 5% of hepatocytes . NAFLD iNetwork pharmacology, a frontier in the field of drug discovery, is valuable for unbiased investigations into the potential target space of natural products , and is,18 column  at 30°C. The mobile phase consisted of 0.1% formic acid in water (A) and acetonitrile (B). The optimized gradient program was as follows: 0–5 min, 95–82% B; 5–10 min, 82% B; 10–15 min, 82–80% B; 15–16 min, 80–78% B; 16–18 min, 78–77% B; 18–27 min, 77–60% B; 27–32 min, 60–50% B; 32–36 min, 50–20% B; 36–40 min, 20–5% B. The flow rate was 0.3 ml/min, and the injected volume was 3 µl.Although water extraction is the traditional extraction method used for CS, recent research has shown ,11 that 2). The parameters were optimized as follows: the capillary temperature and probe heater temperature were, respectively, 320 and 350°C in the negative and positive modes; the spray voltage was 3.5 kV in the positive mode, and 3.0 kV in the negative mode; sheath gas (N2) was 35 l/h, and auxiliary gas  was 10 l/h; the max spray current was 100 A; the s-lens radio frequency level was 50.00%; and the full scan mode range was m/z 100–1500.High-resolution mass spectrometry (HRMS) was performed using an electrospray ion source (ESI), and scanned in a positive ion mode employing a full scan/data dependent secondary scan (Full MS/dd-MShttps://tcmsp.com/tcmsp.php). The thresholds for the filtering of the obtained active compounds were set as Oral Bioavailability (OB) ≥ 30 [https://pubchem.ncbi.nlm.nih.gov/) using InChlKey. Next, active compounds-related targets were screened with a standard ‘prob>0’ in the TargetNet (http://targetnet.scbdd.com/) [http://www.swisstargetprediction.ch/) server. The human gene symbol corresponding to the name of any protein target was transferred by using the Uniprot database (https://www.uniprot.org/) [The active components of CS were extracted from the Traditional Chinese Medicine Database and Analysis Platform (TCMSP)  (https:/OB) ≥ 30  and Drugdd.com/)  online ddd.com/)  in the Sot.org/) .http://db.idrblab.net/ttd/) [https://www.genecards.org), Online Mendelian Inheritance in Man (OMIM) (http://www.omim.org) [http://www.disgenet.org/) were used to screen for NAFLD-related targets. Targets with score >20 in Genecards [https://bioinfogp.cnb.csic.es/tools/venny/) and were regarded as candidate genes.The Therapeutic Target Database (TTD) (et/ttd/) , Genecarmim.org) , and Disenecards  and scorenecards  were selCytoscape3.7.2 was used to construct and visualize the drug-active compound-target-disease network. The nodes in the network diagram, represent different targets and components. Cytoscape was subsequently employed to calculate the average shortest path length, betweenness centrality, closeness centrality, neighborhood connectivity, and the degree between each node using its algorithm. The calculated degree is same as the number of directed edges.https://www.string-db.org/) to construct the protein–protein interaction network (PPI). In the STRING database, the minimum required interaction score necessary to screen with medium confidence (0.4) in the human species and the active interaction sources was dependent on texting, experiments, database, co-expression, neighborhood, gene fusion, and co-occurrence. The visual network diagram was created using the Cytoscape software [Candidate genes were entered into the Retrieval of Interacting Genes online analysis  softwaresoftware .P-value ≤ 0.05 was considered to indicate a biological signal pathway with a unified scientific meaning. In the analytical process, the nodes represent terms that are linked based on a predefined kappa score level. The initial kappa score level threshold can be adjusted from 0 to 1 to restrict network connectivity in a customized way [The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of candidate genes was performed using the Cytoscape software plugin tool Clue GO , where aized way . And thehttp://www.rcsb.org/), the best protein structure was selected and saved in pdb format. The gained protein structure and active ingredients were then imported into PYMOL for pretreatment such as with water or ligand removal. After pretreatment, they were docked using the AutoDuck Vina software. The main connection mode between small molecules and large proteins was established by introducing the PDB file into LIGPLOT software.The energy required for core compounds to bind with key targets was evaluated using molecular docking simulations. The top 6 targets in the PPI network according to the degree value and the top 7 components of CS were selected for molecular docking. After downloading the MOL2 files of the seven main active ingredients from the TCMSP and entering the target into the Protein Data Bank (PDB) , and was identified by Professor Jinlin Guo, School of Pharmacy, Chengdu University of TCM. The powder of Cassiae semens (10 g) was extracted using 95% ethanol at 90°C for 3 h under a reflux condensation device. The acquired product was filtered, and the filtrate was concentrated for 40 min and lyophilized using a freeze-drying system [2.HepG2 cells were cultured in 90% high sugar Dulbecco’s Modified Eagle Medium , 10% fetal bovine serum , and 1% antibiotic , and maintained at 37°C under 5% CO3 per well) at logarithmic phase were seeded in 96-well plates at 100 µl per well and treated with different concentrations of CSEE and/or FFA for 24 h. After the treatment procedure, the liquid in the wells was removed, and 100 µl of complete medium mixed with 10 µl of CCK-8 solution was introduced into each well. The mixture in the plate was incubated at 37°C for 2 h [HepG2 cell viability was assessed using a Cell Counting Kit-8 . HepG2 cells , fixed in 4% paraformaldehyde for 30 min, stained with the ORO working solution for 30 min at 37°C, and then rinsed three times with PBS. The cells were then subjected to the qualitative analysis of lipid using a microscope. The quantification analysis of lipid accumulation was performed based on OD values. A 0.5% storage solution of ORO was prepared by mixing 0.25 g of dry oil red power with 50 ml of isopropanol and then filtering the mixture using a 0.22 µm membrane. A working solution of ORO was concocted by blending the storage solution with deionized water at a ratio of 3:2.−ΔΔCT method was used to determine relative fold changes in gene expression. Designed primers sequences are shown presented in Supplementary Table S1.The total ribonucleic acid (RNA) from each group of HepG2 cells was extracted utilizing the Total RNA Extraction Reagent , and cDNA was synthesized using HiScript II RT SuperMix for qPCR . The SYBR green qPCR Master Mix  was employed for the quantification of the gene expression levels. The succinate dehydrogenase complex flavoprotein subunit A (SDHA) acted as an internal reference, and the 2in vitro experiments. There were only independent single variables (concentration) in the whole experiment. All data were presented as the mean ± S.D. P<0.05 was considered statistically significant.Data were analyzed using GraphPad Prism 8.3.0 . One-way ANOVA with Tukey’s multiple comparisons test was used to compare the effects of different concentrations of CSEE on cells in the + ions. , and amyloidβ (A4) precursor protein (APP) played fundamental role in the treatment of NAFLD. The corresponding degree values are displayed in the Supplementary Table S7.The 64 candidate genes obtained were integrated into the string database, after which the medium confidence > 0.4 and ‘Homo sapiens’ were selected. A total of 62 nodes and 249 edges were displayed using the Cytoscape software visualization, meaning that were 249 interactions in 62 candidate genes . Node siP-value < 0.05), which are displayed in P-value of each term. Of the pathways, the MAPK signaling pathway was the most prevalent (P<0.0005) in each cluster, an indication that it was the most closely associated pathway with NAFLD and, hence, requires further evaluations.The KEGG pathway enrichment analysis identified 