Some engineered nanomaterials incite toxicological effects, but the underlying molecular processes are understudied. The varied physicochemical properties cause different initial molecular interactions, complicating toxicological predictions. Gene expression data allow us to study the responses of genes and biological processes. Overrepresentation analysis identifies enriched biological processes using the experimental data but prompts broad results instead of detailed toxicological processes. We demonstrate a targeted filtering approach to compare public gene expression data for low and high exposure on three cell lines to titanium dioxide nanobelts. Our workflow finds cell and concentration-specific changes in affected pathways linked to four Gene Ontology terms  to select pathways with a clear toxicity focus. We saw more differentially expressed genes at higher exposure, but our analysis identifies clear differences between the cell lines in affected processes. Colorectal adenocarcinoma cells showed resilience to both concentrations. Small airway epithelial cells displayed a cytotoxic response to the high concentration, but not as strongly as monocytic-like cells. The pathway-gene networks highlighted the gene overlap between altered toxicity-related pathways. The automated workflow is flexible and can focus on other biological processes by selecting other GO terms. Engineered nanomaterials have become important in our daily life as they are utilized in the fields of food, packaging, cosmetics, drug and vaccine delivery, and many others . For exaThe detailed biological processes related to the toxicity of many engineered nanomaterials are not yet all fully understood ,8. Due t2). Due to its general properties, such as its photocatalytic activity and white color, it is used in various applications such as (photo)catalysis, antibacterial agents, and consumer products. Whereas TiO2 particles have been shown not to be able to penetrate through the skin, entry into the body can occur via inhalation or ingestion where it then has to pass through the gastrointestinal tract [Furthermore, a relatively well-studied and widely used nanomaterial is titanium dioxide  cells, human monocytic cells (THP1), and human epithelial colorectal adenocarcinoma cells co-cultured with HT29-MTX goblet cells (Caco-2/HT29-MTX). These cellular models represent three common areas of exposure in the human body, i.e., small airway epithelium, colon epithelium, and the innate immune system that will typically respond to particles that have entered the body. We aim to provide an overview of the dose-dependent and cell-type-specific response by focusing on toxicity-related processes. Apart from a significant increase in the activity of toxicity-related processes after exposure with a higher dosage, differences in intensity and affected processes might occur between the cell lines. Using this work as an example, we will discuss the toxicological effects of TiO2-nanobelts on the three cellular models and the importance and benefits of analysis workflows in the nanomaterial research field.In this study we re-examined a publicly available dataset generated by Tilton et al.   is shown in 2-nanobelts. While there was a smaller increase in differentially expressed genes, we found a similar increase in resulting pathways for the SAE cell line. There was also an increase in the number of genes found for the Caco-2/HT29-MTX co-culture; however, it did not directly translate to an increase in overrepresented pathways. Although overrepresentation analysis provides a great overview of all processes that are affected, it takes time to go manually over the many overrepresented pathways. Therefore, an automated method to select desired pathways, i.e., toxicity-related pathways, provides a new approach to interpreting the data.Using the differentially expressed genes from the different cellular models, an overrepresentation analysis was performed to identify altered pathways after TiOTo gain more insight into the toxicity-related processes, the overrepresented pathways were further categorized into pathways related to the apoptotic process, inflammatory response, DNA damage response, and/or oxidative stress. Often this kind of clustering of the pathways is performed manually. However, we implemented a gene-based approach. First, gene sets of four Gene Ontology (GO) terms were obtained, i.e., “apoptotic process” , “inflammatory response” , “cellular response to DNA damage stimulus” , and “response to oxidative stress” . Evidently, these processes are tightly connected. Whereas some can be causative for others, they are expected to overlap. Additional p-value < 0.05) showed the most pathways to be linked to the four GO-terms; in this case it showed 401, 250, 203, and 205 pathways overrepresented for the apoptotic process, inflammatory response, DNA damage, and oxidative stress GO-terms, respectively. The 80% and 70% gene overlap resulted in almost similar amounts of pathways compared to each other. They even resulted in 0 pathways for the inflammatory response GO-term. Moreover, it stands out that for the gene overlap approach only one pathway meets at least 50% or higher gene overlap with the oxidative stress GO-term. Additionally, it