To understand complex diseases, high-throughput data are generated at large and multiple levels. However, extracting meaningful information from large datasets for comprehensive understanding of cell phenotypes and disease pathophysiology remains a major challenge. Despite tremendous advances in understanding molecular mechanisms of cancer and its progression, current knowledge appears discrete and fragmented. In order to render this wealth of data more integrated and thus informative, we have developed a GECIP toolbox to investigate the crosstalk and the responsible genes’/proteins’ connectivity of enriched pathways from gene expression data. To implement this toolbox, we used mainly gene expression datasets of prostate cancer, and the three datasets were GSE17951, GSE8218, and GSE1431. The raw samples were processed for normalization, prediction of differentially expressed genes, and the prediction of enriched pathways for the differentially expressed genes. The enriched pathways have been processed for crosstalk degree calculations for which number connections per gene, the frequency of genes in the pathways, sharing frequency, and the connectivity have been used. For network prediction, protein–protein interaction network database FunCoup2.0 was used, and cytoscape software was used for the network visualization. In our results, we found that there were enriched pathways 27, 45, and 22 for GSE17951, GSE8218, and GSE1431, respectively, and 11 pathways in common between all of them. From the crosstalk results, we observe that focal adhesion and PI3K pathways, both experimentally proven central for cellular output upon perturbation of numerous individual/distinct signaling pathways, displayed highest crosstalk degree. Moreover, we also observe that there were more critical pathways which appear to be highly significant, and these pathways are HIF1a, hippo, AMPK, and Ras. In terms of the pathways’ components, GSK3B, YWHAE, HIF1A, ATP1A3, and PRKCA are shared between the aforementioned pathways and have higher connectivity with the pathways and the other pathway components. Finally, we conclude that the focal adhesion and PI3K pathways are the most critical pathways, and since for many other pathways, high-rank enrichment did not translate to high crosstalk degree, the global impact of one pathway on others appears distinct from enrichment. High-throughput data ((epi-)genomic, transcription, and proteomic) are frequently generated with the goal to understand the genotype–phenotype relationship in complex diseases ,2,3. TheTo address this, we have here developed a GECIP toolbox, a computational method that first processes the list of genes or proteins of interest and performs pathway enrichment and network analysis similar to other methods . Most imhttps://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 November 2022)). The steps in GECIP proceed from preparation of a gene list (DEGs), formulation of the network of DEGs, prediction and analysis of enriched KEGG pathways [In this work, the GECIP toolbox was applied for the differentially expressed genes (DEGs) in prostate cancer versus normal tissue. Since the gene expression alterations in different cancer patients and experimental conditions may vary, we have collected three separate human microarray gene expression datasets for prostate cancer  [us (GEO) . Three sus (GEO)  has beenus (GEO)  has beenus (GEO) . For mosus (GEO) ,4,5,6,51Analysis of crosstalk between the pathways: To investigate the crosstalk between the enriched pathways, we have divided the approach into three major steps. In the first step we have processed the raw data for normalization applying RMA [mattest MATLAB function) has been applied for identifying significant changes between two groups of data  for differential gene expression analysis [mattest ) uses a two-sample t-test. Every gene is subjected to a standard two-tailed, two-sample unpaired t-test for differential expression, which results in a p-value for every gene. For FDR calculation, the details could be seen in the link mentioned here ). For fold change calculation, we have used the p-values after corrections, and the threshold was 0.05. The same we have also updated in the main text. In the second step, we have prepared the list of DEGs and the total gene list and have also collected the list of KEGG pathways. Once we have these three lists in normal text file format, a Fischer exact test [m is the total number of genes genome-widely annotated with each pathway, N is the total number of genome-wide genes, k is the number of genes annotated with an individual pathway in the gene list, n is the total number of genes in the gene list, and f-value is the value of probability that this random event could happen under hypergeometric distribution [ying RMA ,54. A stanalysis ,55,56,57act test ,58,59 isribution ,55,60,61p-values, multiple hypothesis testing has been applied for false discovery rate (FDR) correction. From this step, we get the list of enriched pathways. We have considered only those pathways which have FDR < 0.05 [For all the pathways listed in the KEGG database , prepared a mathematical model by using mass-action kinetics and ordinary differential equations (ODEs) [s (ODEs) ,72,73,74s (ODEs) ,75, optis (ODEs) ,67,68. Ts (ODEs) ,67,68.We have established a signaling cascade that operates similarly to the MAPK signaling cascade. The target level is represented by target proteins, which are those proteins that transmit information to the nucleus in the form of the output response. This signaling cascade is divided into several different levels of signaling, such as the receptor level, intracellular signaling level, and the target level. Then, at various signaling levels, we have incorporated various loop types as well as cross-interactions  between two signaling cascades. In the next phase, we have a mass-action kinetic model which utilizes ODEs for all the molecules. The model equation (Equation (4)) used to express the temporal change in the concentration of the signaling molecules was mentioned in detail in our previous work ,63,64. with randomly generated kinetic parameters . The secondary antibodies were goat anti-mouse immunoglobulin G (IgG)–Alexa Fluor 488  and goat anti-rabbit IgG–Alexa Fluor 555 .Experimental validation: We have collected human prostate biopsies for both normal and cancer condition and performed immunohistochemistry (IHC) for human prostate biopsies . The primary antibodies were p44/42 MAPK (Erk1/2) (L34F12) Mouse mAb #4696 and Phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) (D13.14.4E) XPDetails of the staining procedure: These sections were deparaffinized and rehydrated before antigen retrieval with R-Buffer A (Electron Microscopy Sciences) in a pressure cooker. After blocking with 2% bovine serum albumin, the sections were incubated with different primary antibodies (see above) at 4 °C overnight. Extensive rinsing was performed once; the sections were incubated with the secondary antibodies goat anti-mouse immunoglobulin G (IgG)–Alexa Fluor 488  and goat anti-rabbit IgG–Alexa Fluor 555  for 1 h at room temperature. DNA was counterstained with TO-PRO-3 iodide (Molecular Probes), and slides were mounted with ProLong Gold (Molecular Probes).Fluorescence images were obtained with a Zeiss LSM 780 inverted confocal microscope, using a Plan-Apochromat 40×/NA 1.2 objective. Through-focus maximum projection images were acquired from optical sections 0.5 μm apart and with a section thickness of 1.0 μm.All the MATLAB codes which have been used for entire calculation are available on request, and the pseudocode is presented below .t-test statistical analysis approach was applied to identify the differentially expressed genes (DEGs) in the three datasets [p-values  method. After normalization, a two-sample datasets ,26,55. Iequation . To acquWe found 27, 45, and 22 enriched pathways in Grasso, GSE8218, and GSE1431, respectively c. GrassoSince a large number of enriched pathways were evident in all three individual datasets, to unravel more comprehensively the crosstalk between the enriched pathways, all the DEGs of the three datasets were combined into one list, and we further calculated the pathways a–c.Many of the enriched pathways were shared between the three datasets, and the pathways with highest datasets . TherefoCTdegree . For DEGCTdegree . Common The enriched pathways and datasets a. Thus, datasets . In thisTdegree) b with thCTdegree c. FurtheCTdegree .After crosstalk calculations, as shown in In addition to extending our entire approach, we have integrated our code with AutoDock vina software and also have our own code, which predicts putative binding sites on the protein for the given ligand or drug. Therefore, for more applications in terms of the clinical perspective or personalized therapy, this toolbox can be applied. In this study, three herbal drugs have been selected which are known for their application in herbal cancer therapy , targeting the top three proteins which appear to be highly dominant based on our crosstalk analysis ; we obseMost of the cellular network architectures have been reported to fall within a scale-free topology ,26,85. Ip-values, but this does not clarify how these enriched pathways are associated or crosstalk with each other. By using GECIP, we add yet another step to such pathway-level understanding. The aim of this work is methodology-focused, and off course, there are few steps for which the methodology may resemble many different softwares, such as DEGs’ calculation, motif prediction, docking, and the prediction of enriched pathways, while the main goal was crosstalk (CT-score) prediction and is uniquely presented here. The prediction of crosstalk is completely different from any other work. We agree that elation will be helpful, which we have updated.With the advancement in high-throughput technologies for gene expression, sequencing, and proteomics experiments, the primary goal has been to find the significant gene  or protein list and infer the pathways for these lists. For differential gene expression analysis, there are a number of tools available such as DESeq2, DEseq, baySeq2, EBseq, edgeR, etc. . Since wBased on our analysis, we consider GECIP particularly useful to translate the high-throughput transcriptomic, genetic, and proteomic data to pathway–pathway-interaction-network-level understanding. As a result, few pathways or representative pathway components with most or least overall crosstalk or connectivity can be selected as targets for more precise studies based on the study questions and goals. Instead of focusing on a large number of pathways, the study focus can thus be narrowed to the selected pathways. Based on the overall analysis results, we conclude that the DEGs and enriched pathways can vary from one dataset to another within the same cancer type, whereas there are also common frequently altered pathways. Notably, all the highly enriched pathways will not have comparable enriched . Out of Finally, we conclude that our approach appears unique and novel, which reveals the systems-level understanding and application, validation, and integration of high-throughput data and the possible scope to predict putative drug targets. In summary, we could say that it helps understanding from large biological data integration to phenomics to final drug targets.