37 meaningful terms , Quinizarin-EGFR docking , Aloe emodin-EGFR docking , Rubrofusarin-ESR1 docking , and Rubrofusarin-EGFR docking  are shown in −1 are provided in the Supplementary Figure S1). Of the combinations, the binding of EGFR with Toralactone (MOL002281) and with Quinizarin (MOL006489) produced the tightest binding energies.The top 6 targets in the PPI network and the top 7 compounds of CS were docked. In the output of molecular docking, lower binding energy indicated that the binding force between the active ingredient and the target protein was strong. The binding energies are shown in P>0.05, in vitro model of NAFLD. The assay (P>0.05).The cytotoxicity of CSEE and 1 mM FFA on HepG2 cells was evaluated employing the CCK-8 assay. Based on previously published results  and the he assay B establiP<0.01). In contrast, lipid accumulation diminished in a concentration-dependent manner in the presence of the various concentrations of CSEE, the OD values were noted as 0.405 ± 0.029, 0.459 ± 0.069, 0.509 ± 0.037, 0.539 ± 0.024, and 0.613 ± 0.064 for the 0.1 (P<0.01 vs. FFA), 0.2 (P<0.05 vs. FFA), 0.4, 0.8, and 1.6 mg/ml CSEE concentrations, respectively. Remarkably, lipid droplets and lipid accumulation both substantially reduced after treatment with 0.1 mg/ml of CSEE compared with the FFA model group (P<0.01) , the 1 mM FFA model group (0.579 ± 0.031) without CSEE treatment had significantly increased lipid droplets ((P<0.01) C,D.P<0.0001), EGFR , and APP  gene expressions and down-regulated PIK3CA  gene expression in the model group compared with the control group. CSEE at a concentration of 0.1–0.2 mg/ml markedly down-regulated the gene expressions of CASP3, EGFR, and APP and up-regulated that of PIK3CA in the in vitro model of NAFLD.  reversed the FFA-induced up-regulation of CASP3, EGFR, and APP genes and down-regulation of PIK3CA, indicating that CASP3, EGFR, PIK3CA, and APP are targets of CSEE and mediate its lipid-lowering properties. Of note, CASP3 was the most frequently identified molecule by the PPI analysis, pointing to its potentially critical role in NAFLD. CASP3, an intracellular cysteine protease, mediates apoptosis and inflammation through the processing and activation of pro-inflammatory cytokines. As one of the mediators of apoptosis, CASP3 can be regulated by multiple factors. Previous studies have shown that it modulates hepatocyte responses to toxic levels of lipids . FFA-ovepocampus . We suggThe high binding affinity between EGFR and the active ingredients of CS, as determined using molecular docking analyses, shows that EGFR is the most likely mediator of the therapeutic influence of CS on NAFLD. As a receptor tyrosine kinase of EGFR signaling, CS drives various cellular responses. Epidermal growth factor receptor signaling has been shown to have critical roles in all stages of liver responses to injury, from early inflammation and hepatocellular proliferation to fibrogenesis and neoplastic transformation . PreviouAccording to our KEGG pathway analyses using Cytoscape, the MAPK signaling pathway was the predominantly enriched pathway. MAPK has three main families: extracellular signal-regulated kinase, Jun amino terminal kinases, and stress-activated protein kinases. Inhibiting P38 and Jun N-terminal kinase, as demonstrated by one report, decreases hepatocyte steatosis and apoptosis in a rat model of NAFLD . The MAPin vitro experiments to show that treatment of lipid metabolism with low CSEE concentrations significantly reduces lipid accumulation by modulating the MAPK signaling pathway to decrease CASP3 and EGFR expressions. This research provides theoretical and experimental bases for further investigations on the efficacy and mechanism of action of CS in treating NAFLD. However, these findings may have some limitations because the databases used conceivably do not include all known or unknown targets and protein–protein interactions associated with CS. Expanding these databases further could lead to a greater understanding of the effects of Cassiae semen on NAFLD, which would enable more comparative scrutiny of the composition of CS extracted using ethanol and alcohol in the next experiment so as to explore the effect of CS more fully on lipid metabolism.In summary, the present study made use of network pharmacology, molecular docking, and Click here for additional data file.


Aim of the Study. This study aims to investigate HD's possible role in MG treatment. Materials and Methods. The TCMSP database was used to identify the active chemicals and their targets. The GeneCards, DisGeNET, and OMIM databases were used to search for MG-related targets. The STRING database was employed in order to identify the common PPI network targets. We next utilised Cytoscape 3.8.2 for target identification and the DAVID database for gene ontology (GO) function analysis as well as Encyclopaedia of Genomes (KEGG) pathway enrichment analysis on the selected targets. The AutoDock Vina software was used to test the affinity of essential components with the hub gene before concluding that the primary targets were corrected through molecular docking. Results. 41 active compounds were screened from HD, and the number of putative-identified target genes screened from HD was 112. There were 21 target genes that overlapped with the targets of MG, which were postulated to be potential treatment targets. Through further analysis, the results showed that the active compounds from HD  may achieve the purpose of treating MG by acting on some core targets and related pathways . Molecular docking findings demonstrated that these active molecules have a near-perfect ability to attach to the primary targets. Conclusion. Through network pharmacology, the findings in this study provide light on the coordinated action of several HD formula components, targets, and pathways. It provided a theoretical basis for further study of HD pharmacological action.The herbal pairing of Huangqi and Dangshen (HD) is traditional Chinese herbal medicine and has been widely used in China, especially to treat myasthenia gravis (MG). However, the mechanism of HD on MG is unclear.  Antibodies attached to components of the neuromuscular junction, such as the acetylcholine receptor (AChR), are the primary cause of myasthenia gravis (MG). More than 700,000 individuals throughout the globe are thought to be affected by the condition, which has an incidence of 0.3 to 2.8 per 100,000 [Astragalus membranaceus (Fisch.) Bge. var. mongholicus (Bge.) Hsiao and has been found to be rich in flavonoids, saponins, polysaccharides, and amino acids. Dangshen (DS) is the root of Codonopsis pilosula (Franch.) Nannf and is composed of several bioactive phytochemicals including lithospermic acid, protocatechuic acid, danshensu, caffeic acid, protocatechualdehyde, rosmarinic acid, salvianolic acid A-C, cryptotanshinone, dihydrotanshinone I, tanshinone IIa, and tanshinone I. However, it is not known how HD affects MG. HD is a very important supplement of TCM treasure house, both of which have the effect of invigorating the spleen and replenishing Qi. It can be used for diseases such as myasthenia gravis, heart failure, and hypotension caused by Qi deficiency. Qi deficiency syndrome is the main pathogenesis of myasthenia gravis, which runs through the whole process of myasthenia gravis. Therefore, invigorating Qi is the basic principle of treatment for MG with Qi deficiency syndrome. Clinically, HD has been widely used for the treatment of myasthenia gravis [α. Therefore, DS extract can enhance the hematopoietic and immune function of mice [Traditional Chinese medicine (TCM) has been widely studied because of its stable curative effect, no drug resistance, and fewer toxic and side effects. The herbal pairing of Huangqi and Dangshen (HD) is traditional Chinese herbal medicine and has been widely used in China, especially to treat myasthenia gravis (MG). Huangqi (HQ) is the root of a gravis . Studiesa gravis , 6. DS e of mice . Because of mice . HoweverNetwork pharmacology is a complex system that describes the interaction of “drug (compound)-target (gene)-disease.” It analyzes the network of biological systems through bioinformatics databases, combined with systems biology, bioinformatics, pharmacological analysis, computational biology, and other multidisciplinary theories. It is a new method to analyze the target and mechanism of drug intervention in diseases from multiple perspectives . When ithttps://tcmspw.com/tcmsp.php) [Using the keywords “Astragalus” and “Codonopsis,” the TCMSP (msp.php)  databasehttps://www.genecards.org/) [https://www.disgenet.org/) [https://www.omim.org/) [(1). Create a database of TCM active components using the TCMSP database to anticipate the target proteins of the active substances. (2). Search for myasthenia gravis (MG)-related genes in GeneCards (ds.org/) , DisGeNeet.org/) , and theim.org/)  using thhttps://string-db.org) [The targets of each active component of TCM and the disease target were intersected. There were only a limited number of molecules that intersected in the collection; thus, the target molecules were uploaded to the STRING database (-db.org) . Cytoscahttps://david.ncifcrf.gov) [P < 0.05). Key targets' signalling pathways and important biological processes were examined. The significance of an association between the target gene set and a given gene ontology or biological pathway was determined using a hypergeometric distribution model, as shown in the following equation:Import the screened Hub gene into DAVID 6.8 database (crf.gov) , and seln is the number of genes identified as being targets of HD, M is the number of genes annotated to specific GO terms or pathways, N is the total number of genes in the reference set, and k is the number of genes that are shared between HD-target genes and the reference set.Specifically, With the parameters set at P 0.5 and a large number of target enrichment, 247 molecules were added into the DAVID database for GO enrichment study and analyzed for GO enrichment. Key biological processes include the positive regulation of gene expression and transcription from the RNA polymerase II promoter, as well as the response to drugs, the negative regulation of the apoptotic process, cell proliferation, and so on. Finally, the findings of the disease enrichment were exported.Cytoscape v3.8.2 was used to create the “active ingredient-potentialtarget-action route” network . Active https://pubchem.ncbi.nlm.nih.gov/) using the PubChem ID. From the PDB (http://www.rcsb.org/pdb/home/home.do) database [An AutoDock  programmdatabase , the hig41 kinds of main chemical components in Astragalus + Codonopsis (HD) were obtained through the TCMSP database, including 20 from HQ and 21 from DS. The corresponding information for the screened active ingredients is shown in A total of 97 HQ active ingredient targets and 51 DS active ingredient targets were identified using the target database's prediction findings. A total of 112 targets were gathered from the merged and deduplicated data sets. The GeneCards database yielded 918 disease-related targets; the DisGeNET database returned 336; the OMIM database returned seven; all based on the search keywords. After integrating and deduplicating, we had a total of 1067 targets. In order to create the Venn diagram, 112 TCM components and 1067 disease targets were mapped out . A TCM-dThere are only 21 intersection molecules between the action targets of TCM ingredients and disease targets, which are relatively small. These target compounds were then submitted to STRING to acquire information on their interactions with proteins. And a total of 21 molecules from the intersections were isolated. Then, the linked protein interaction information was obtained and formed a PPI network map , in whicMCODE subnetwork analysis is used to find more closely connected genomes in the network. The point with the highest weight obtained by weighted calculation is set as the seed. Starting from the seed, it moves outward, recursively, to find nodes that can join the subnetwork. The closer the target is to the center, the more important it is. Subnetwork 1 is centered on EGFR, FOS, ESR2, and MYC, and its important targets, such as MAPK14 and RAF1, are targets related to the MAPK pathway, indicating that subnetwork 1 is closely related to it . The corP < 0.5 and a large number of target enrichments, as shown in Figures A total of 247 molecules were entered into the DAVID database for GO enrichment analysis and evaluated for GO enrichment under the parameters of P < 0.05) through the DAVID 6.8 data platform, as shown in Potential targets were subjected to KEGG pathway enrichment analysis  and the ESR2 gene expressing estrogen receptor β (ERβ) can mediate multiple physiological effects of estrogen [In order to predict the mechanism of action of HD for MG, the intersecting targets were screened in the PPI network by cytoHubba and MCODE plugins, and the 7 core targets that were obtained were EGFR, FOS, ESR1, ESR2, MYC, CASP3, and IL-6. It was found that HD could influence the occurrence and progression of MG through biological processes such as the MAPK cascade, the extrinsic apoptotic signalling pathway in the absence of ligand, immune response, inflammation response, and negative regulation of apoptotic processes, as shown by GO enrichment analysis. Recent studies have reported that EGFR can be expressed in thymoma, which is an important cause of myasthenia gravis, and that EGFR overactivates the downstream PI3K/Akt/mTOR signalling pathway, inhibits apoptosis, and stimulates tumor growth , 32. EGFestrogen . A recenestrogen . Immune estrogen , 39. Cysestrogen , 41. Lacestrogen . Plasmacestrogen , 44. Theestrogen . Studiesestrogen , 47. Cliestrogen , 49. In estrogen . The resestrogen . So the α enhances Th17 expression and regulates Th17 differentiation by increasing aerobic glycolytic activity required for rapid T-cell expansion [α have a significant role in managing MG patients and concluded that the mTOR-HIF-1α signalling could be the possible immune metabolism reprogramming checkpoint of MG [HD may serve a therapeutic function in MG via the PI3K-Akt signalling pathway, inflammatory bowel illness, according to the findings of the KEGG enrichment study. Cell proliferation, differentiation, metabolism, and death are all regulated by the PI3K-Akt signalling system, according to research. Different hormones, growth factors, and cell-to-cell contacts frequently trigger this regulation . It is bxpansion , 56. Myaxpansion , 58. A rnt of MG .After docking the five major components of HD with their key target genes, it was found that their docking binding energy with the target was all <−5. It indicated that the five key components showed a strong affinity with the key target genes. Therefore, HD is expected to be a major component of novel natural drugs for the treatment of MG.In summary, the results of the network pharmacological analysis indicate that HD mainly acts on EGFR, FOS, ESR1, ESR2, MYC, CASP3, IL-6, and other genes through various active components, such as quercetin, kaempferol, lignan, isorhamnetin, and 7-methoxy-2-methylisoflavone. These components treat myasthenia gravis through anti-inflammatory, immunomodulatory, and inhibiting muscle cell apoptosis. Molecular docking verified the treatment effect of HD on MG. The mechanism of action of HD in the treatment of myasthenia gravis is through the combined action of various major active ingredients from the formula with potential core targets. This study laid the foundation for the in-depth study of the pharmacological mechanism of HD in the treatment of myasthenia gravis.Our study results showed significant efficacy of HD against MG by interacting and altering several gene expressions. Besides these wonderful results, our study lacks practical experimentation. To prove these facts as an outcome of network pharmacological analysis, experimentation is recommended and could be a step forward.