is interesting to see that an overrepresentation analysis prompts hundreds of pathways compared to just dozens when looking at gene overlap. Comparing the results of the overrepresentation analysis and various cut-offs, the at least 50% gene overlap cut-off was deemed best for our approach.To select the desired gene overlap cut-off, we compared four cut-offs with each other and an overrepresentation analysis approach to see how many pathways these would give. The cut-offs we used were 50%, 60%, 70%, and 80%. For the overrepresentation analysis we looked for overrepresentation of the pathway genes in the gene lists of the GO-terms. The results are shown in additional After this filtering step we found that, out of the 1076 pathways in the human pathway collection, 66 pathways are related to the apoptotic process, 15 are linked to inflammatory response, 28 are linked to DNA damage, and 2 are linked to oxidative stress. It is noticeable that there are only two oxidative stress pathways, i.e., Detoxification of Reactive Oxygen Species (wikipathways:WP2824 ) and Oxip-value lower than 0.05).The number of altered toxicity-related pathways is shown in 2-nanobelt exposure, and 15 for the high concentration. The THP1 cell line shows a clear toxic response activation for the high concentration with 39 overrepresented pathways, and 10 for the low concentration.The Caco-2/HT29-MTX co-culture shows for both concentrations 10 overrepresented pathways. The SAE cells show 3 for the low concentration, which could be an indication of a very small toxic response to the TiOImportantly, while molecular pathways describe a process on a detailed level, their boundaries are often not clearly defined. Pathways are not independent, and they interact with each other through shared genes or sub-pathways. 2-nanobelts. However, these results indicate that the studied processes in Caco-2/HT29-MTX co-cultures are not extensively affected by the administration of TiO2-nanobelts, for either the low or the high concentration. This finding supports the conclusion based on the cell viability assay results in the original study, which showed that there was no significant decrease in cell viability for the Caco-2/HT29-MTX co-culture for both concentrations of TiO2-nanobelts [While for both concentrations the Caco-2/HT29-MTX co-culture shows the same number of overrepresented pathways, the pathways compared between the low and high concentrations are not all the same. Of the ten pathways, three are found for both concentrations, namely IL-5 Signaling pathway , IL-2 Sanobelts .2-nanobelts, more biological processes related to toxicity are affected compared to the low concentration.For the SAE cell line, the low concentration only prompted three overrepresented pathways compared to 15 for the high concentration. Noticeable as well, only one of those three pathways was found to be overrepresented also for the high concentration, namely HDR through Homologous Recombination (HRR) or Single Strand Annealing (SSA) . This i2-nanobelts. For the low concentration the pathways HDR through Homologous Recombination (HRR) or Single Strand Annealing (SSA) , IL-6 S:WP3567, ), and Re:WP3567, ) are ove:WP3567, ), DNA IR:WP3567, ), DNA IR:WP3567, ), and DN:WP3567, ) among o:WP3567, ) and Apo:WP3567, ). This i2-nanobelts. However, among these pathways, the pathways TNF Signaling , Apopto:WP3380, ), Apopto:WP3380, ), TNF al:WP3380, ), and TN:WP3380, ) are preg NM-300 .2-nanobelts results in toxicity-related processes such as inflammation, DNA damage, and oxidative stress. The higher number of pathways that show up in the result together with the biological processes they depict indicate that the high concentration induces greater effects than the low concentration. This was also seen in the original paper where the low concentration induced a significant decrease in cell viability, while the high concentration induced an even greater decrease [Compared to the low concentration, the high concentration yields more pathways with significant overrepresentation i.e., 39 versus 10. Except for the pathway Apoptotic Execution Phase  all othdecrease . Furtherdecrease ,61.The THP1 cell line is the cellular model that has the most inflammation-related pathways in the results: four for the low concentration and nine for the high concentration. All four of the low concentrations show up in the results of the high concentration as well. These pathways are Photodynamic-therapy-induced NF-kB Survival Signaling , InterlThe focused analysis of alterations in toxicity-related processes showed differences between the three cell lines and the concentrations studied. It has been reported before that the molecular response depends on the cell type and concentration .2-nanobelts caused the biggest toxicological response to THP1 cells, as they are more sensitive to exposure compared to epithelial cell lines RLE-6TN and BEAS-2B [2 [Caco-2/HT29-MTX cells seem resilient to the exposure and show very little activation of toxicity-related processes. While for this dataset a co-culture was used, the passage of nanoparticles through the cellular barriers of Caco-2 cells has been shown to be limited, which could result in reduced uptake and therefore less toxic