Biejiajian pill (BJJP), a classical traditional Chinese formula, has been reported that it has an effective treatment for diabetic atherosclerosis in recent years, but its underlying mechanisms remain elusive. The study aimed to explore the potential mechanisms of BJJP on diabetic atherosclerosis by integrating network pharmacology, molecular docking, and molecular dynamics simulation.  The active components of BJJP were collected by TCMSP and TCMID, and then the potential targets were obtained from the SwissTargetPrediction database. The targets related to diabetic atherosclerosis were identified from the GeneCards and OMIM databases. The intersection of the potential targets regulated by active components of BJJP and the targets of diabetic atherosclerosis were common targets, which were visualized by the Venn diagram. The common targets were imported into the STRING database to construct a protein-protein interaction (PPI) network. The network of “Medicine-Compound-Target” was constructed with Cytoscape 3.7.1 software. GO functional enrichment analysis and KEGG pathway enrichment analysis were performed using the DAVID database and visualized through bioinformatics. The intersecting targets were input into Cytoscape 3.7.1 software, and the Network Analyzer tool was employed to screen out the key targets. Then molecular docking was used to verify the binding affinity between the active compounds and the key targets, and molecular dynamics simulation was used to investigate the stability of the binding models. −1, −37.12 kJ·mol−1, and −62.91 kJ·mol−1, respectively.  A total of 81 active components, 186 targets of BJJP, and 4041 targets of diabetic atherosclerosis were obtained. Furthermore, 121 overlapping targets were identified. GO functional enrichment analysis revealed that these targets were correlated with the oxidation-reduction process, negative regulation of apoptotic process, inflammatory response, and other biological processes. The results of the KEGG pathway enrichment analysis showed that the common targets mainly participated in proteoglycans in cancer, PPAR signaling pathway, adherens junction, insulin resistance, HIF-1 signaling pathway, PI3K-Akt signaling pathway, etc. The results of molecular docking confirmed that the core active components in BJJP could bind well to the key targets. Results from molecular dynamics simulation showed that the binding energies of AKT1-Luteolin, MMP9-quercetin, and MMP9-luteolin complexes were −28.93 kJ·mol The study revealed that BJJP is characterized as multicomponent, multitarget, and multipathway to treat diabetic atherosclerosis, which is helpful to provide ideas and a basis for pharmacological research and clinical application in the future. Diabetes mellitus has emerged as a pandemic. The International Diabetes Federation (IDF) has released new figures showing that 537 million adults are now living with diabetes worldwide, which is a rise of 16% 74 million) since the previous IDF estimates in 2019 [ million Synopsis of Golden Chamber, a traditional Chinese medicine clinical prescription, is broadly utilized for the prevention and therapy of hepatocellular carcinoma [Biejiajian pill (BJJP) originated from arcinoma , so the arcinoma . Furtherarcinoma –9. HowevDiabetic atherosclerosis is a disease with a complex pathological mechanism and its potential mechanism remains elusive. At present, lifestyle interventions, antihyperglycemia, antihypertension, lipid regulation, and antiplatelet therapy are widely adopted in clinics, but single-targeted therapy cannot meet the needs of inhibiting the occurrence and development of diabetic atherosclerosis. Therefore, it is crucial that safer and more effective drugs are needed for the treatment of diabetic atherosclerosis.Network pharmacology provides new horizons for traditional Chinese medicine (TCM) formula research. Its comprehensiveness, systematicness, and holistic concept are consistent with the TCM formula's characteristics of multicompound, multitarget, and multipathway. Most TCM formula prescriptions were derived from empiricism. It lacks scientific and rational mechanism interpretation although its efficacy has been validated for many years in clinical practice. The application of network pharmacology in the TCM formula is expected to achieve the change from experience-based medicine to evidence-based medicine, and network pharmacology is expected to become an important direction of TCM research studies .In this study, network pharmacology analysis, molecular docking techniques, and molecular dynamics simulation were used to visualize and elucidate the complex relationships and underlying mechanisms of BJJP in the treatment of diabetic atherosclerosis, which is helpful to provide a reference for experimental studies and clinical application in the future. The detailed flowchart of this study is summarized in http://tcmsp.com/tcmsp.php) [http://47.100.169.139/tcmid/) [https://pubchem.ncbi.nlm.nih.gov) [http://www.swisstargetprediction.ch/) [The active compounds of BJJP were acquired from the Traditional Chinese Medicine Systems Pharmacology database and Analysis Platform   with the/tcmid/)  with theion.ch/)  and the In order to more intuitively show the interactions between the active compounds of BJJP and their potential targets, the “medicine-compound-target” network was constructed by Cytoscape 3.7.1 software .https://www.genecards.org/) [https://omim.org/) [The keyword “diabetic atherosclerosis” was used to extract the targets from the GeneCards (ds.org/)  and Onliim.org/)  databasehttps://string-db.org) [In general, it is difficult for a single protein to complete a series of complex biological processes. The common targets of BJJP and diabetic atherosclerosis were imported to the Search Tool for the Retrieval of Interacting Genes (STRING) database (-db.org)  with thehttps://david.ncifcrf.gov/) [P value <0.05 were considered significant entries. A histogram was constructed with the first 10 functional categories of BP, CC, and MF. A bubble diagram was drawn by the top 20 signal pathways, and then the information of the first 20 pathways was imported into Cytoscape 3.7.1 software to display the “target-pathway” network.The common targets were imported into the Database for Annotation Visualization and Integrated Discovery   for bioihttps://www.rcsb.org/) [The 3D structures of the key targets were retrieved and downloaded from the RCSB Protein Data Bank (sb.org/) , removinsb.org/) , and thesb.org/) . After tsb.org/) .pdb2gmx in GROMACS v4.6.5 [+ and Cl− neutralizing the system; and thirdly, the periodic boundary condition with a minimal distance of 1.2 nm. The steepest descent minimization method was applied to the energy minimization of complex systems. Meanwhile, 100 psNVT and NPT simulations were implemented to sequentially balance the complex systems. Furthermore, the PME , LINCS (Linear Constraint Solver), and SETTLE algorithms were performed to calculate the long-range electrostatic interactions, constrain hydrogen bonds, and restrict water molecules, respectively.The docking models of the complexes on the top three molecular docking results were used as the initial conformation for molecular dynamics (MD) simulations. The force fields of the receptor were set to Amberff99SB using S v4.6.5 , 25. SimS v4.6.5 , 27. TheA total of 81 active compounds in BJJP were screened via TCMSP and TCMID databases and the published related literature. The basic information of active ingredients is listed in To further reflect the interaction relationship between the active compounds and their targets, the network of medicine-compound-target was constructed through Cytoscape 3.7.1 software. The blue diamond nodes represented the targets, the green circle nodes represented Chinese medicine, and the octagon nodes represented the active compounds as shown in For disease target identification, 3865 and 227 targets of “diabetic atherosclerosis” were identified from the GeneCards and OMIM databases, respectively. Subsequently, 4041 disease targets were integrated after deleting duplicates. Finally, 121 overlapping targets were shown as a Venn diagram between the drug targets and the disease targets .The PPI network was constructed with a medium confidence score of 0.4 by importing the common targets into the STRING database, as shown in P < 0.05 and a bubble diagram was drawn by the top 20 signal pathways. The larger the bubble, the more genes were gathered in the pathway. As depicted in GO function enrichment included BP, CC, and MF. Among them, 205 items were obtained via BP enrichment analysis, 31 items via CC, and 84 items via MF. The BP analysis indicated that the overlapping targets were mainly related to signal transduction, positive regulation of transcription from RNA polymerase II, oxidation-reduction process, negative regulation of the apoptotic process, inflammatory response, etc. The CC results mainly included plasma membrane, cytosol, extracellular space, protein complex, etc. The MF results are mainly comprised of enzyme binding, protein kinase activity, steroid binding, etc. The top 10 items of BP, CC, and MF were selected to draw the GO function histogram of BJJP in the treatment of diabetic atherosclerosis . MeanwhiMolecular docking and binding ability prediction were carried out with five core active compounds  and nine key targets , respectively. Finally, 45 groups of ligand-receptor docking results were obtained, 44 with affinity <−4.25 kcal/mol. The results of the molecular docking indicated most of the core active compounds could bind well with key targets and there were stable hydrogen bonds at the binding sites. A section of the docking patterns of the core compounds and targets are shown in The MD simulations of the AKT1-luteolin, MMP9-quercetin, and MMP9-luteolin complexes were implemented to investigate the stability of the binding models. From −1, −37.12 kJ·mol−1, and −62.91 kJ·mol−1, respectively, showing that the binding processes were spontaneous. In addition, it also implied that luteolin was more likely to bind to MMP9 compared to quercetin, which was consistent with the docking result.Meanwhile, the binding energies of these complexes were calculated using the MM/PBSA method. From Then, the binding energies of these complexes were decomposed to each residue. From Diabetes mellitus (DM) is a chronic disease that occurs when the pancreas is no longer able to make insulin or when the body cannot make good use of the insulin it produces. People with diabetes have an increased risk of developing a number of serious health problems. Notably, macrovascular complications are the leading cause of disability and death in patients with diabetes mellitus, and the injury of VECs is the initiating factor and the key link. Previous studies have shown that BJJP has a significant effect on diabetic atherosclerosis. However, the underlying mechanisms of BJJP against diabetic atherosclerosis are not fully elucidated. In this study, we employ the technology of network pharmacology, approaches of molecular docking, and MD simulation to explore the potential mechanisms of BJJP on diabetic atherosclerosis in order to better guide clinical treatment.