Pathway-based analysis of transcriptomic data has shown greater stability and better performance than traditional gene-based analysis. Until now, some pathway-based deep learning models have been developed for bioinformatic analysis, but these models have not fully considered the topological features of pathways, which limits the performance of the final prediction result.To address this issue, we propose a novel model, called PathGNN, which constructs a Graph Neural Networks (GNNs) model that can capture topological features of pathways. As a case, PathGNN was applied to predict long-term survival of four types of cancer and achieved promising predictive performance when compared to other common methods. Furthermore, the adoption of an interpretation algorithm enabled the identification of plausible pathways associated with survival.PathGNN demonstrates that GNN can be effectively applied to build a pathway-based model, resulting in promising predictive power.The online version contains supplementary material available at 10.1186/s12859-022-04950-1. Identification of gene expression profiles that discriminate disease phenotypes is a relatively routine research procedure in bioinformatics. Most traditional methods aim to discover gene-sets associated with disease phenotypes . HoweverPathway-based analysis of gene expression profiles has shown recently greater stability of biological activities and better performance . The canRecent advancements in graph neural networks (GNNs) have expand the capabilities to capture the hidden topological information in pathway. GNNs, a class of deep learning methods, has been designed to perform analysis on non-Euclidean data that was represented by graph consisting of nodes and relationships between edges . Due to Furthermore, interpretation algorithms for deep learning model generally are often used for biological interpretation, such as identifying pathways associated with disease phenotypes. Until now, various strategies have been proposed to interpret deep learning models. Recently, the integrated gradients (IG) algorithm has been widely used for interpretation in community . IG measIn this study, we proposed a novel method, called PathGNN, based on the hypothesis that GNNs can effectively capture the topological feature of pathway, thereby improving model performance. In addition, we introduced IG interpretation algorithms to explain PathGNN model for identifying key pathways associated disease phenotypes. As a case, we performed PathGNN to predict cancer survival and detect plausible pathways using transcriptomic and clinical data, which is a fundamentally challenging problem in bioinformatics. We demonstrated the method achieved promising predictive performance in differentiating between long-term survival (LTS) and non-LTS when applied to multiple cancers. Through the proposed methods, we show that the resulting GNN model achieves much better predictive performance due to capturing the topological information hidden in pathway.The gene expression matrix and clinical data of cancer patients were download from TCGA . IndividAfter data preprocessing, we selected four types of cancer . We determined the survival years threshold k = 3 to validate our method, which was considered to have a larger data sample size and better data balance. After removing individuals who missed clinical information or survived but had their last followed-up ≤ 3 years, the LTS and non-LTS groups had 132 and 137, 232 and 110, 155 and 75, 290 and 111 cases for LUAD, SKCM, LGG and KIRC, respectively. Age and tumor stage were used as clinical features through feature selection.Typically, a graph is defined as ractions  as edgesdatabase  using grdatabase , a R pacIn total, 2390 pathways downloaded from Reactome database, while 855 pathways were remained for followed analysis , a readout layer after each block , graph normalization layer between blocks  and multilayer perceptrons    and PytoTo understand the contribution of the component to the overall system, an optimization study by removing or replacing individual modules was conducted on LUAD. Specifically, we first replaced the GraphSAGE with graph attention network (GAT) or graph convolutional network (GCN) to investigate the impact on applying different graph convolution algorithm. Next, we removed or added blocks in the Subnetwork1 to explore the effect of different number of blocks. In addition, the Set2Set that adopted in the PathGNN architecture is a global pooling algorithm based on iterative content attention. We replaced the Set2Set with global add pooling and global average pooling algorithm, which returns graph-level outputs by adding and averaging node features across the node dimension, respectively. The experimental training procedure for this method optimization was the same as before.The aberrant pathways associated with clinical prognosis were identified by IG. Here, we calculated the importance value  [The IG scores were z-transformed and those pathways with z-score value ( < 0.05)  were conThe predictive performance of PathGNN was compared with several machine learning methods, including random forest (RF), deep neural network (DNN), logistic regression (LR) and pathway-guided deep neutral network (PGDNN) –9. For aThe DNN model was constructed with four hidden layers, where the number of nodes were 855, 128, 64 and 16, respectively. For PGDNN, the architecture was the same as DNN, but the first hidden layer, named pathway layer, which represents the biological pathways linked with input genes. The construction of these two models can be found in the Additional file The result of architecture optimization was shown in Fig. The PathGNN model was trained to classify LTS and non-LTS groups in multiple cancers. In addition, the predictive performance was compared with four different machine learning methods, including logistic regression, random forest, DNN and PGDNN. Those methods were constructed as described in the methods section. As a result, the predictive performance for five algorithms on four datasets mentioned above were summarized in Table P value < 0.05) . The survival curves of partial pathways that have been demonstrated to be associated with cancer prognosis were shown in Fig. A Kaplan–Meier analysis was performed to compared survival time between two groups dichotomized by a median split of IG score in the key pathways. That is, given a key pathway associated with disease phenotypes, IG scores across all specific cancer samples were dichotomized by a median split. Then, the log-rank test was performed for each key pathway. As shown in Additional file Pathway-based analysis of transcriptomic data has shown recently greater stability of biological activities and better performance than gene-based analysis . Until nThe pathway is considered as a set of genes and their interactions with specific biological function, which is a graph data structure. In addition, GNNs are a class of deep learning methods that show better performance on graph than DNNs, CNNs or other machine learning methods due to the ability to capture topological information. Therefore, we proposed a novel deep learning method based on GNNs, called PathGNN, which has the additional ability to capture the topological information hidden in pathway.We applied PathGNN to predict 3-year survival of four types of cancer  using transcriptomic and clinical data, considering that the sample size of LTS and non-LTS was relatively balanced, which was conductive to verifying our method. The predictive performance of PathGNN in differentiating between LTS and non-LTS shown an average AUCs of 0.723, 0.702, 0.862 and 0.754 were obtained in LUAD, SKCM, LGG and KIRC, respectively Table . OverallThe IG algorithm was utilized in PathGNN to detect those pathways that associated with clinical outcomes. There were 13, 4, 9 and 5 key pathways identified on LUAD, SKCM, LGG and KIRC, respectively  it inherits the advantage of PGDNN due to the introduction of gene-sets in pathways into modeling; (2) the topological information of pathway is remained through GNNs. In this study, PathGNN achieved further predictive power than PGDNN and the other machine learning methods, which suggested topological feature can improve model performance based on pathway. In short, PathGNN is expected to further promote the application of pathway-based analysis in biomedical field.The inherent limitation of our approach is that it can identify key pathways with disease phenotype, but not how the pathway activation trend affects disease phenotype. This limitation could be extended in a future extension through transfer learning.Additional file 1. The information of total pathways for building PathGNN model.Additional file 2. The architecture of DNN and PGDNN.Additional file 3. The key pathways associated with cancer survival identified by PathGNN.