response . This co BEAS-2B . MoreoveAS-2B [2 .Interestingly, approximately 10% of the pathways in WikiPathways and Reactome can be categorized as toxicity-related, in other words these pathways have at least 50% gene overlap with the GO terms we selected. This highlights the fundamental cellular processes involved but could also indicate a bias in the pathway collections towards these well-studied processes. Nonetheless, these pathways are considered important pathways, hence they are studied well.To illustrate how the effects can be studied in more depth we visualized the log fold change of all six conditions on the Oxidative Stress pathway (wikipathways:WP408 ), the ApWhile the Oxidative Stress pathway shown in  pattern . These g2-nanobelts.Moreover, the Apoptosis pathway shows positive log fold changes of the CASP2 and CASP7 genes, which are involved in apoptosis execution, for the SAE and THP1 cell lines for both concentrations whereas the Caco-2/HT29-MTX co-culture shows no negative or positive log fold change ,72. FurtEnrichment analysis for gene expression data is well-established and can easily be automated to increase reproducibility . The int2-nanobelts. Using our process-level analysis based on pathway analysis in combination with the use of gene sets and network visualization, our findings support the results by S.C. Tilton et al. showing that the three cellular models, Caco-2/HT29-MTX, SAE, and THP1, show different toxicity-related responses to the exposure of TiO2-nanobelts from resilient Caco-2/HT29-MTX and SAE cells to strongly responding THP1 cells. The latter is not unexpected since the observed effects align with the biological function of these immune cells. Importantly, the approach allowed us not only to find changes in gene expression, but also to find responding molecular pathways via the pathway analysis. Additionally, it allowed us to filter the broad pathway enrichment results to a focused view on the cytotoxic processes affected. The filtering steps we included in our workflow allow a very targeted approach. This allows us to visualize and explore the interactions between responding genes, based on underlying molecular processes in greater detail and in a less time-consuming manner. The approach is suitable for quick reanalysis of datasets each time pathway databases are updated with newly discovered pathway knowledge. Moreover, this versatile approach captured in the R-script can easily be adapted to isolate other processes by using other Gene Ontology terms diverting the focus to other biological processes of interest.In this study we investigated the molecular response of three different cell lines to exposure to TiOIn this study, a published and publicly available transcriptomics dataset generated by Tilton et al.  was used2-nanobelts for 24 h in triplicate. To run the same analysis for the 1 h time point the workflow in the 1 h repository can be used [The dataset consisted of 18 samples from three human-like cellular models, i.e., Caco-2/HT29-MTX, SAE, and THP1, which were exposed to either medium (control), 10 μg/mL or 100 μg/mL TiO be used . The num be used .p-value lower than 0.05.Volcano plots depicting differential gene expression were made using the EnhancedVolcano package (version 1.4.0) for R (version 3.6.1) ,82. Genehttp://www.wikipathways.org (accessed on 28 August 2021), version 20201003, [p-value cutoff as described in the previous section. Additionally, default settings of the enricher function of the clusterProfiler package were used, except p-value and q-value  cutoff were set to 1 to include all results at this stage. This allowed us to later select the desired results based on a p-value smaller than 0.05. Minimal gene set size was set to 10 and the maximum gene set size was set to 300.Overrepresentation analysis was performed using the enricher function of the clusterProfiler package (version 3.14.3)  for R to0201003, ). The Cuhttp://geneontology.org/docs/guide-go-evidence-codes/ (accessed on 28 August 2021), see additional Within the two human pathway collections used, toxicity-related pathways were identified based on the gene overlap with the toxicity-related gene sets. The overlap was calculated by dividing the number of toxicity-related genes present in the pathway by total number of genes present in the pathway. The gene sets were retrieved from the Gene Ontology  for the following four toxicity-related GO-terms: “apoptotic process” (GO:0006915), “inflammatory response” (GO:0006954), “cellular response to DNA damage stimulus” (GO:0006974), and “response to oxidative stress” (GO:0006979) ,84. AssoAltered pathways in the enrichment analysis were filtered for toxicity-related pathways and then visualized as pathway-gene networks to portray the overlap and crosstalk between the pathways. To construct the networks, edge  and node  files were created of the altered pathways. The networks were made using the igraph R package (version 1.2.4.1) .https://github.com/laurent2207/TiO2-scripts (accessed on 28 August 2021)) and archived on Zenodo [The complete analysis is automated in R (version 3.6.3) and can easily be repeated with a new transcriptomics dataset or different selection focus. The R scripts are available on GitHub (n Zenodo .