κB signaling pathway [Firstly, we retrieved the active compounds of BJJP, and 81 were identified. Then we constructed the medicine-compound-target network by mapping 81 active compounds to 186 related targets. Notably, the top five active compounds with high degree value are quercetin, luteolin, kaempferol, chrysin, and glutamic acid, respectively, indicating that they may be the core bioactive compounds. Among them, quercetin, luteolin, kaempferol, and chrysin all belong to flavonoids, which exist widely in nature, and have the function of both medicine and food and a wide range of pharmacological activities. They all show antioxidative and anti-inflammatory effects; however, the mechanisms are not exactly the same. To some extent, these were associated with the pathogenesis of diabetic atherosclerosis. It has been reported that quercetin may have promising potential in ameliorating diabetic atherosclerotic pathophysiology in the diabetic-induced atherosclerotic rat model carotid artery induced by the administration of a high-fat diet with streptozotocin by inhibiting oxidative stress and inflammatory responses mechanistically by modulating the AMPK/SIRT1/NF- pathway . Luteoli pathway . VSMC pr pathway . Kaempfe pathway .Metformin is currently recommended as a first-line treatment for patients with diabetes and as an essential drug in combination, in the guides of both domestic and international. Its main pharmacological effect is to reduce blood sugar by reducing the liver glucose output and improving insulin resistance. Metformin alone did not increase the risk of hypoglycemia and reduced the risk of cardiovascular events, but there were gastrointestinal adverse events . Chrysin−/− mice via the PPARγ pathway [Insulin resistance is closely associated with chronic low-grade inflammation through interactions with the insulin signaling pathway in the liver and adipose tissue, which is also considered one of the main factors responsible for the onset and progression of diseases such as obesity, diabetes, and atherosclerosis. Therefore, the improvement of insulin resistance is important in the management of diabetes atherosclerosis. The HIF-1 signaling pathway plays a key role in the development of atherosclerosis through cell-specific responses acting on endothelial cells, VSMCs, and macrophages. Through the upregulation of VEGF, NO, ROS, and PDGF, it is able to cause endothelial cell dysfunction, proliferation, angiogenesis, and inflammation . It was  pathway . The fun pathway . Adheren pathway .−/− background displaying severe peripheral vascular disease, atherosclerosis, occlusive coronary artery disease, plaque vulnerability, and cardiac dysfunction. This manifested itself in decreased blood flow, reduced migration of fibroblasts and endothelial cells, and decreased production of the potent vasodilator NO [α and IL-1 but also regulate adipocyte differentiation as well as lipid metabolism. This activity might contribute to the treatment of diabetic atherosclerosis [κB/NF-κB signal pathway via the MMP9/TIMP1 axis has an effect on vascular inflammation T2DM atherosclerosis [In the PPI network, we found that AKT1 (degree = 70), EGFR degree = 53), SRC (degree = 53), STAT3 degree = 50), PPARG degree = 45), ESR1 degree = 44), MMP9 (degree = 37), PIK3R1 (degree = 29), and AR (degree = 29) played important roles in BJJP against diabetic atherosclerosis. It is well known that AKT1 is one of the three isoforms of the AKT family. AKT is one of the key molecules activated downstream of the PI3kinase signaling pathway and regulates many cellular processes including metabolism, proliferation, cell survival, growth, and angiogenesis through serine and/or threonine phosphorylation of a range of downstream substrates. However, one of the key roles of AKT is the regulation of glucose uptake into insulin-responsive tissues, which is mediated by the translocation of insulin-regulated glucose transporter 4 (GLUT4) from vesicular intracellular compartments to the plasma membrane . In addilator NO , 44. EGF, MMP9 (d, ESR1 delator NO . Activatlator NO . PPARG cclerosis . Previouclerosis . MMP9 is, PPARG dclerosis . Accumulclerosis . It was , SRC (deIn conclusion, the therapeutic potential of BJJP in the treatment of diabetic atherosclerosis may be through regulating blood lipids, reducing inflammatory factors and oxidation-reduction reaction, and so on.−1, −37.12 kJ·mol−1, and −62.91 kJ·mol−1, respectively, showing that the binding processes were spontaneous.Molecular docking and MD simulation were employed to further verify the results obtained. The results of the molecular docking indicated most of the core active compounds could bind well with key targets. Then the results of molecular dynamics simulation suggested that AKT1-luteolin, MMP9-quercetin, and MMP9-luteolin could bind tightly, and the binding energies of the complexes were −28.93 kJ·molHowever, it should be noted that there were certain limitations of the network pharmacology. Firstly, it is difficult for current network pharmacology technology to achieve the goal of quantizing, such as the dose-efficacy relationship between the drugs and disease . SecondlIn conclusion, the study was based on network pharmacology, molecular docking, and MD simulation to explore the potential mechanisms of BJJP against diabetic atherosclerosis through compound screening, target prediction, construction of PPI network, GO enrichment analysis, and KEGG pathway enrichment analysis. The results indicated that quercetin, luteolin, kaempferol, chrysin, glutamic acids, and other effective compounds of BJJP showed therapeutic effects against diabetic atherosclerosis via multiple targets and multiple pathways. In addition, the good activities of the core active compounds and the key targets were verified by the molecular docking method and molecular dynamics simulation. All in all, the results of this study provided a reference for clinical application and guidance for further experimental verification of BJJP for the treatment of diabetic atherosclerosis.


To establish a normative database for the vascular density (VD) in macular, disc, and peripapillary regions in healthy myopic children and to evaluate associated ocular features with optical coherence tomography angiography (OCTA).This was an observational, prospective and cross-sectional study. 776 Chinese healthy myopic children (375 boys and 401 girls) were enrolled, mean (±SD) age 9.84 ± 1.98 (range 6–16) years. En-face angiogram OCTA was performed on 6 mm × 6 mm retinal and 4.5 mm × 4.5 mm disc regions. VD measurements in the macular retina were segmented into the four regions: superficial capillary plexus (SCP), intermediate capillary plexus (ICP), deep capillary plexus (DCP), and choriocapillaris (CC). Correlations between macular, disc, and peripapillary VD and possible influencing factors  were assessed by Pearson’s correlation and multivariate regression analysis.P < 0.001). The corrected VD in CC was (72.96 ± 4.42)% for the whole macular retina. The macular CC VD in every section was all higher than the SCP, ICP, and DCP . The size of foveal avascular zone (FAZ) and foveal VD 300 (FD-300) was 0.28 mm2± 0.10 mm2 and (58.43 ± 4.17)% respectively. For disc scans, the corrected VD was (58.04 ± 2.73)% for the whole disc area. Both AL and SER were strongly correlated with ICP, DCP, and CC VD in all regions . Larger SSI was correlated with a lower VD in the SCP and ICP, and a higher VD in DCP and CC .For macular scans, the corrected VD in the ICP region was (48.25 ± 4.24)% for the whole macular retina. The macular ICP VD in most sections was lower than the SCP, DCP, and CC (all Vascular density values provide large scale normative data on macular, disc, and peripapillary vascular parameters in a large sample of healthy myopic children with OCTA measured in the four different retinal plexuses and regions. The VD in different regions had various influencing factors; mainly a close correlation with AL and SSI. Optical coherence tomography angiography (OCTA) is helpful in the evaluation of retinal blood flow which can be compromised in some macular or optic nerve head diseases , 2. PrevThere are some studies relating to peripapillary retinal nerve fiber layer thickness and the macular retinal thickness in children . HoweverIn this study, OCTA was used to measure the normative data on each regional superficial and deep macular, disc, and peripapillary VD in a large sample of 6–16 year old healthy Chinese children. Various ocular parameters were also measured to assess potential correlations with retinal VD.This observational, prospective and cross-sectional study was conducted at the Eye Hospital of Wenzhou Medical University. A total of 714 subjects (1428 eyes) between 6 and 16 years old were recruited for this study. The inclusion criteria were as follows: (1) Uniocular corrected distance visual acuity was no worse than 20/20; (2) Intraocular pressure was between 9 and 21 mmHg; (3) Refractive error was between 0.00 and -6.00 diopters (D); (4) No history of systemic disease or any evidence of retinal pathology. This study was approved by the Medical Ethics Committee of the Eye Hospital of Wenzhou Medical University. Written informed consent was obtained from parents of participants. Besides, the poor OCTA image quality was evaluated by the low signal strength index (SSI) and the low quality index (QI), which reflects the signal strength, motion artifacts and image sharpness respectively. Therefore, subjects with poor quality images on OCTA (SSI lower than 55 and/or QI lower than 7) were excluded. We also excluded blink or doubling artifacts, and/or segmentation errors. All subjects underwent standard ophthalmic examinations, cycloplegic autorefraction , axial length , and OCTA imaging in both eyes . All measurements and OCTA images were performed by the same skilled examiner (SHM). All OCTA images were checked by one expert for quality and correctness of automated layer segmentation.The VD measurements of macula, disc, and peripapillary regions were generated by OCTA using the RTVue XR Avanti device with the AngioVue Analytics software (version 2018.0.0.14) to analyze image. This software includes the 3D PAR algorithm, which could remove projection artifacts of image. The Angio Retina HD mode (6 mm × 6 mm) and the Disc (4.5 mm × 4.5 mm) mode were performed on each eye with horizontal and vertical orthogonal OCTA volume scans at the fixed position. The OCTA  showed tThe macular VD (VD) was defined as the ratio of the area of the large vessel and capillary vessel divided by the total area measured in particular sections. The VD of the fovea, parafovea and perifovea were acquired through 6 mm × 6 mm HD mode OCTA images were calculated by AngioVue Analytics software. In accordance with the “Early Treatment Diabetic Retinopathy Study” (ETDRS) subsegment method, the parafovea and perifovea regions were divided into four quadrants, namely superior, inferior, temporal, and nasal, respectively, and two equal hemispheres (superior and inferior). In addition, retinal OCT angiographs was automatically layered into superficial vascular plexus (SVP) and the deep vascular complex (DVC) by the standard commercial software traditionally , which mFor 4.5 mm × 4.5 mm optic disc scan, parameters of the optic nerve head (ONH) VD referred to the whole VD  and whole capillary VD (only including capillary). The whole capillary VD was divided into the inside disc capillary VD referring to the area within the optic disc boundary and the peripapillary VD that was defined as a 750-mm-wide elliptical annulus extending from the optic disc boundary. The VD of disc or peripapillary capillary were measured from the ILM to the posterior boundary of the retinal nerve fiber layer (RNFL). RNFL thickness and the peripapillary thickness also were measured in this study. According to the Garway Heath Partition Method, the peripapillary area was subdivided into eight sections including nasal superior, nasal inferior, inferior nasal, inferior temporal, temporal inferior, temporal superior, superior temporal, and superior nasal.2 outlined and calculated by this software