Lung adenocarcinoma (LUAD) is one of the major causes of cancer death in the world. Studies show that the effective anticancer component in blister beetles is cantharidin, which can improve chemotherapy efficacy, median survival, and prognosis of LUAD. However, the antitumor mechanism of blister beetles has not been fully clarified. This study aimed to identify the key targets of the treatment of LUAD by blister beetles based on the principle of network pharmacology. An integrated approach including network pharmacology and a molecular docking technique was conducted, which mainly comprises target prediction, weighted gene correlation network analysis (WGCNA) analysis, network construction, gene ontology, and pathway enrichment analysis. 35 key targets were obtained and significantly associated with response to external stimuli, collagen binding, cyclin binding, organic acid binding, pyruvate metabolism, glycolysis, and amino acid biosynthesis pathways. Both LASSO regression and the RF model had a high predictive ability, and 9 candidate genes were screened, among which BIRC5 and PLK1 were the key targets for the treatment of LUAD by using blister beetles and showed significant survival significance. Cantharidin exerts its antitumor effects through 8 targets in 32 pathways, while BIRC5 and PLK1 have obvious survival significance. Lung cancer is the most common malignant tumor in clinical practice, and its mortality and morbidity rank first in malignant tumors . MoreoveThe blister beetle is a famous Chinese medicine and has been demonstrated in vitro to have significant antitumor effects in cancer cells such as lung, breast, gastric, hepatocellular, and ovarian cancers –12. In CNowadays, network pharmacology integrates network biology and polypharmacology on the basis of existing databases to provide a new approach to explore the mechanisms and synergistic effect of TCM formulas for the treatments of diseases –19. For In order to systematically explore the targets of blister beetles in inhibiting LUAD, we integrated complex pharmacology and molecular docking experiments based on four databases to obtain the key chemical components and their targets, which not only provided a new direction for the mechanism of action of LUAD inhibition by blister beetles, but also identified clinically relevant biomarkers that could be used to monitor the treatment of blister beetles.https://portal.gdc.cancer.gov/) was used to download transcriptomic data of TCGA-LUAD. In this study, WCGNA was used to screen for key modules associated with LUAD [R package. The coexpression network of genes with mean expression values greater than 1 in LUAD samples (n = 535) and normal samples (n = 59) was used to filter out key modules and genes associated with LUAD. First, the samples were clustered and the outlier samples were deleted to ensure the accuracy of the subsequent analysis. Then, the soft threshold is determined and the coexpression network is constructed by using the dynamic tree cutting algorithm. The minimum number of genes per gene module was set to 50 and MEDissThres was set to 0.2. The Limma package (version 3.46.0) [FC/≥ 1, p < 0.05). Ggplot2 (version 3.3.3) was used to map volcano plots to show differentially expressed genes, The VennDiagram package (Version 1.6.20) was used for differentially expressed genes between LUAD and the control with intersection of key module genes associated with LUAD obtained by WGCNA. The TCMSP (https://tcmspw.com/tcmsp.php) database and the DrugBank (https://go.drugbank.com/) database were used to obtain the active ingredients of blister beetles and their drug targets; the Genecards (https://www.genecards.org/) database was used to predict disease targets in LUAD, retaining only genes with a category of protein coding (category = protein coding and relevance score ≥1). The VennDiagram package was used to intersect key module genes obtained from WGCNA with drug targets and LUAD disease targets from the database to screen for key targets.TCGA database (ith LUAD . TCGA mR 3.46.0)  was usedP < 0.05 and COUNTS ≥2). The enrichplot package (Version 1.10.2) was used to draw bar and bubble graphs to show GO enrichment and KEGG pathway enrichment results.GO stands for Gene Ontology, which is an international standard classification system for gene function. It aims to establish a linguistic vocabulary standard for qualifying and describing gene and protein functions that are applicable to various species. The GO system consists of three components: biological process (BP), molecular functions (MF), and cellular components (CC). Using the GO database, we can obtain what our target genes are mainly related to at the CC, MF, and BP levels. Enrichment analysis of KEGG and GO databases was used to find common functions and related pathways of a large number of genes within the module key genes. In this study, the VennDiagram package was used to obtain key targets by taking intersections of differentially expressed key module genes, drug targets, and LUAD disease targets. The clusterProfiler package  was usedhttps://string-db.org) website was used to construct a PPI network map of 35 key targets with confidence = 0.4 and remove discrete proteins.The key targets of GO_BP, GO_CC, and GO_MF were presented to construct a key target-function regulatory network. The top 50 pathways of KEGG and their corresponding key targets were presented to construct a key target-pathway regulatory network map and were visualized using Cytoscape (version 3.8.2) . To inven = 594) were used as the training set, and GSE10072 was used as the verification set (n = 107) to construct and verify the diagnostic model. The pROC (Version 1.17.0.1) package is used to draw the receiver operating characteristic curve (ROC) of the training set and verification set, and to calculate the area under curve (AUC) value. The higher the AUC value is, the more accurate the prediction is. When the RF algorithm was used to construct the optimal diagnostic model, the expression values of each sample of 35 key targets in TCGA database were combined with the grouping information of the samples, and the RF model was constructed by using the Caret package (Version 6.0-86). Then, the EXPLAIN function of the DALEX package (Version 2.3.0) was used for explanatory analysis of the RF model, and the plot function was used to visualize the distribution of model representation; then, the cumulative residual distribution and boxplot distribution were drawn.The LASSO regression model and RF algorithm were used to construct a diagnostic model to determine the predictive power of the model constructed based on candidate genes for LUAD. The LASSO analysis tool is a glmnet package (Version 4.1-1) . All TCGThe VennDiagram package (Version 1.6.20) was used to take the intersection of the genes obtained by LASSO and the RF algorithm. The ggpubr R package (Version 0.4.0) and the ggplot2 package were used for scatter plotting and visualization to study the expression of nine biomarkers in LUAD samples and normal samples. The test was a rank sum test.https://www1.rcsb.org/) database, and the small molecules and water molecules were removed. Molecular docking is used to determine the presence of interactions between biomarkers and molecules, and to predict their binding patterns and affinities. The AutoDock tools (version 1.5.6) were used to complete protein hydrogenation and charge calculation. The active ingredient structures were downloaded from the PubChem (https://pubchem.ncbi.nlm.nih.gov/) database. Charge balanced and rotatable bond checking for small molecules was performed using AutoDock tools. Then, AutoDock Vina was used to calculate the receptor-ligand docking according to the range of docking boxes selected by the receptor-active center. The structure with the lowest binding free energy in the output results was selected. Finally, PyMol (Version 2.5) software is used for visualization and beautification.The active components corresponding to 9 biomarkers were extracted to