However, the advent of pathway data has not been accompanied by an improvement in interoperability across databases, hampering the use of pathway knowledge from multiple databases for enrichment analysis. While integrative databases have attempted to address this issue, they often do not account for redundant information across resources. Furthermore, the majority of studies that employ pathway enrichment analysis still rely upon a single database or enrichment method, though the use of another could yield differing results. These shortcomings call for approaches that investigate the differences and agreements across databases and methods as their selection in the design of a pathway analysis can be a crucial step in ensuring the results of such an analysis are meaningful. Here we present DecoPath, a web application to assist in the interpretation of the results of pathway enrichment analysis. DecoPath provides an ecosystem to run enrichment analysis or directly upload results and facilitate the interpretation of results with custom visualizations that highlight the consensus and/or discrepancies at the pathway- and gene-levels. DecoPath is available at  One of the most common applications of HT technologies is gene expression profiling to simultaneously determine the expression patterns of thousands of genes at the transcription level under certain conditions  technologies have given rise to a perpetual influx of nditions . While anditions . To datenditions . Though nditions .Numerous pathway databases have been developed which aim at representing biological pathways from various vantage points . The existence of several hundreds of these databases reflects the inherent complexity and variability of biological processes that occur in living organisms . Furtheromics layers using several databases. However, performing pathway enrichment analysis using multiple databases to increase the number of pathways covered can only partially address the challenges associated with variability in results. This is because such an approach falls short of leveraging the substantial overlap of pathway knowledge across databases which could provide more comprehensive results . Finally, although DecoPath provides four default databases, it also allows users to upload gene sets and mappings such that analyses can be run on their independently curated gene sets.https://www.djangoproject.com/). This framework operates using a Model-View-Controller (MVC) architecture and was integrated with Celery (http://www.celeryproject.org) and RabbitMQ (https://www.rabbitmq.com) for asynchronous task execution. The front-end of DecoPath comprises several interactive visualizations implemented using a collection of powerful Javascript libraries, including jQuery (https://jquery.com), D3.js (https://d3js.org/) and DataTables (https://datatables.net/). Furthermore, DecoPath relies on Bootstrap 4 (https://getbootstrap.com/) for the main design of the website. The web application is containerized using Docker for reproducibility purposes and easy deployment. We strongly recommend the use of DecoPath on Chrome, Firefox or Safari browsers and on Mac or Linux operating systems.The server-side was implemented in the Python programming language using the Django framework . The hierarchy comprises eight major categories: metabolism, immune, signaling, communication and transport, cell death, disease, DNA repair and replication, and others. All pathways in the hierarchy retained their original identifiers except equivalent pathways which were merged and given unique names and identifiers. The pathway hierarchy is a directed acyclic graph with a maximum depth of 4, in which relation types between pathways can be either is-part-of or equivalent-to relations. The curation process to generate the hierarchy is described in the Supplementary Text. Periodic updates to the pathway hierarchy are made on an annual basis.The consolidation of each of the pathway databases into a pathway meta-database was conducted in order to generate a pathway hierarchy. In doing so, equivalent representations of pathways across KEGG, PathBank, Reactome and WikiPathways were combined. The pathway hierarchy contains a total of 644 pathways from these four databases and can be found at P-value arising from a one-sided Fisher’s exact test . Additionally, DecoPath enables conducting differential gene expression (DGE) analysis between groups through DESeq2 (version 1.22.2). Apart from these methods, DecoPath also provides the option to include additional pathway enrichment methods into the web application.DecoPath comprises two of the most widely used pathway enrichment methods : over react test  is lowerpendency . The secpendency , is alsohttps://decopath.scai.fraunhofer.de/, in the case of large datasets or cases where the compute capacity of the server may be insufficient depending on the type of analysis, users can install and use DecoPath in their own system. We offer two options to install DecoPath depending on the needs of the user. The first and easiest method for those unfamiliar with Django-based web applications is to install Docker and deploy the Docker container which will install required components and run the web application. Detailed instructions are provided on GitHub (https://github.com/decopath/decopath). Alternatively, DecoPath can be directly deployed following the instructions in the GitHub repository.Although we provide a freely available instance of DecoPath at Computation time is dependent on the type of analysis, size of the datasets as well as the device specifications. ORA can be run on a gene list on a timescale of seconds and requires the relatively lowest usage of memory. A DGE analysis task has a timescale of several minutes, while GSEA on a typical expression dataset with two experimental groups and four databases can also be done within minutes with a dual-core Intel Core i5 CPU and 16 GB RAM.https://gdc.cancer.gov) portal through the R/Bioconductor package, TCGAbiolinks  was performed between normal and tumor samples to derive a gene list to conduct ORA. This final list of genes was restricted to genes that exhibited an adjusted P-value < 0.05. Specifications of the parameter settings for ORA and GSEA are listed in Using each of the available enrichment methods, we demonstrate a typical workflow in DecoPath with the The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset . Gene ex 2.16.3; ) on 4 AuHere, we describe the DecoPath web application. A typical workflow of the web application involves the submission of an experiment, generation of results, and