using the FAZ measure function. Perimeter and acicularity Index of FAZ also was calculated. The macular, disc, and peripapillary vasculature was automatically segmented by the software , p can be calculated as 1/[0.01306 × (23.824.46 – 1.82)] = 3.48 according to RTVue XR Avanti system . A sample Kolmogorov–Smirnov (K–S) test was used to verify the normality of the factors such as age, AL, spherical equivalent refraction (SER), and VD in each section of the macula, disc, and peripapillary parameters. Pearson’s correlation was used to assess correlation with possible influencing factors and VD parameters. The correlation between different plexuses in macular, disc, and peripapillary VD was also analyzed by Pearson’s correlation. We conducted a multivariate stepwise linear regression analysis to identify the main factors determining a greater or lesser macular and disc VD. The children normative VD values in macular and disc were corrected by the magnification factor and were reported as the mean with standard deviation and the range from minimum values and maximum values. The significance value was set at 5%.In total, 793 healthy children were enrolled in this study. Fourteen children were excluded due to poor quality images with macular (10 children) and optic disc (6 children) scans and segmentation errors (1 child). Therefore, the study included 1,552 eyes of 776 Chinese children (375 boys and 401 girls) with a mean (± SD) age of 9.84 ± 1.98 (range 6–16) years. The characteristics of the eyes, whole macular VD in different layers and disc retina VD in the children studied were shown in P < 0.001), correlated with the SCP VD only in three regions (P < 0.05), and not correlated with the DCP VD or CC VD in any region (P > 0.05). Age was significantly positively correlated with DCP VD and CC VD in all macular region (P < 0.01), not correlated with SCP or ICP VD in most regions (P > 0.05). Both AL and SER, were strongly correlated with ICP, DCP, and CC VD in all regions (P < 0.01), and related with SCP VD in only three regions (P < 0.05). A longer AL or lower refraction could imply larger ICP VD and lower DCP and CC VD (P < 0.01) (P > 0.05), except for superior, nasal parafovea in SCP, superior parafovea in ICP, and fovea in DCP (P < 0.05). The VD of right eyes in SCP, ICP, and CC was closely related to those of the left eyes (P < 0.01) but DCP VD.nd CC VD , 2. SSI  < 0.01) . Larger P < 0.05). There was close relationship in those parameters between right and left eyes (P < 0.05). A shorter AL implied a smaller FAZ and FD-300 area (P < 0.05), and higher SSI implied a larger FD-300 area and FD-300 length (P < 0.05). Those parameters was not associated with age, SER, CFT, and CFV (P > 0.05), as shown in FAZ area, FAZ perimeter, FD-300 area and FD-300 length was larger in girls than boys . AL and SSI persisted as significant determinants of DCP VD and CC VD (P < 0.05). A larger FAZ area was mainly found in girls and determined by a shorted AL (P < 0.05).In multivariate regression analysis , only SSP < 0.05). The inside disc VD was also higher in girls than boys, with a higher SSI and a larger disc area (P < 0.05). The peripapillary VD was higher in girls than boys (P < 0.05), with a shorter AL, larger SER, larger disc area, and larger TS (P < 0.05). There was close relationship in those parameters between right and left eyes (P < 0.05). The relationship between the possible factors and each regional peripapillary and disc VD is detailed in P < 0.05). Gender, AL, SSI, disc area, and TS persisted as significant determinants of peripapillary VD (P < 0.05).At the disc and peripapillary region , the whoP > 0.05). A larger SCP VD related to a larger ICP VD, a lower DCP VD (P < 0.05), and a lower CC VD. A larger DCP VD implied a lower ICP VD and a larger CC VD (P < 0.05). There were positive relationships between whole disc VD, inside disc VD, and peripapillary VD (P < 0.05). SCP VD was positively correlated with whole disc VD, inside disc VD, and peripapillary VD (P < 0.05). CC VD was not corrected with those VD in disc. ICP VD and DCP VD were correlated with whole disc VD and inside disc VD (P < 0.05). The details were showed in There were close relationships between whole macular VD in any two layers, except for no relationship between ICP and CC VD (P < 0.001) (P < 0.001). The highest VD in CC was in the fovea among different area. The size of foveal FAZ area and perimeter was 0.28 mm2 ± 0.10 mm2 and 2.02 mm ± 0.38 mm. The density of FD-300 area was (58.43 ± 4.17)%. The normative data of macular VD in the four subsegments were shown in For macular scans, the SCP VD were (52.86 ± 3.08)%, (23.90 ± 6.93)%, (55.82 ± 3.64)%, and (53.58 ± 3.20)% for the whole, fovea, parafovea, and perifovea respectively. The ICP VD were (48.25 ± 4.24)%, (40.23 ± 7.60)%, (51.15 ± 3.70)%, and (48.95 ± 4.49)% for the whole, fovea, parafovea, and perifovea respectively. The ICP VD for every section was the lowest among SCP, ICP, DCP, and CC in macular retina  , except P < 0.001). The normative data of peripapillary VD in the subsegments were shown in For disc and peripapillary scans, VD vs. capillary VD was (58.04 ± 2.73)% vs. (51.02 ± 2.69)%, (65.41 ± 4.10)% vs. (56.69 ± 4.95)%, and (59.68 ± 3.08)% vs. (52.58 ± 3.14)% for the whole, disc, and peripapillary. The VD in temporal inferior peripapillary was significantly higher that other section of peripapillary  in the present study. The result was similar to that reported by Zhang et al. (2 in 6–16 year old children were reported, respectively. The perimeter of FAZ and acicularity index in adults is also in line with our studies, 1.99 mm and 1.08, respectively (2. While the correlation analyzes (The enlargement of the FAZ is a sign associated with retinal microvascular disease. The size of the FAZ area in children of 6–16 years old was 0.28 mmg et al.  and Hasag et al. . In theiectively . The FAZectively , 20, 21.ectively  ranging analyzes  found gianalyzes  and Kizianalyzes . The difanalyzes , 25, whianalyzes  and age,analyzes , 24. Howanalyzes  also hadThe largest VD was observed in the CC among the four different vascular plexuses in macular retina in all sections of the macular vascular layers. The correlation  between This is the first study to measure disc and peripapillary VD with and without large vessels in large sample size of children by RTVue-XR Avanti algorithm. The mean disc and peripapillary VD were 65.41 and 59.68%, and reduced by about 7% after the subtraction of large vessels. The VD in the temporal peripapillary region was greater than nasally, which was similar with findings in adults , 31. TheThis study was a large sample prospective study to report normative data of VD and analyze various clinical and demographic factors that may affect VD measurement. However, this study had some limitations. First, the 6 × 6 not 3 × 3 macular scan pattern was chosen in this study. The regional perifoveal VD based on the 6 × 6 scan pattern was first reported in this study. If two models were included at the same time, there was concern about visual fatigue in children. Second, subjects in this study was limited to the Chinese myopic children. More children coming from different countries and emmetropic eyes were needed to study in future. Third, choroidal thickness was not included in the study because there is no method for objectively measuring this parameter currently. Fourth, due to the limitations of the algorithm, it was not possible to analyze the normative data and influencing factors of the VD in subsegments from choroid and outer retina. Finally, longitudinal studies are needed to observe the effect of age on various regions of retinal VD.These findings are a baseline reference providing normative data on the size of the FAZ, FD-300 and VD at the macula, disc, and peripapillary regions in a large sample of healthy myopic children in China with the RTVue XR-Optovue OCTA. We also describe how retinal perfusion parameters are influenced in each individual region by gender, age, eye, AL, SER, SSI, CFT, CFV, total thickness of never fiber and disc area. These data in a large sample can be used in future studies and provide a reference for the development of blood flow diseases involving the macula or ONH.The original contributions presented in this study are included in the article/The studies involving human participants were reviewed and approved by the Medical Ethics Committee of the Eye Hospital of Wenzhou Medical University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.RC, HL, JQ, JH, and YL contributed to the conception and design of the study. SL, TW, KD, and HP organized the database. HL performed the statistical analysis. RC wrote the first draft of the manuscript. RC, CM, ES, JQ, JH, and YL wrote sections of the manuscript. All authors contributed to the manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Pathway enrichment analysis is extensively used in high-throughput experimental studies to gain insight into the functional roles of pre-defined subsets of genes, proteins and metabolites. Methods that leverages information on the topology of the underlying pathways outperform simpler methods that only consider pathway membership, leading to improved performance. Among all the proposed software tools, there’s the need to combine high statistical power together with a user-friendly framework, making it difficult to choose the best method for a particular experimental environment.p values regarding node-specific group effect estimates in terms of activation or inhibition, after statistically controlling biological relations among genes within pathways. We used SEMgsa to identify biologically relevant results in a Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) together with a frontotemporal dementia (FTD) DNA methylation dataset (GEO accession: GSE53740) and compared its performance with some existing methods. SEMgsa is highly sensitive to the pathways designed for the specific disease, showing low p values (We propose SEMgsa, a topology-based algorithm developed into the framework of structural equation models. SEMgsa combine the SEM https://CRAN.R-project.org/package=SEMgraph.SEMgsa is a novel yet powerful method for identifying enrichment with regard to gene expression data. It takes into account topological information and exploits pathway perturbation statistics to reveal biological information. SEMgsa is implemented in the R package SEMgraph, easily available at The online version contains supplementary material available at 10.1186/s12859-022-04884-8. Biomedical research has been transformed by recent advances in high-throughput technologies, enabling extensive monitoring of complex biological systems. As a result, new methodological developments have emerged, most notably the adaptation of systems perspectives to analyze biological systems. Pathway enrichment has become a key tool in the analytic pipeline for Omics data and has been effectively used to generate novel biological hypotheses and determine if specific pathways are linked to specific phenotypes. In the literature, dozens of strategies have been developed, varying in model complexity and effectiveness.Earlier methodologies, such as over-representation analysis (ORA)  and geneA second generation of approaches was created to reduce this reliance on gene selection criteria by taking into account all gene expression values. The hypothesis behind these approaches is that small yet coordinated changes in groups of functionally related genes may be crucial in biological processes. These methods are named functional class scoring methodologies (FCS) . Such meORA and FCS methods can be referred to as the first two