construct the drug-active component-key target gene network map, and Cytoscape was used for visual analysis. The protein structure of the key target was obtained from the PDB  and survMiner (Version 0.4.8) packages, and P < 0.05 indicated that the genes had a significant survival significance. Subsequently, the correlation between biomarkers and clinical factors  was studied.GO and KEGG were used for functional and pathway analysis of biomarkers. Kaplan–Meier survival analysis of the biomarkers was performed using To obtain key targets for the interaction between blister beetles and LUAD disease, a total of 27 active drug ingredients and 503 drug targets of the blister beetle were obtained from the TCMSP database. Furthermore, 1236 drug targets were obtained from the DrugBank database and 7031 LUAD disease targets were obtained from the GeneCards database. Subsequently, these targets and key module genes were intersected to obtain key targets.n = 594) and found that there were no outliers, as shown in R^2 approaches 0.85 (red line), the soft threshold (β) is equal to 5. At this time, the network approaches the scale-free distribution, and mean connectivity also approaches 0. Therefore, we choose the optimal soft threshold (β) as 5, as shown in P = 0.003), and the absolute value of the correlation coefficient is the largest, which is the key module of lung adenocarcinoma, as shown in P < 0.05), as shown in We clustered all LUAD samples (n = 396), drug targets (n = 1653), and LUAD disease targets (n = 7031), as shown in A total of 35 key targets were obtained by taking the intersection of differentially expressed key module genes  were used as the training set, and GSE10072 was used as the verification set (n = 107) to construct and verify the diagnostic model. The graph of the gene coefficient and the error graph of cross validation were obtained by LASSO regression analysis, and a total of 11 characteristic genes were screened, as shown in In this study, all TCGA samples  were obtained from the intersection of genes obtained by the LASSO model and RF model, as shown in The drug-active ingredient-key target gene network diagram contains 1 drug (blister beetles), 7 drug-active ingredients, 8 biomarkers, and 32 relationship pairs, as shown in P < 0.05 and count ≥ 2). In terms of cell composition, biomarkers were associated with the STAGA complex, dihydro lipid-based dehydrogenase complex, concentrated chromosome, centromere central centromere concentrated chromosome, centromere concentrated spindle microtubules, etc. In terms of molecular function, biomarkers were associated with retinal binding, H4 histone acetyltransferase activity, long-chain fatty acid transporter activity, retinol binding, long-chain fatty acid binding, microtubule binding, organic acid binding, carboxylic acid binding, lipid transporter activity, and monocarboxylic acid binding, as shown in P < 0.05 and count ≥ 1), and the biomarkers were mainly enriched into CC and MF functional categories. The results showed that biomarkers were associated with PPAR signaling pathway, platinum resistance, notch signaling pathway, lipolysis regulation of adipocytes, ABC transporter, apoptosis, and antifolic acid resistance, as shown in T staging and stage staging, as shown in GO function analysis was performed on the biomarkers, and the biomarkers were mainly enriched into two functional categories of CC and MF , also known as survivin, is a member of the inhibitor of apoptosis protein (IAP) family and also a target for cancer therapy  which exβ1, thus inhibiting the proliferation, migration, and invasion of small-cell lung cancer. Reda et al. [Polo-like kinase 1 (PLK1) is a highly conserved silk/threonine protein kinase that plays an important role in cell cycle regulation and DNA damage repair . Currenta et al.  found thIn this study, we obtained the key targets of blister beetles treating LUAD and the biomarkers related to LAUD by using network pharmacology and molecular docking methods based on four databases, indicating that the application of network pharmacology is a scientific and feasible method. However, these results need to be verified in more cell experiments to be better applied in clinical treatment and drug development, including the potential pathway of the Blister beetle antilung cancer activity, the mechanism of action in treating lung cancer, and in vivo experiments to confirm its activity are further needed.In conclusion, we screened the key targets of cantharidin for the treatment of LUAD and the biomarkers in LUAD based on network pharmacology and bioinformatics gene analysis. The results revealed that the 7 active compounds exert their antitumor effects via 8 targets in 32 pathways, and BIRC5 and PLK1 had a significant survival significance. This is consistent with the TCM concept of “multiple compounds, multiple targets, and multiple effects.” This provides a systematic view of the potential anticancer mechanism of cantharidin.


This article aims to explore the impact and mechanism of invigorating qi and promoting blood circulation (IQPBC) on angiogenesis after myocardial infarction (AMI) by using network pharmacology approach.  First, IQPBC was searched on the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP), and the main active ingredients and targets of IQPBC were screened and obtained. Second, by virtue of GeneCards and Online Mendelian Inheritance in Man (OMIM) databases, the targets related to AMI are screened and then obtained. Then, the intersection targets between IQPBC and AMI can be obtained by using online tool Venny 2.1.0. Third, based on the STRING database, the interaction of target proteins is established and some key targets can be analyzed and obtained. Finally, the IQPBC-AMI interaction network is constructed by using Cytoscape, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses are executed by DAVID and OmicShare databases. α, and other 10 key targets are obtained. A total of 752 enrichment results are acquired by using GO analysis. KEGG pathway enrichment analysis shows 241 signaling pathways, focusing on cancer, fluid shear stress and atherosclerosis, and TNF and PI3K/AKT signaling pathways.  143 intersection targets between IQPBC and AMI are obtained. Besides, key active ingredients, namely, quercetin, tanshinone, kaempferol, and luteolin, are shown. Furthermore, AKT1, VEGFA, STAT3, HIF-1 This article studies the potential targets and signaling pathways of IQPBC drugs acting on AMI via the network pharmacology approach, which better illustrates the effect and mechanism, and provides some good ideas for the following mechanism research studies. In recent years, it is reported that cardiovascular disease (CVD), currently, was already the first factor leading to death among urban and rural residents in China. Not just that, the number of CVD patients has reached 290 million –4. As isSalvia miltiorrhiza Bunge), Guizhi (Ramulus Cinnamomi), Huangqi , Mu dan pi (Cortex Radicis Moutan), and Renshen (Radix Ginseng), which have been verified that all these show good effect in IQPBC and removing stasis [Traditional Chinese medicine has shown obvious advantages and potential in promoting angiogenesis. For instance, some effective herbs, components, and compound preparations can promote the growth of coronary artery collateral vessels and protect the ischemic myocardium. Based on this fact, this article considers a Chinese herbal medicine compound: Danshen  is to capture the relationships between drugs, targets, and diseases. Based on TCMSP, the main active ingredients and corresponding targets of drugs can be screened by limiting oral bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18. Meanwhile, the UniProt database was used for data correction.The objective of TCMSP (https://omim.org/) and GeneCards database. First, enter the OMIM database, click “Gene Map,” search with “angiogenesis of myocardial infarction” as the search term, and download the search results as Excel files. Second, the GeneCards database was