the subsequent exploration and visualization of these results Figure . In the Once a user has logged into DecoPath, on the Homepage, the input form allows them to upload their files and select parameters to run different analyses or upload results from them Figure . For useThese pathway enrichment methods can also be supplemented by DGE analysis to generate visualizations and identify genes that are differentially expressed according to a fold change cutoff. In order to run DGE analysis, un-normalized read counts in the form of a matrix of integer values is required, as is a design matrix, analogous to the one required for GSEA. For each of these analyses, gene identifiers should be in the form of HUGO Gene Nomenclature Committee (HGNC) symbols. Alternatively, users can opt to download gene set files for pathway databases included in DecoPath, run GSEA, ORA and/or DGE analysis, and upload the results of the analysis to the website. By directly uploading the results, users can also analyze the results of alternative enrichment methods such as EnrichNet  and SignOnce users have submitted their query, they are directed to the Experiments page where they can view the status as well as details of their experiments, and explore and visualize their results Figure . To inteThe first visualization summarizes the consensus results of pathway enrichment analysis on multiple databases. For each pathway (row), the table shows the concordance across databases, reflected in terms of the significance value, specifically for ORA, and both the significance value and directionality of the normalized enrichment score (NES) for GSEA Figure . Using tWe conducted a case scenario to investigate the results for ORA and GSEA using four pathway databases on the TCGA-LIHC dataset. Among the pathways enriched in ORA which could be found in more than one pathway database, we found 88 concordant pathways and 41 contradictory ones. Similarly, the results of GSEA revealed 70 concordant and 45 contradictory pathways. Among the contradictory pathways we observed in GSEA, the majority of contradictions pertained to whether or not the pathway was significantly enriched, while 12 pathways also differed in the sign of the NES (i.e. the same pathway was reported as enriched at the top of a ranked gene list for one database and at the bottom for another). Additionally, 53 concordant pathways were common between the results of GSEA and ORA; however, as expected, differences based on the pathway enrichment method were observed. Overall, the results of the LIHC-TCGA dataset for both methods showed that approximately one-third of equivalent pathways were contradictory across the two methods. Thus, the selection of databases, as well as the enrichment method, are important aspects in the experimental design of pathway enrichment analysis. We have observed that the use of one over another can yield discordant results, leading to different interpretations of results depending on the database choice. In the following sections, we illustrate why these results may be discrepant by analyzing the gene sets of a given pathway.P-value <0.05) differences between groups are colored gray, while statistically significant ones are colored red or blue based on the sign of the NES, and shaded by a gradient based on the magnitude of the NES. In the case of ORA, pathways are colored gray if they are not significant with an adjusted P-value < 0.05 and red otherwise. Additionally, the size of the gene sets for each of the pathways is proportional to the size of the circles. Furthermore, interactive visualizations also offer zoom and search functionalities to easily identify pathways of interest. In summary, with this tool, users can not only explore the enrichment results through the entire pathway hierarchy but also intuitively evaluate equivalent pathways and the size of the pathways, both of which are known to affect results . This user-friendly and interactive visualization represents the different levels of the pathway hierarchy as circles, each of which represent a child or a parent pathway. In the case of GSEA, pathways that do not show statistically significant  of the ranked list of DEGs, this can account for the observance of the high NES  and another showing contradictory results (pyruvate metabolism) from the results of pathway enrichment on the TCGA-LIHC dataset. In the case of the DNA replication pathway, the results showed that the KEGG, Reactome and WikiPathways equivalent representations consistently reported NES over 2.0, suggesting that the pathway is regulated in the liver cancer dataset. We then explored the overlap of the gene sets of the DNA replication pathway from the three databases, observing that the logS Figure . SimilarS Figure , while oomics data has grown over the past two decades and led to the development of over a hundred different methods, recent benchmarks have shown that the selected method can influence results  and DecoPath can be containerized in Docker, users can deploy the web application as per their needs to run more computationally demanding analyses.Nonetheless, the presented web application is not without its limitations. First, while multiple enrichment methods exist, DecoPath only enables running two of the most popular pathway enrichment analyses. Similarly, DecoPath exclusively contains four pathway databases given the substantial curation effort required to map and harmonize pathway databases. To address these limitations, we enable users to directly upload results from other enrichment methods or pathway mappings from additional databases. Another limitation is the computational power of the server required to run experiments on datasets with a large sample size, or depending on the type of analysis conducted, may not be enough. However, since the source code of the web application is available (P-value. Finally, we hope that our curation effort lays the groundwork for a future overarching pathway ontology with cross-references to databases that could be leveraged and extended by the pathway community.In the future, we plan to map and integrate additional databases into DecoPath, as well as more enrichment methods. Furthermore, we envision the implementation of a consensus algorithm to combine the results obtained across multiple databases into a single score, in line with approaches which integrate results obtained by an ensemble of enrichment methods, such as CGPS  and EGSEhttps://decopath.scai.fraunhofer.de/.A freely available instance of DecoPath can be found at lqab087_Supplemental_FileClick here for additional data file.