generations of pathway enrichment analysis approaches. However, when pathways are seen as a basic unstructured and unordered collection of genes, all the genetic connections and interactions that are supposed to capture and characterize the actual processes at hand are simply neglected.With the aim of including all of this additional information into the analysis, topology-based (TB) approaches have been created. These methods account for interactions between biomolecules and provide better performance than standard second-generation methods , 3. A vaThe goal of pathway enrichment approaches is to compare the ’activity’ of interest pathways across two or more biological situations or groups of specimens . Another technical feature that distinguished pathway enrichment methods is the type of the statistical null hypothesis being tested. The majority of approaches may be divided into two categories: those that test (I) self-contained null hypotheses and (II) competitive null hypotheses . A self-The main contribution of this article is the development of a self-contained topology-based algorithm developed into the framework of structural equation models (SEM), called SEMgsa , 17. EvaA second objective of this study is to provide a consistent optimum solution of any given biological situation. Many topology-based methods that investigate distinct null hypothesis have been proposed in literature. We compare five popular pathway analysis approaches to SEMgsa, starting from the most similar ones in terms of multivariate test and self-contained hypothesis type  together with one approach of competitive hypothesis type (PathwayExpress) and the older approach of over-representation analysis (ORA). All the methods in this article offer a nice use interface in R.The aforementioned methods have been applied on observed and simulated expression data. The ultimate goal of expression data application is to provide a meaningful comparison of gene set analysis methods in terms of (i) sensitivity and (ii) prioritization for observed data and (i) type I error and (ii) power within each simulation run.The remainder of the article is organized as follows. Firstly, we describe SEMgsa features with regard to gene expression data both in terms of inference procedure and user interface. Then, we outline the experimental setup constructed to evaluate pathway enrichment methods, including real data application and simulation design. In the end, we provide the results together with overall discussion.path diagram, represented as a graph V is the set of nodes  and E is the set of edges . The set E may include both directed edges parent set siblings set linear equations, as follows:k and j.A structural equation model (SEM) , 19 is aincluded , we focuUnder such a model, the dependence structure among genes provided by pathways in biological database with directed and/or bi-directed edges  , 21, intX to V, and suppose that V(x) and V(y) are the sets of exogenous  and endogenous  genes, respectively. The covariances, SEMgsa procedure adds a binary group (treatment or disease class) node labeled mentclass2pt{minimX may affect the mean gene expression values, but not their regression paths or covariances. In the R package SEMgraph these coefficients can vary by experimental or disease group via two-group SEM [j-th node) determine the effect of the group on the j-th node, while the common path coefficients Comparing Eq.  with Eqsroup SEM , 17, butThis type of SEM enables the identification of differentially expressed genes (DEGs) if genes show a statistically significant variation in their activity  in the experimental group respect to the control one. In other terms, a test for the null value of the path, SEMgraph. Specifically, the core of model fitting in SEMgsa function relies on the residual iterative conditional fitting (RICF), an efficient iterative algorithm that can be implemented through least squares, with the advantage of clear convergence properties [p values for testing null hypothesis in Eqs. (Maximum likelihood estimates (MLEs) of the paths ..\documenp values of group effect , using the moment based approximation proposed by [p values are extracted from the normal distribution with mean and standard deviation estimated from the empirical distribution.posed by . Once thp values, overall group perturbation on pathway genes can be computed based on the Brown’s method for combining non-independent, one-sided significance tests [p values: p value is obtained from the chi-square distribution with new degrees of freedom f and a correction factor c to take into consideration the correlation among p values, resulting in From node-wise ce tests . The metp values in one-sided p values is performed according to the sign of the path  as the column sum of weights across each source node. The pathway is marked as down-regulated if the total sum of the node weights is less than 1, and otherwise as up-regulated.p values determines whether the node perturbation is activated or inhibited; if positive, the node perturbation is described as activated, and otherwise as inhibited.The minimum among the It is possible to determine the direction (up or down) of gene perturbation by combining these two quantities. In cases compared to the control group, an up- or down-regulated gene status that is associated with node inhibition shows a decrease in activation (or an increase in inhibition). In contrast, up- or down-regulated gene status, associated with node activation, leads to an increase in activation (or decrease in inhibition) in cases relative to control group.The example code of the function SEMgsa is as follows.SEMgsag); gene expression data where rows represent subjects, and columns graph nodes (data); a binary vector with 1 for cases and 0 for control subjects (group). Optional inputs are the multiple testing correction method (method), and the significance level  for DEGs selection, and the number of randomization replicates for RICF algorithm , is applied on data, considering the group binary vector and the number of specified randomization replicates. More specifically, SEMrun takes as input a single graph as an igraph object and has several additional inputs, including: a numeric value fit indicating the SEM fitting mode, where algo is used for model fitting, in this case fitting is done via RICF.The first step in SEMgsa workflow is to compute the weighted adjacency matrix of each pathway, obtain the sum of node weights and flag the pathway as up- or down-regulated. This is crucial to obtain the overall pathway perturbation status at the end. Then RICF algorithm of R package The covariance matrix could not be semi-definite positive in the situation of large dimensionality (n.variables > n.subjects), making it impossible to estimate the parameters. When this occurs, regularization of the covariance matrix is enabled. SEMrun uses internally two functions of the corpcor R package: the is.positive.definite tests if the observed covariance matrix is positive, and if the response is equal to FALSE, the function pcor.shrink implements the James-Stein-type shrinkage estimator  to regulp values are extracted from model fitting object together with the number of DEGs obtained adjusting p values with the chosen correction method while testing the specified level of alpha. Then, a data frame of combined SEM results is obtained putting together node-wise p values with Brown’s method and Bonferroni’s correction.Node-wise group effect gsa, a dataframe reporting size (No.nodes), DEGs number (No.DEGs), pathway perturbation status (pert), Brown’s combined p value of pathway node activation (pNA), Brown’s combined p value of pathway node inhibition (pNI) and the Bonferroni combination of them (PVAL). ADJP refer to the pathway combined p value adjusted for multiple tests with Bonferroni correction, i.e., K is the number of the input pathways.DEG, a list with DEGs names for each pathway, selected with p value < alpha after the multiple correction procedure with one of the method available in R function p.adjust. By default, method is set to “BH”  and the significance level alpha to 0.05.To read more about SEMgsa function, in terms of description, usage, function arguments and value, refer to https://rdrr.io/cran/SEMgraph/man/SEMgsa.The output of SEMgsa is represented by a list containing two objects with the following information for each pathway in the input list:SEMgraph package as a list of 225 edge weighted igraph objects corresponding to the KEGG pathways extracted using the ROntoTools R package [Coronavirus disease (COVID-19) RNA-seq expression data from   KEGG pathway according to its name. The latter is a graph with 232 nodes and 208 edges, including five components and 109 sigleton (i.e. node degree = 0). The maximum subgraph consists of 54 nodes and 83 edges. This pathway was subsequently labeled as target pathway and its p value and rank were further investigated for assessing the sensitivity and prioritization ability of the methods [Coronavirus disease of 2019 (COVID-19) is a highly contagious respiratory infection that is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple probes for each Entrez gene ID were first eliminated. The empirical Bayes technique, as implemented in the  package , was useHochberg . This pr methods , 35.SEMdata package as ftdDNAme, a list of two objects: a data matrix of 256 rows (subjects) and 16,560 columns (genes) containing the value of the first principal component of DNAme levels, obtained applying a principal component analysis to methylated CpG sites within the promoter region, for each gene (genes with unmethylated CpGs in both conditions were discarded); and a binary group vector of 105 FTD patients (1) and 150 healthy controls (0). Unlike COVID-19 data, FTD has not a unique KEGG pathway associated to its name. According to KEGG BRITE database, the term Frontotemporal lobar degeneration  is associated to 6 KEGG pathways: MAPK signaling pathway (hsa04010), Protein processing in endoplasmic reticulum (hsa04141), Endocytosis (hsa4144), Wnt signaling pathway (hsa04310), Notch signaling pathway (hsa04330), and Neurotrophin signaling pathway (hsa04722). We can use the SEMgsa function to apply gene set analysis (GSA) on a collection of networks, exploring the 6 selected FTD pathways as target ones. Thus, the ability of GSA methods will be investigated on 6 target pathways, combining results in terms of median p values and ranks for readability purposes. We refer the reader to the Additional file Frontotemporal Dementia, a neurodegenerative disorder characterized by cognitive and behavioural impairments . We willK pathways has been chosen to be dis-regulated. Next, a pre-specified number (s) of genes within each dis-regulated pathway was chosen to be altered (up or down) according to a topological measure  and different mean signals : Vascular smooth muscle contraction (hsa04270), Platelet activation (hsa04611), Toll-like receptor signaling pathway (hsa04620), NOD-like receptor signaling pathway (hsa04621), RIG-I-like receptor signaling pathway (hsa04622), JAK-STAT signaling pathway (hsa04630), Natural killer cell mediated cytotoxicity (hsa04650), Fc gamma R-mediated phagocytosis (hsa04666) and Leukocyte transendothelial migration (hsa04670).We have to alter We looked at three different methods for reflecting pathway topology in order to assign impacted genes to the deregulated pathways: betweenness, community and neighborhood, following the practice in , 38.betweenness deregulation design. According to the community deregulation design, we located modules with dense connections between module nodes and spare connections between module