used to search for “angiogenesis of myocardial infarction,” and the search results were downloaded as Excel files. Finally, combined with the aforementioned results, the corresponding tables of IQPBC and AMI targets were imported by using the online tool Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/index.html), and then the intersection targets of IQPBC and AMI were obtained.Targets for AMI are screened using the OMIM database  algorithm in the STRING database and the CytoHubba plugin in Cytoscape 3.7.2 software were jointly utilized to obtain the clusters and key targets.https://david.ncifcrf.gov/) can be used for differential analysis of genes and enrichment of pathways, and it links genes in the input list to biological annotations. The steps can be summarized as follows: enter the “Function Annotation Table” and upload the intersection targets. Then, click “OFFICIAL_GENE_SYMBOL,” “Gene List,” “Submit List,” and “Homo sapiens” in turns. Next, select “Gene-Ontology” and “Kegg-Pathway” and download the files, respectively. In the end, combined with R software, DAVID, and OmicShare, the GO and KEGG enrichment analyses of intersection targets can be obtained.DAVID  terms, 110 molecular function (MF) terms, and 67 cellular components (CC) terms. In p value <0.05). The first 20 pathways are shown in Figures α signaling pathway, microRNAs in cancer, endocrine resistance, colorectal cancer, proteoglycans in cancer, apoptosis, and PI3K-Akt signaling pathway. The enriched classification, the number and p values of the classification, the prospective gene, and Rich Factor values are shown in KEGG pathway analysis shows 241 treatment pathways are screened  takes integrity and systematicness as the starting point to construct a complex network of component-target-disease to analyze and clarify the mechanism of action of the research object , 31, whiα and STAT3 signaling pathways [α and VEGF2 activation in endothelial cells mainly through ERK/p38 MAPK and PI3K/Akt/mTOR [By analyzing in detail, four key active ingredients of IQPBC in the treatment of AMI are screened and obtained, namely, quercetin, tanshinone, kaempferol, and luteolin. As a potential inhibitor of angiogenesis, quercetin plays a vital part in anti-inflammatory and antiatherosclerosis. Furthermore, some experiments have shown that quercetin can significantly recover the blood flow of the ischemic hind limb in mice, increase the capillary density of the ischemic muscle, and improve cardiac function . Tanshinpathways . Kaempfepathways , 37, whiAkt/mTOR  and thenα are the key targets of IQPBC in the treatment of AMI. AKT1 shows a great effect on growth factor signal transduction, cell proliferation, differentiation, and transcriptional regulation and development. It is worth noting that STAT3 is considered a key regulator for adjusting the cardiac microenvironment [α, which activates HIF-1 to bind to VEGF and activate the VEGFA/VEGFR2 signaling pathway. Angiogenesis is induced by PKC, NOS, Akt, and other pathways. Combined with the relevant literature, GO and KEGG enrichment analyses are directed to find that the PI3K-AKT signaling pathway, TNF signaling pathway, and fluid shear stress and atherosclerosis signaling pathway are the key pathways of IQPBC in the treatment of AMI. The PI3K-Akt signaling pathway has the ability to improve cardiac function and myocardial ischemia after MI by ensuring the survival, proliferation, and differentiation of endothelial cells. Some remarkable results have found that AngII and its receptor PI3K/AKT signaling pathway may be related to AMI [Based on the obtained results, it can be seen from the PPI network that AKT1, VEGFA, STAT3, and HIF-1ironment  where itironment . VEGFA pironment . Under hd to AMI , 43, andd to AMI , 45.α is highly expressed in the myocardial tissue after myocardial infarction, and TNF-α stimulates the expression of the stem cell factor (SCF) in myocardial cells, thereby inducing the migration of BMSC from the damaged human myocardium and differentiation into cardiac cells, enhancing the repair of the damaged heart and improving the function of the heart [Tumor necrosis factor (TNF) is expressed in ischemic tissues and promotes angiogenesis , 47. Undhe heart . Fluid she heart . Laminarα, and other targets as key components. It may regulate PI3K/AKT signaling pathway, TNF signaling pathway, and fluid shear stress and atherosclerosis signaling pathway to promote angiogenesis after infarction. It laid the foundation for the mechanism and pharmacodynamic substance of IQPBC in the treatment of AMI. Therapeutic angiogenesis plays an important role in restoring revascularization and improving cardiac function, which opens a new perspective for the treatment of myocardial infarction.In conclusion, quercetin, tanshinone, kaempferol, and luteolin in IQPBC act on AKT1, VEGFA, STAT3, HIF-1


As a kind of traditional Chinese medicine, HQ is widely mentioned in the treatment of cancerous diseases in China, which has been proven to have a therapeutic effect on cancerous diseases, such as prostate cancer. To predict the specific mechanism of HQ in the treatment of CRPC, we will conduct preliminary verification and discussion based on a comprehensive consideration of network pharmacology and molecular docking.TCMSP was used to obtain the compounds and reach the effective targets of HQ. The targets of CRPC were reached based on GeneCards database and CTD database. GO and KEGG were utilized for the analysis of overlapping targets. The software of Openbabel was used to convert the formats of ligands and reporters. In addition, molecular docking studies were performed by using the software of Autodock Vina.It can be seen from the database results that there were 87 active compounds (20 key active compounds) in HQ, and 33 targets were screened out for CRPC treatment. GO and KEGG pathway enrichment analyses identified 81 significant GO terms and 24 significant KEGG pathways. There is a difference in terms of the expression of core protein between cancer patients and healthy people. The expression of core protein in patients also has an impact on the life cycle. The results of molecular docking showed that the docking activity of drug molecules and core proteins was better.It is concluded from the results of this network pharmacology and molecular docking that HQ makes a multi-target and multi-biological process, and results in the multi-channel synergistic effect on the treatment of CRPC by regulating cell apoptosis, proliferation and metastasis, which still needs further verification by experimental research. Prostate cancer (PCa) ranks the second among the most common malignancies diagnosed in men. It was the third largest source of cancer-related deaths across the world in 2018, with 1,276,106 new cases annually and 358,989 deaths . The trehttp://lsp.nwu.edu.cn/tcmsp.php), the bioactive ingredients (OB) and targets of HQ were obtained. The former refers to the rate and extent of the absorption of the drug into the body’s circulation. Drug-like properties (DL) reflect the nature of a drug with a specific functional group or the same or similar physical characteristics. Bioactive ingredients were collected under the condition of OB≥30% and DL≥0.18. After that, the corresponding molecular targets of these collected active compounds were obtained by using the same database.The Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP)  is a plahttps://www.genecards.org/) and the Comparative Toxicogenomics Database . Venny2.1.0 (http://bioinfogp.cnb.csic.es/tools/venny/index.html) was employed to construct over-lapping targets for CRPC treatment and bioactive ingredients of HQ, allowing the identification of targets of HQ in the treatment of CRPC.Search for CRPC-related targets with “castration-resistant prostate cancer” as a search term by using GeneCards database  can be used for the analysis of the interaction between proteins. In our study, the species was limited to “Homo sapiens”, and the lowest interaction score was set to