Astragali Radix (AR) is a safe and effective treatment to improve CRF, but the related mechanistic studies are still limited.  Cancer-related fatigue (CRF) is an increasingly appreciated complication in cancer patients, which severely impairs their quality of life for a long time.  To systematically analyze the mechanism of AR against CRF by network pharmacology.  TCMSP was searched to obtain the active compounds and targets of AR. The active compound-target (AC-T) network was established and exhibited by related visualization software. The GeneCards database was searched to acquire CRF targets, and the intersection targets with AR targets were used to make the Venny diagram. The protein-protein interaction (PPI) network of intersection targets was established, and further, the therapeutic core targets were selected by topological parameters. The selected core targets were uploaded to Metascape for GO and KEGG analysis. Finally, AutoDock Vina and PyMOL were employed for molecular docking validation.  16 active compounds of AR were obtained, such as quercetin, kaempferol, 7-O-methylisomucronulatol, formononetin, and isorhamnetin. 57 core targets were screened, such as AKT1, TP53, VEGFA, IL-6, and CASP3. KEGG analysis manifested that the core targets acted on various pathways, including 137 pathways such as TNF, IL-17, and the AGE-RAGE signaling pathway. Molecular docking demonstrated that active compounds docked well with the core targets.  The mechanism of AR in treating CRF involves multiple targets and multiple pathways. The present study laid a theoretical foundation for the subsequent research and clinical application of AR and its extracts against CRF. Cancer-related fatigue (CRF) is a long-lasting physical, emotional, and/or cognitive tiredness or exhaustion associated with cancer or cancer treatment . There iAstragali Radix (AR) is the dried root of Astragalus membranaceus Bunge. As an effective and widely accepted treatment, AR has been applied to a variety of diseases. AR was first contained in the ancient Chinese herbal book Shennong Ben Cao Jing in the Han Dynasty and is listed as a good product for tonifying qi and tonifying deficiency. It was demonstrated that AR has immunomodulatory, antioxidant, and anti-inflammatory effects by modern pharmacological studies [ studies .AR has a significant effect on CRF. Clinical practice shows that traditional Chinese medicine decoction and injection with AR, the main drug, can effectively improve the symptoms of CRF and is widely used in the treatment. Jeong et al. used multiple evaluation scales to prove that Buzhong Yiqi decoction with AR as the main drug can effectively alleviate the fatigue state of cancer patients . Xu et aNetwork pharmacology can map herbal compounds and disease phenotypes into biomolecular networks and conduct a complex network of disease-gene-target-drug through qualitative and quantitative analysis , 11. MolConsequently, this study made an elaborate investigation into the pharmacological mechanism of AR in treating CRF by network pharmacology . It furnhttp://tcmspw.com/tcmsp.php) to acquire related compounds. The aforementioned compounds that conformed to the criterion were selected and regarded as active compounds of AR: oral bioavailability (OB) ≥ 30% and drug likeness (DL) ≥ 0.18 [https://www.uniprot.org).“Huangqi” (AR) was used as a search term in TCMSP  (http://) ≥ 0.18 . The cor) ≥ 0.18  (https:/http://www.genecards.org) was searched to gain CRF targets. Through EXCEL software, the intersection of AR targets and CRF target was taken to draw a Venny diagram.Using “cancer-related fatigue” as the keyword, the GeneCards database   to obtain PPI relationships. The CSV file containing the PPI relationship was downloaded to establish the PPI network. In this network, degree centrality (DC) determined the color depth and diameter size of the node. Targets with DC exceeding the average value were screened as core targets.The intersection targets were transmitted to the STRING platform  (https:/http://metascape.org/) integrated more than 40 gene function annotation databases. The core targets were uploaded into the Metascape platform to carry on Go and KEGG analysis. Data with P < 0.05 were selected as the analysis results.As an online tool for gene enrichment analysis, Metascape  (http://http://www.rcsb.org) and TCMSP, structural files of the main active compounds as well as core targets were downloaded and saved as pdbqt format. The coordinate files of receptors and ligands were preprocessed by AutoDockTools 1.5.6 software and saved in pdbqt format: the water molecules in the ligands were removed, the ligands and receptors were separated, nonpolar hydrogen was added, and the Gasteiger charge was calculated. Through the gradient algorithm and multithreading technology of AutoDock Vina software, the docking activity between receptors and ligands can be evaluated by molecular docking score. Docking scores <−4.25, <−5.0, and <−7.0, respectively, represent the existence, good, and strong docking activity between the ligand and the receptor. The docking of molecular ligands and protein receptors was intuitively exhibited through PyMOL software.Searching PDB database  were extracted. 57 core targets were screened out. The PPI network of intersection targets and core targets is displayed in The GO and KEGG analysis results of 57 core targets after the arithmetic by Metascape are shown in Figures P < 0.05) manifested that 1552 entries of biological processes were yielded, such as the apoptotic signaling pathway, blood vessel development, and response to inorganic substances. 