nodes. Given the division of vertices in each community, the 10 affected genes are then randomly sampled from the community with the highest proportion of members. In the neighbourhood deregulation procedure, the vertices not farther than a given limit from another fixed vertex are called the neighborhood of the vertex. After computing the neighbourhood of order 2, we sampled the 10 vertices from the neighbourhood with the biggest size.The number of all shortest paths in a directed graph that pass through a given node is known as its betweenness. The top 10 highest scoring betweenness nodes were used to choose affected genes in the s has been selected as ‘dysregulated’. We decided to fix the number of affected genes to S is not fixed, but changes according to the chosen topology design. As a note, given the random sampling within the community deregulation design, the sampled genes change according to the specified seed. In the end, a weight representing the up- or down-regulation of genes was kept along with the Entrez gene ID of the affected genes. The weighted adjacency matrix of each pathway comprises a column regarding the sum of weights over each source node, which can be used to determine up- or down-regulation (taken from the KEGG database). The pathway is marked as down-regulated or up-regulated depending on whether the total sum of node weights is less than 1. Each gene’s weight has been extracted in order to obtain an up or down mean signal, which better reflects variations in the expression of the impacted genes between the control and treatment groups.Within each of the associated pathways plus the target pathway of interest, an equal number of genes After identifying the subset of DEGs according to the chosen topology design, pathways with a number of dysregulated genes S inside the To summarise, for all topology design, the total altered genes igraph objects corresponding to the chosen KEGG pathways.The simulated expression matrices were directly supplied to SEMgsa and DEGraph, topologyGSA, NetGSA, PathwayExpress and ORA algorithms together with the list of p values. ORA and PathwayExpress test the competitive null hypothesis of whether the genes in the set of interest are at most as often DE as the genes not in the set, instead SEMgsa, DEGraph, topologyGSA and NetGSA test the self-contained null hypothesis of no genes in the set of interest are DE. Another major difference among these methods regards the input requirements. There is a high sensitivity to the p value cutoff because all techniques based on testing the competitive null hypothesis must identify DE genes based on a pre-specified threshold of corrected p values. Without making any arbitrary decisions on the list of DE genes, all self-contained tests directly employ expression data.Table Common covariance matrix: DEGraph, TopologyGSA and NetGSA are multivariate hypothesis testing-based approaches. The vectors of gene expression levels in each (sub)pathway are assumed to be random vectors with multivariate normal distributions, DEGraph was developed to perform a two-sample test of means while taking the topology of the genes into account. It considers a special case where both covariance matrices are expected to be equal, TopologyGSA begins by transforming the pathway network graph into a direted acyclic graph (DAG) and then to its “moral” graph by connecting all parent nodes of a vertex and removing the edge directionality. The moral graph is then decomposed into cliques (i.e. subsets of nodes in the graph for which each pair is connected by an edge). A set of two hypothesis tests is applied to compute the statistical significance of the impact on a given graph. The first test determines if the inverses of the covariance matrices are equal. The second hypothesis test examines the equality of the distributions’ means. To reduce the computational burden of this method, we consider a special case like DEGraph by assuming that the covariance matrices are expected to be equal and we employed the hypothesis test only for the mean of the distributions. When the covariance matrices are equal, the test of differential expression for the means is performed through a multivariate analysis of variance (MANOVA), equivalent to Hotelling’s K networks may differ, and K take into account a linear mixed effects model for each condition. The underlying biological network is encoded in the 0–1 adjacency matrix, With NetGSA, the Hotelling’s-test: this test represent a a natural generalisation of the t-test for testing the difference between multivariate means of two populations. D are very close to p values for testing null hypothesis are computed by randomization of group labels, as performed in SEMgsa function, allowing more accurate p value estimation with no need of a large number of permutations.ument}D2 , but theFor computational purposes, two main aspects have been addressed within the main analysis, mostly regarding DEGraph, topologyGSA and NetGSA:p values for the target pathway and prioritization refers to the ability of ranking close to the top the gene sets that are indeed relevant to a given condition.In the benchmark data analysis, all methods were evaluated according to (i) sensitivity and (ii) prioritization. The sensitivity refers to the ability of producing small Performance of GSA methods within each simulation run has been evaluated looking at (i) type I error and (ii) power. When a true null hypothesis is rejected, a type I error, also known as a false positive, occurs, whereas the power assesses the probability of a test successfully rejecting the null when the alternative hypothesis is true. Power comparisons are only useful if the tests have appropriate type I error control. Pathways associated with COVID-19 disease  didn’t exceed the level of 0.05. COVID-19 pathway was identified only by SEMgsa together with topologyGSA and NetGSA. The lowest p value was reported by SEMgsa together with topologyGSA  is reported by SEMgsa, confirming the high sensitivity of our method. Results disaggregated for the six pathways related to FTD are shown in Additional file Significance of target pathway was detected if the adjusted p value receive the same rank. The gene sets with the lowest p value are ranked first, and so forth. Relative rankings are computed by dividing the absolute rank by the number of unique p value categories and multiplied by 100 . Among all methods, SEMgsa perform the best with a 10th position for the COVID-19 pathway and a median ranking of 39 for FTD. Same ranking for FTD is reported by DEGraph but with a p value larger than threshold (0.05). Similar performance is reported by topologyGSA for Coronavirus data, with a ranking of 13 but with poor ranking for FTD (58). Despite the good sensitivity, NETgsa shows poor prioritization results.Table ORA method has poorer performance in terms of both sensitivity and prioritization because this type of approach only works when the magnitude of mean changes between conditions is large.We summarize the relative performance of all methods based on false positive rate and power results on the subset of pathways associated to the target disease see “” sectionFigure Statistical power of different methods has been investigated in terms of average proportion of simulations where the method correctly rejects the null hypothesis of no enrichment. The higher the power, the better. Figure Among the methods compared, SEMgsa has the best overall performance.Topology-based approaches exhibit greater statistical power in finding pathway enrichment, according to earlier studies , 40. HowExploiting pathway information: Existing methods have specific input requirements about pathway topology. TopologyGSA, for instance, only works for pathways whose topology is a DAG and whose size is less than the required number of samples in the two conditions/groups. If a pathway has multiple connected components, DEGraph will check to see if the means vary for each connected subgraph. Without taking into account singletons, NetGSA fits a linear mixed model for each condition. Overcoming this limitations, SEMgsa accepts as input directed and/or undirected networks that define pathway interconnectedness. Inside SEMgsa workflow, the function SEMrun maps the expression data onto the input graph corresponding to each pathway and converts it into a SEM. Node-level perturbation is evaluated according to the specified binary group variable (i.e. case/control) by fitting a “common” model to evaluate group effects on graph nodes. In this way, adding group-nodes and group-genes edges  p value smaller than a pre-specified cutoff are considered as DEGs. The adjustment process is highly dependent on the number of tests performed, for example Bonferroni adjusted p values are calculated by multiplying the original p values by the number of tests performed. SEMgsa fits a SEM model for each tested pathway. For source and singleton genes, marginal tests of traditional DEGs analysis are applied while for connections and sink genes conditional testing, given the parents are used. When there is a direct group effect, conditioning increases power and reduces gene variability. As a result, the topological structure of the pathway improves the precision of the inference. Furthermore, the significance of node-level perturbation is computed within each pathway and the adjustment procedure for p values is less stringent given the smaller number of tests. This choice allows to obtain higher sensitivity to pathway perturbation. Thus, from the output of SEMgsa we can extract a seed list of DEGS for each pathway that can be useful to discover novel disease-associated interactions. A further step could be to extract a Steiner tree, mapping the DEGs on the union of the KEGG pathways and finding a connected subgraph such that the additional nodes (Steiner or connector nodes) connecting seed nodes  minimize the sum of the weight of every edge in the subgraph . Then, fitting the filtered active (perturbated disease) module with SEMrun, we can obtain a perturbed backbone regarding the disease of interest, where important connectors or clusters of genes can be identified [entified , 17.Index for overall pathway perturbation: Among the existing methods, only SPIA reports the direction in which the pathway is perturbed (activated or inhibited), exploiting a posteriori pathway information obtained from hypothesis testing. Like SPIA, SEMgsa outputs a column summarising overall pathway perturbation, but combining also a priori information obtained from biological databases (up- or down-regulation of genes derived from KEGG) to a posteriori information obtained from the analysis of gene expression data (node perturbation obtained from SEMgsa). The combination between these flow of information allows to better define the direction of gene perturbation. Table SEMgsa represent a topological based and self-contained hypotesis method, in line with NetGSA, DEGraph and topologyGSA. Three main points make SEMgsa more valuable for users than existing GSA methods:We have shown that SEMgsa is easily accessible to common users and provides robust results under several experimental conditions. It obtains external pathway information solving the problem common to many topology-based methods but offering better statistical power and prioritization results, while also controlling for type I error.We believe that SEMgsa can be a valuable tool for practitioners, also when undertaking complex pathway enrichment analysis.SEMgraph package)Project name: SEMgsa: Platform independentProgramming language: RLicense: GNU General Public License version 3 or higher  and frontotemporal dementia (FTD).