medium confidence (0.400). After obtaining the PPI networks from the STRING database, Cytoscape software was utilized for further topology analysis. Finally, the node size and colour were adjusted with the software of Cytoscape to construct the complete PPI networks of overlapping targets, so as to clarify the key regulatory proteins functioned in the networks.The STRING database  (https:/https://david.ncifcrf.gov/) database [P<0.01 was considered to indicate a statistically significant difference.The Database for Annotation, Visualization, and Integrated Discovery  database, after that, the expressions of these targets in prostate tissues of cancer patients and normal people were compared. The cBioportal For Cancer Genomic  database is used for the survival analysis to analyze the impact of changes in core targets on the prognosis of cancer patients.The immunohistochemical images of core targets were screened from The Human Protein Atlas (https://www.rcsb.org/) for molecular docking based on the compound-target network relationship. Respectively, the software of Openbabel and AutoDock Vina were used for chemical format conversion and molecular docking. The active sites of the cocrystal ligands were used as the pockets of receptors for molecular docking. The interaction of the compounds with the lowest binding free energy was analyzed on the Biotechnology Center of the TU Dresden  platform [Select corresponding ligands obtained from TCMSP database and receptors performed in the Protein Data Bank database  website, and then we got the Advanced Bubble Chart , 5 items related with Molecular function (MF) and 56 items related with Cellular component (CC) were obtained, after Go enrichment analysis   for survival analysis for five core proteins (as shown in P<0.0001). We performed a survival analysis of the AR gene using cancer database  (6875 patients / 7161 samples in 22 studies). It can be seen from the results that whether the AR gene is changed or not has a significant impact on the life cycle of Pa patients (P<0.05).The Human Protein Atlas database showing the expressions of these targets in prostate tissues of cancer patients and normal people is different. Prostate tumor and normal prostate tissues show elevated AR expression instead of ESR1 expression. Though normal prostate tissues show no HSP90AA1 expression, in prostate tumor tissues, the gene is weakly expressed or not expressed. PPARG gene is not expressed in normal prostate tissues but weakly expressed or not expressed in prostate tumor tissues. Normal prostate tissues show elevated PTGS2 expression, but prostate tumor tissues show elevated expression or no expression   Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(DOCX)Click here for additional data file.


Pathway annotation is a vital tool for interpreting and giving meaning to experimental data in life sciences. Numerous tools exist for this task, where the most recent generation of pathway enrichment analysis tools, network-based methods, utilize biological networks to gain a richer source of information as a basis of the analysis than merely the gene content. Network-based methods use the network crosstalk between the query gene set and the genes in known pathways, and compare this to a null model of random expectation. We developed PathBIX, a novel web application for network-based pathway analysis, based on the recently published ANUBIX algorithm which has been shown to be more accurate than previous network-based methods. The PathBIX website performs pathway annotation for 21 species, and utilizes prefetched and preprocessed network data from FunCoup 5.0 networks and pathway data from three databases: KEGG, Reactome, and WikiPathways.https://pathbix.sbc.su.se/erik.sonnhammer@scilifelab.seBioinformatics Advances online. Data generation is often not the limiting factor to gain biological insight. High-throughput experiments provide a plethora of data, which in turn yields a list of genes or proteins that are related to the studied phenotype. However, this list alone is not enough to understand the complex biological mechanisms that are involved in a given condition. An indispensable approach to resolve this issue is pathway enrichment analysis, which can reveal activated biological processes by detecting patterns of gene groups in the list. This is done by comparing to functionally characterized and curated gene sets, known as pathways, in databases such as KEGG  , and a backend application containing the application logic, built as a Python (v.3.7) (https://podman.io) containers, and the containers are run on a CentOS8 virtual machine.The PathBIX application is built with a frontend application containing the graphical interface, using the Angular (v.9) framework ( (v.3.7)  rest API (v.3.7)  version  (v.3.7)  algorith (v.3.7)  databaseThe networks used to calculate the crosstalk between the query and the pathway gene sets were collected from FunCoup 5.0   and the PathBIX uses information on pathway gene sets from three different pathway databases, KEGG . All results can be downloaded as text files or network images in various formats  to be used for further analysis. The results of PathBIX can easily be stored or shared by using the shareable URL, which will allow the users to quickly return to their results.To give the user an overview of the crosstalk between the query and the pathways, the pathway table contains a matrix showing all query genes, colored by the level of crosstalk to the pathway genes in the respective pathway. The way that the coloring is displayed can be modified by the user to show either genes colored by gene crosstalk over the observed crosstalk, gene crosstalk over the total links per gene, or absolute crosstalk. The results view is highly customizable, and lets the user adapt the visible results in accordance to its needs, by for example displaying pathway category, observed and expected crosstalk, H.sapiens and 74 pathways for Methanocaldococcus jannaschii in KEGG, 289 pathways for H.sapiens and 49 pathways for Plasmodium falciparum in Reactome, and 622 pathways for H.sapiens and 6 pathways for Escherichia coli in WikiPathways. For Reactome and WikiPathways, pathway information is missing for some of the species present in PathBIX, while KEGG pathways are available for all species. The number of unique pathway genes included in the PathBIX database varies between the pathway databases, and since ANUBIX is basing the analysis on the information in the FunCoup networks, only pathway genes included in the networks are utilized in the tool. To visualize how many of the unique pathway genes are represented in the FunCoup network, we calculated the fraction of genes in the networks represented as genes in a pathway for each of the pathway databases (see H.sapiens retrieved from KEGG on the FunCoup network, covers close to 50% of the genes in the network with a cutoff of 0.8. The number of pathways and unique pathway genes included in PathBIX from each pathway database, for each species, can be found in Supplementary The integration of data from the three pathway databases, KEGG, Reactome and WikiPathways into PathBIX resulted in a total of 7856 pathways in the PathBIX database. The number of pathways included in PathBIX for each of the pathway databases, for each species, can be seen in ases see . Here, wH.sapiens against the KEGG database, with a network cutoff of 0.95. This gene set is provided as one of the example gene sets in the tool. The results page of PathBIX when running this analysis can be found in To illustrate the usability of PathBIX, we ran the analysis with the gene set CHIN_BREAST_CANCER_COPY_NUMBER_UP for Here we present PathBIX, a web application for reliable crosstalk-based pathway enrichment analysis. Other websites for network-based pathway enrichment rely on methods that are prone to find false positives  because Because different pathway databases use different pathway boundary definitions (vbab010_Supplementary_DataClick here for additional data file.