34 entries of cell components were yielded, such as membrane raft, membrane microdomain, and transcription regulator complex. 94 entries of molecular functions were yielded, such as transcription factor binding, kinase binding, and protein kinase binding.GO analysis (P < 0.05) showed that AR treatment of CRF involved 137 pathways. The top 5 were pathways in cancer, AGE-RAGE pathway, hepatitis B, IL-17 pathway, and Chagas disease.KEGG analysis (The top five active compounds were selected to dock to the top five core targets in turn to calculate the docking scores .All docking scores were <−5.0, and the abovementioned 5 active compounds docked well with the 5 core targets. Take quercetin as an example to show the visual pictures of its docking with 5 core targets, as shown in Figures 7Assessment of AC-T network demonstrated that the top five active compounds in the treatment of CRF by AR were quercetin, kaempferol, 7-O-methylisomucronulatol, formononetin, and isorhamnetin.α and elevates heme oxygenase 1 protein levels to exert anti-inflammatory properties [Quercetin exerts its antifatigue effect mainly by eliminating metabolic accumulation, and as an effective antioxidant, it has the ability to scavenge free radicals while regulating mitochondrial biogenesis . Studiesoperties , 30. Furoperties –33.According to the attribute parameters of the core targets, AR mainly treats CRF through AKT1, TP53, VEGFA, IL-6, CASP3, and other targets.AKT1 is one of the important isoforms of AKT. As a key signal downstream of PI3K, AKT mainly promotes cell growth, cell proliferation, and cell motility through the phosphorylation of a series of substrates . The absKEGG analysis of core targets showed that AR treatment of CRF involved a variety of signaling pathways related to tumors: pathways in cancer, AGE-RAGE, IL-17, TNF, Toll-like receptor, and HIF-1 pathways.α regulates immunity, host defense, and inflammation by regulating cytokine production, cell survival, and cell death [α dramatically multiplied [α is involved in regulating and interfering with energy metabolism [In the AGE-RAGE signaling pathway, AGEs interact through AGE-RAGE to bind and trigger RAGEs that play a key role in inflammation and oxidative stress . Furtherll death . Moss etltiplied . In additabolism . In the tabolism . TLRs patabolism . In additabolism . In the tabolism . Furthertabolism .In summary, quercetin, kaempferol, 7-O-methylisomucronulatol, formononetin, isorhamnetin, and other active compounds in AR may exert positive effects via AKT1, TP53, VEGFA, IL6, CASP3, and other targets, and through AGE-RAGE, IL-17, TNF, Toll-like receptor, and HIF-1 signaling pathways in treating CRF.


Amending from ancient classic, Ziyin Tongluo Formula (ZYTLF) has been prescribed to treat postmenopausal osteoporosis (PMOP) for decades with good curative effect. However, the possible mechanisms of it are still unknown.Ovariectomized rat model was established to validate the therapeutic effect of ZYTLF on PMOP by Micro-CT bone analysis and pathological observation. Subsequently, active ingredients of ZYTLF and corresponding putative targets were identified by online databases. Overlapping genes were first obtained from mining genes associated with PMOP and then overlapped them with the putative targets. Key genes were selected from the multiple constructed and analyzed networks. GO and KEGG pathway enrichment analysis were performed by importing the key genes to the DAVID database. Moreover, validation of the binding association between key targets and their corresponding active compounds were accomplished by AutoDock Tools and other software. Lastly, Enzyme linked immunosorbent assay (Elisa) detection and Western blot analysis were utilized to further explore the possible mechanism of ZYTLF on PMOP.With 129 target genes interacting with PMOP, 92 active compounds of ZYTLF corresponded to 243 targets, and 50 key genes were chosen. Network analysis revealed the top 10 active ingredients, such as quercetin and kaempferol and the top 50 key genes, such as ERα, p38 MAPK, p-AKT and TGF-β1. Enrichment analysis uncovered multiple signaling pathways, including estrogen signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway and MAPK signaling pathway. Furthermore, our finding of the foremost active compounds was tightly bound to the core proteins, which were verified by molecular docking analysis. Through experimental studies, we confirmed that the prescription of ZYTLF could ameliorate the OVX-induced bone loss, suppress the osteoclast activity and boost osteoblast ability through experimental studies.The potential mechanisms and therapeutic effects of ZYTLF against PMOP may be ascribed to inhibition of osteoclast activity, boost of osteoblast activity and enhancement of the expression of ERα.The online version contains supplementary material available at 10.1186/s13020-021-00503-5. Postmenopausal osteoporosis (PMOP) is regarded as a severe chronic metabolic bone disease. There is a growing number of postmenopausal women around the world suffering from increased fragility and fracture susceptibility because of decreased ovarian function and estrogen level every year. At this stage of life, the bone resorption is faster than the bone building. Therefore, the incidence rate of osteoporosis steeps up in the population of women at menopausal age with plummeted estrogen level. Factors such as age, gender, genetics, reproductive status, calcium: phosphorus ratio, and exercise have certain impact on bone strength. Bone strength is consisted of bone mineral density (BMD) and