This study is aimed at predicting and contrasting the mechanisms of artemisinin (ARS), dihydroartemisinin (DHA), artesunate (ART), artemether (ARM), and arteether (ARE) in the treatment of osteoporosis (OP) using network pharmacology and molecular docking.  The targets of ARS, DHA, ART, ARM, and ARE were obtained from the SwissTargetPrediction. The targets related to OP were obtained from the TTD, DrugBank, Genecards, and DisGeNet databases. Then, the anti-OP targets of ARS, DHA, ART, ARM, and ARE were obtained and compared using the Venn diagram. Afterward, the protein-protein interaction (PPI) networks were built using the STRING database, and Cytoscape was used to select hub targets. Moreover, molecular docking validated the binding association between five molecules and hub targets. Finally, GO enrichment and KEGG pathway enrichment were conducted using the DAVID database. The common pathways of five molecules were analysed.  A total of 28, 37, 36, 27, and 33 anti-OP targets of ARS, DHA, ART, ARM, and ARE were acquired. EGFR, EGFR, CASP3, MAPK8, and CASP3 act as the top 1 anti-OP targets of ARS, DHA, ART, ARM, and ARE, respectively. MAPK14 is the common target of five molecules. All five molecules can bind well with these hubs and common targets. Meanwhile, functional annotation showed that MAPK, Serotonergic synapse, AMPK, prolactin, and prolactin signaling pathways are the top 1 anti-OP pathway of ARS, DHA, ART, ARM, and ARE, respectively. IL-17 signaling pathway and prolactin signaling pathway are common anti-OP pathways of five molecules. Besides, GO enrichment showed five biological processes and three molecular functions are common anti-OP mechanisms of five molecules.  ARS, DHA, ART, ARM and ARE can treat OP through multi-targets and multi pathways, respectively. All five molecules can treat OP by targeting MAPK14 and acting on the IL-17 and prolactin signaling pathways. Osteoporosis (OP) is a bone metabolic disease common in the elderly . OP patiArtemisia annua L, which has several derivatives such as dihydroartemisinin (DHA), artesunate (ART), artemether (ARM), and arteether (ARE) [rtemisia annua L is mentioned to improve OP symptoms, including limb pain and joint inflexibility. Therefore, the anti-OP effect of ARS and its derivatives has gained wide attention. In vivo, Artemisia annua ethanol extract, ARS, and DHA can inhibit bone loss in ovariectomized mice [in vitro. ARS, DHA, ART, and ARM can impair RANKL-induced osteoclast differentiation by hampering the expression of NFATc1 [κB activation and controlling the mitochondria-dependent apoptosis pathway [Artemisinin (ARS) is the first-line antimalarial drug acquired from er (ARE) , 6. In azed mice , 8. DHA zed mice . To furtf NFATc1 –12. Besi pathway . ART can pathway , 14. MeaAs mentioned above, ARS and its derivatives can play the anti-OP role through similar and unique mechanisms. A comprehensive and systematic mechanism for treating OP is still not widely reported. This study investigates the potential mechanism of ARS and its derivatives in treating OP using network pharmacology, which provides a reference for further experimental research.https://pubchem.ncbi.nlm.nih.gov/). The SMILES were input into SwissTargetPrediction (http://www. swisstargetprediction.ch/) to obtain potential targets (probability >0) of ARS and its derivatives. Homo sapiens was selected as the target organism.The canonical simplified molecular input line entry specification (SMILES) and the structure of ARS and its derivatives were obtained from PubChem database (http://database.idrb.cqu.edu.cn/TTD/), the DrugBank database (https://www.drugbank.ca/), Genecards , and DisGeNet (https://www.disgenet.org/). The UniProtKB ID was used to verify all targets.OP-connected genes were acquired from these online databases: Therapeutic Target Database (TTD http://jvenn.toulouse.inra.fr/app/example.html). Moreover, a Venn diagram compared the potential anti-OP targets of five molecules.The overlapping of drug-related and OP-associated targets might be the potential targets for drugs against OP. Anti-OP targets of ARS, DHA, ART, ASM, and ARE were acquired by Venn online tool (https://cn.string-db.org/). The protein type was set to Homo sapiens, and the minimum required interaction score was medium (0.4). The PPI results were moved to Cystoscope (3.7.2) to screen the hub targets with a high degree.Protein-protein interaction (PPI) networks were constructed to obtain the interaction between targets by inputting anti-OP targets of each molecule into the STRING database, respectively, (http://www.pdb.org/). Autodock tools software aids in the process of the receptors and ligands. Autodock Vina was used for molecular docking and to acquire binding energy. Finally, the graphical software PyMOL was used to illustrate the docking of the receptor and ligand.The five molecules' common and hub targets were verified by docking with the five molecules. The structure data files (SDFs) of five molecules were acquired from PubChem and converted to protein data bank (PDB) files via open Babel. The 3D structures of target proteins were acquired from the PDB (https://david.ncifcrf.gov/). A P value less than 0.05 were considered significantly enriched. The bubble diagrams were constructed to analyze the top 10 biological pathways (BPs), cell localization (CC), molecular function (MF), and the top 20 KEGG pathways that were significantly enriched. The common terms of five molecules of the specific terms of each molecule were obtained in excel.Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of each molecule was conducted by the Database for Annotation, Visualization and Integrated Discovery . Different colors were used to represent targets of different molecules. The genes enriched by five molecules in the common pathway were presented on one map.The maps of the common KEGG pathways were acquired from the KEGG database (The canonical SMILES and molecule structure of ARS, DHA, ART, ARM, and ARE acquired from PubChem are shown in 1504 OP-related targets were obtained from TTD, Genecards, DrugBank, and DisGeNet databases using the keyword “osteoporosis”. The overlapping targets of molecules and OP were considered as targets for treating OP. 28, 37, 36, 27, and 33 targets of ARS, DHA, ART, ARM, and ARE were identified to treat OP. The Venn diagrams are generated as Figures As shown in MAPK14 is the only common target of 5 molecules. EGFR, EGFR, CASP3, MAPK8, and CASP3 are the top1 hub anti-OP targets of ARS, DHA, ART, ARM, and ARE, respectively. Molecular docking is conducted to investigate the binding of five molecules with EGFR, CASP3, MAPK8, and MAPK14. GO enrichment of five molecules was performed, respectively, to determine the anti-OP mechanism of each molecule. The common GO terms can uncover the common mechanism of five molecules. A total of 5 BP and 3 MF were identified, and no CC was shared by all five molecules .P values were plotted in the bubble chart, as shown in KEGG pathways enrichment was conducted to predict the potential anti-OP pathways of five molecules, respectively. The KEGG pathway involved in human disease section was removed because OP was caused by basic biological dysfunctions .  FigureOn the other hand, the prolactin signaling pathway and IL-17 signaling pathway were found to be shared by five molecules, a common anti-OP mechanism of five molecules. In addition, specific pathways of each molecule have been found. The calcium signaling pathway is the ARS-specific pathway, and apoptosis is the ARE-specific pathway. DHA has 15 specific pathways, such as parathyroid hormone synthesis, secretion and action, sphingolipid signaling, and arachidonic acid metabolism. ART has 8 specific pathways, such as renin secretion, glucagon signaling pathway, and thermogenesis. More details on the common and specific KEGG pathways are shown in All five molecules exhibit an anti-OP effect by prolactin signaling pathway and IL-17 signaling pathway, acting on different genes of the two pathways. In the IL-17 signaling pathway, ARS target at HSP90AA1, MAPK8, and MAPK14; DHA target at MMP1, MAPK1, MAPK14, PTGS2, MMP9, and MAPK3; ART target at MMP1, CASP3, MAPK1, MAPK14, and MMP9; ARM target at MAPK8, MAPK14, and PTGS2; and ARE target at MAPK8, MMP13, CASP3, MAPK14, and PTGS2. In the prolactin signaling pathway, ARS target at MAPK8, MAPK14, and ESR2; DHA target at PIK3CA, MAPK1, JAK2, MAPK14, CYP17A1, and MAPK3; ART target at PIK3CA, MAPK1, MAPK14, and ESR2; ARM target at STAT5B, MAPK8, JAK2, MAPK14, and ESR2; and ARE target at STAT5B, MAPK8, PIK3CA, JAK2, MAPK14, and ESR2. in vivo and in vitro, but detailed mechanisms are unclear [ARS and its derivatives have been used to treat many diseases, such as cancers, viral infections, inflammatory, and autoimmune diseases –19. ARS  unclear –14. Netw unclear . MoreoveIt is found that ARS, DHA, ART, ARM, and ARE act in an anti-OP role through multi targets and multi pathways, respectively. In our study, EGFR is the most noticeable anti-OP target of ARS and DHA, CASP3 is the most important anti-OP target of ART and ARE, and MAPK8 is the most promising target of ARM. Previous studies have reported the importance of EGFR, CASP3, and MAPK8 in the pathogenesis of OP. EGFR can regulate the proliferation and differentiation of osteoblast and induce osteoclast differentiation by upregulating RANKL expression , 22. TheKEGG analysis suggested that the MAPK signaling pathway, serotonergic synapse, AMPK signaling pathway, prolactin signaling pathway, and prolactin signaling pathway are the top 1 anti-OP pathway of ARS, DHA, ART, ARM, and ARE, respectively. These pathways are known as OP-related pathways. MAPK signaling pathway is a classical signaling pathway for regulating bone metabolism . ExperimThere are a few common anti-OP mechanisms among the five molecules. MAPK14 is the common target of five molecules, regulating the expression of OPG and promoting the proliferation and differentiation of osteoclast progenitors , 37. AllInterestingly, among the five molecules, DHA can act on the most targets, regulate the most GO MF, and involve the most GO BP and KEGG pathways. Besides, ARS, DHA, ART, ARM, and ARE have specific targets, BP terms, and KEGG pathways.There were a few shortcomings in this study. Due to the limitations of network pharmacology, the dose-effect relationships of ARS, DHA, ART, ARM, and ARE are required to explore in additional studies. The anti-OP mechanisms of five molecules also need further experimental validation. However, this study not only provides a basis for subsequent experimental verification but provides an example for exploring the mechanism of similar compounds using network pharmacology.In conclusion, ARS, DHA, ART, ARM, and ARE act in an anti-OP role through multitargets and multipathways, respectively. DHA is a prominent molecule due to its many targets and pathways. All five molecules can treat OP by targeting MAPK14 and acting on the IL-17 and prolactin signaling pathways.


Angelica dahurica Radix and Ligusticum chuanxiong Rhizoma herb pair (ALHP) has been used to treat migraines for centuries in traditional Chinese medicine (TCM). However, the physiological mechanisms of migraine treatment have not yet been elucidated. In this study, a total of 50 hub targets related to the effect of 28 bioactive compounds in ALHP on anti-migraine were obtained through network pharmacology analysis. GO and KEGG analyses of the hub targets demonstrated that ALHP treatment of migraines significantly involved the G-protein-coupled receptor signaling pathway, chemical synaptic transmission, inflammatory response, and other biological processes. According to the degree of gene targets in the network, ACE, SLC3A6, NR3CI, MAPK1, PTGS2, PIK3CA, RELA, GRIN1, GRM5, IL1B, and DRD2 were found to be the core gene targets. The docking results showed a high affinity for docked conformations between compounds and predicted targets. The results of this study suggest that ALHP could treat migraines by regulating immunological functions, diminishing inflammation, and improving immunity through different physiological pathways, which contributes to the scientific base for more in-depth research as well as for a more widespread clinical application of ALHP. Migraines are a common neurological disorder characterized by desperate throbbing unilateral headaches and are related to phonophobia, photophobia, nausea, and vomiting. The  A migraine is a disabling primary headache that affects approximately 15% of the general population . AccordiA. dahurica radix and L. chuanxiong rhizoma with a weight proportion of 4:1. It has also been approved by the China State Food and Drug Administration (statement number Z20000011) to treat symptoms such as stuffy nose, runny nose, and headaches since 2000 [A. dahurica Benth. et Hook. belongs to a perennial Apiaceae plant found abundantly in Taiwan, Korea, China, Japan, and Russia [A. dahurica radix have been known for thousands of years [L. chuanxiong Hort. is a commonly used traditional medicine for stimulating blood circulation and eliminating stasis in the clinical practice of TCM [L. chuanxiong rhizoma can be utilized to foster qi flow, blood circulation, wind expelling, and pain relief. It is often used to treat migraines, irregular menstruation, and rheumatism [Based on the TCM theory, migraines belong to the category of disease resulting from “Head Wind”. In this regard, wind phlegm, deficiency syndrome, and blood stasis syndrome are also considered the primary pathophysiological mechanisms of migraines . Therefonce 2000 . The radd Russia ,17,18. Tof years . This efe of TCM . Accordieumatism . In TCM, the formula, which can be developed using one or several herbal components, contains many active ingredients. One ingredient might target one or multiple genes, proteins, and pathways in the pathogenic mechanism of a disease. Therefore, traditional herbal formulas can lead to an integrated or synergistic effect that is suitable for treating complex diseases. Network pharmacology is a recently developed method based mainly on the theory of systems biology and computer simulation technology . The netIn summary, this study aimed to exploit the network pharmacology approach to discover the potential bioactive compounds, core targets, and signaling pathways involved in the anti-migraine activity of ALHP. The results of this study may provide a theoretical basis for the molecular mechanism of ALHP in migraine treatment in future studies. A. dahurica radix and 6 compounds of L. chuanxiong rhizoma . Four other compounds, daucosterol [A. dahurica and L. chuanxiong, we obtained 655 targets corresponding to the compounds. The full names of the targets are displayed as gene symbols using the online UniProt database. The detailed data are shown in In accordance with the cut-off criteria of OB ≥30% and DL ≥0.18, 24, the main compounds of ALHP were obtained from the TCMSP database, including 19 compounds of costerol , feruliccosterol ,32, and costerol , which dRemoving the targets with a relevance score lower than twice the median in the GeneCards database and then the duplicated targets between the GeneCards, OMIM, and DisGeNet databases induced a total of 979 targets that were considered candidate therapeutic targets . The targets of ALHP were intersected with those of migraines; 131 overlapping targets were determined , and a VThe efficacy of TCM prescriptions underlies the synergistic effect of various compounds in different herbs on multiple targets involved in a disease. Insight into the effects of compounds in ALHP on the target proteins of migraines may help clarify the mechanism of the synergistic effect and the potential mechanism of ALHP for migraine treatment. Therefore, the herb–compound–target–disease network associated with ALHP and migraines was analyzed using the Cytoscape software . The construction of the PPI network was implemented using the STRING web server. A total of 131 overlapping targets were entered into the STRING web server to yield a network with 131 nodes and 967 edges A. Thereap < 0.01, FDR < 0.05) included 218 biological processes, 46 cellular components, and 57 molecular functions  were obtained from the KEGG pathway analysis using DAVID. The elimination of obviously irrelevant KEGG pathways was performed, such as “pathways in cancer,” “Chagas disease,” and “hepatitis B”. The top 20 KEGG pathways with ranked p values were chosen and are shown in Sixty KEGG pathways  serves a primary role in stimulating inactive angiotensin I to active angiotensin II, a vasoconstrictor. Vasoconstrictor were early proved to cease migraine attacks ,42. The In terms of the pathway to further determine the therapeutic mechanisms of the ALHP formula, our study focused on the canonical KEGG pathways possibly linked to anti-migraine treatment and prophylaxis. In the serotonergic synapse pathway, the intracellular network cascade is triggered by serotonin, resulting in repressive or excitatory neurotransmission. The dispersion of serotonin receptors occurs in the brain, pain-signaling circuits, and cranial blood vessels. Anti-migraine therapies have been used to modulate serotonin receptors . These pThis study hypothesized that the anti-migraine effect of ALHP may be exerted mainly via the regulation of neuroactive ligand–receptor interaction, pathways of neurodegeneration—multiple diseases, serotonergic synapses, cAMP, and calcium signaling pathways. In addition, as holistic medicine, the anti-migraine mechanism of ALHP possibly acts through the NF-kappa B, TNF, cAMP, HIF-1, Toll-like receptor, and calcium signaling pathways to moderate the neurovascular systems and through neuro-inflammation and pain-related proteins, which produces a synergistic effect to relieve the burden of migraines.The workflow of network pharmacology approach included these steps: (1) Compounds of ALHP were collected using the database of medicinal herbs and text mining. (2) Information about gene targets related to migraine disease was also retrieved in free and updated databases of human gene and diseases. (3) The overlapping targets were determined using a Venn diagram. (4) Topology analysis including the PPI analysis and network construction was carried out. (5) Core targets and compounds were screened and determined with threshold criteria of degree, closeness, and betweenness centrality. (6) GO and KEGG pathway analysis and molecular docking assay were performed on the potential compounds and core gene targets. The process of network pharmacology is described and summarized in https://tcmspw.com/tcmsp.php (accessed on 02 November 2021)). Second, oral bioavailability (OB) and drug-likeness (DL) were utilized to select the potential active compounds, and their threshold values were set to OB ≥ 30% and DL ≥ 0.18, as previously described [Angelica dahurica” or “A. dahurica” or “Ligusticum chuanxiong” or “L. chuanxiong” AND “migraine” or “headache”, were also collected. After combining and removing redundant compounds from two collection methods, the remaining compounds were selected for later steps. Finally, the most likely biological targets of the output compounds were acquired from the Swiss Target Prediction (http://swisstargetprediction.ch/ (accessed on 18 November 2021)) [First, the compounds of the two medicinal herbs in ALHP were collected from the TCM Systems Pharmacology Database ) .https://www.genecards.org (accessed on 20 November 2021)), DisGeNET (https://www.disgenet.org/home/ (accessed on 20 November 2021)), and OMIM (https://omim.org (accessed on 23 November 2021)). The keyword “migraine” was entered and searched for in each database. The GeneCards database, which was automatically mined and integrated from 150 web sources, provides user-friendly and comprehensive information regarding disease targets annotated and predicted in the human species. The wealth of GeneCards annotation was exploited with the GeneCards Inferred Functionality Score (GIFtS) algorithm to yield scores to predict the degree of functionality of the target. Based on the general criteria of GeneCards Inferred Functionality Score (GIFtS), the target with a score ≥30 was identified as the criteria target [We collected targets related to migraines from three data sources: GeneCards ). The herb–compound–target–disease network was established and visually displayed using Cytoscape software ) with information input formats such as source node, target node, and source node attribute. The overlapping targets from the two sets of targets of compounds and diseases were determined using the Venny tool (https://string-db.org/ (accessed on 14 December 2021)), and a protein–protein interaction (PPI) network was constructed with the following screening conditions: the species as “Homo sapiens”, the required interaction score at the level of medium confidence (0.400), and other parameters in default mode [The overlapping targets were imported into the STRING database ) [Gene Ontology (GO) functions and KEGG signaling pathways with potential targets were enriched using the Database for Annotation, Visualization, and Integrated Discovery ) . DAVID, http://www.rcsb.org/ (accessed on 3 February 2022)). In another way, the 3D model of protein, based on the amino acid sequence from UniProt database [https://www.uniprot.org/ (accessed on 2 December 2021)), was also built via the online server SWISS-MODEL (https://swissmodel.expasy.org/ (accessed on 3 February 2022)) and validated using the Verify3D Structure Evaluation Server (https://www.doe-mbi.ucla.edu/verify3d/ (accessed on 3 February 2022)) [https://pymol.org (accessed on 9 February 2022)) was utilized. The format of the receptor and ligand was transformed into pdbqt format via AutoDockTools 1.5.6 software. Active-binding pockets were identified. Subsequently, molecular docking was performed and calculated using Perl scripts in AutoDock Vina [A molecular docking study was performed to validate the association of compounds with key targets in the pathogenesis pathways in a network pharmacology study. The Avogadro program was utilized to form the 3D chemical structures of molecular ligands via the input of molecules in the SMILES format and auto-optimization function . The thrdatabase  (https:/y 2022)) ,88,89. Tock Vina . Finallyock Vina . Discoveock Vina .Using computational methods, including network pharmacology combined with molecular docking, this study revealed that the ALHP formula exerts an anti-migraine effect by regulating multiple targets and pathways in the pathogenesis of migraines. Among the components of the ALHP formula, imperatorin, ligustilide, oxyimperatorin, phellopterin, sen-byakangelicol, cnidilin, ferulic acid, senkyunolide A, senkyunone, and wallichilide were expressed in various associations in the pathophysiological pathways of migraines, which are considered as biomarkers of the formula. In addition, our study will provide a scientific basis for more comprehensive research and for a more widespread clinical application of ALHP in migraine treatment.