bone quality. BMD is detected with dual energy absorptiometry (DXA) and declined by 30–40% at the age of 70s. However, there are many elderly patients who present with vertebral fractures but have bone density within the normal range in the ward. Bone quality deterioration is also closely associated with fracture , 2. WithHormone replacement therapy (HRT) in postmenopausal women enhances their estrogen levels and effectively reduces bone resorption, but its safety is still controversial . A meta-Radix Rehmanniae Praeparatae (Shu Di) as the monarch herb, Ophiopogon japonicus (Mai Dong), Fructus Ligustri Lucidi (Nv Zhen Zi), Radix Angelicae Sinensis (Dang Gui), Radix Paeoniae Alba (Bai Shao) as the minister herb, Loranthus parasiticus (Sang Ji Sheng), Achyranthes bidentata (Niu Xi), Caulis Spatholobi (Ji Xue Teng), Zaocys (Wu Shao She), Scolopendra, Radix Astragali (Huang Qi), Saposhnikoviae Radix (Fang Feng), Rhizoma Atractylodis Macrocephalae (Bai Zhu) as the assistant herb, Radix Glycyrrhizae Preparata (Gan Cao) as the guide herb. Radix Rehmanniae Praeparatae and its derivatives have been reported exerting bone protecting effect in the osteoporosis model by maintaining homeostasis between osteoclastogenesis and osteoblastogenesis [Ophiopogonin D, which was isolated from the Ophiopogon japonicus, showed anti-osteoporosis effect via reducing reactive oxygen species in vivo [Fructus Ligustri Lucidi is proved to have the function of improving bone metabolism and bone quality in ovariectomized, growing, aged and diabetic rats through the regulation of OPG/RANKL/cathepsin K and other signaling pathways [With specific therapeutic activity and low side effect, ZYTLF has been prescribed for decades in Guangdong Province Hospital of Traditional Chinese Medicine to prevent and treat postmenopausal osteoporosis. Our previous study demonstrated that ZYTLF could effectively alleviate clinical symptoms in patients and increase their bone density by inhibiting bone resorption, reducing bone turnover and improving bone microstructure . As a foogenesis . Ophiopo in vivo . Meanwhipathways . In addipathways . The menNetwork pharmacology is an emerging technology, which integrates systems biology and bioinformatics and other emerging interdisciplinary disciplines to reveal the relationship between drugs and the body from the perspective of biological systems network. Li first applied network pharmacology to TCM and proposed the concepts of “TCM network pharmacology” and “network target” , 17. In In this work, network pharmacology was applied to mine the key ingredients, targets and signaling pathways of ZYTLF against PMOP. Moreover, molecular docking simulation was utilized to validate the stability of key proteins and corresponding compounds by detecting binding affinity. Furthermore, ovariectomized (OVX) rat models was established to confirmed the therapeutic effect of ZYTLF on PMOP and elucidate its preliminary mechanisms Fig. .Fig. 1ScThe name and content of fourteen herbs in ZYTLF are shown in Table Experimental animal ethics panel of Guangdong Provincial Hospital of Traditional Chinese Medicine (License No. 2019044) approved the animal experiments. Experiments were performed in China Academy of Chinese Medical Sciences Guangdong Branch (License No. 00228297). Sixty three-month old female Sprague-Dawly (SD) rats, weighting 200–230 g, were purchased from the Laboratory Animal Center of Southern Medical University (License No. 44002100022874) and were allowed to acclimatize for one week before free access to food and space. All animal procedures complied with international ethical guidelines and the National Institutes of Health Guide concerning the Care and Use of Laboratory Animals. We tried our best to attenuate the number and suffering of the animals.The rats received either sham operation  or bilateral ovariectomy . All operations were performed with the application of anesthesia methods and sterile techniques as well as postoperative anti-infection and analgesia. Three months after ovariectomy, if the BMD of the rats were decreased significantly, the establishment of OVX animal model wound be considered to be successful.Five groups of qualified rats were randomly divided ). The rats in the SHAM group and OVX group were given distilled water by gavage, the OVX + ZYTLF Low/Middle/High dose group were administrated 1.34 g/kg, 2.68 g/kg, 4.02 g/kg per day respectively, and the OVX + WM group were treated with alendronate 10.51 mg/kg per week and calcitriol 0.075 μg/kg per day for 12 weeks. Rat intake dose was modified according to half, one or two times of the clinical human dose. The conversion obeys the Human-Rat Equivalent Dose Conversion Principle.After the last 24 h of medicinal treatment, overdose of anesthesia was used to euthanize the animals. The rats were anesthetized after three months. Blood samples from the abdominal aorta were first centrifuged at 4000 rpm for 10 min. Then the serum was removed and the samples were stored at – 20 °C. Lumber vertebra body and bilateral femur and tibia were also removed as samples.The contents of estradiol (E2), alkaline phosphatase (ALP), procollagen I N-terminal propeptide (PINP), tartrate-resistant acid phosphatase (TRACP), type I collagen cross-linked Amino Terminal Peptide (NTX), type I collagen cross-linked carboxyl Terminal Peptide (CTX-1), calcium (Ca), phosphorus (P) in rat serum samples were confirmed by commercial ELISA kits based on manufacturer’s instructions. The ELISA kits were purchased from Nanjing Jiancheng Bioengineering Institute, Nanjing, China.Bone structural and mineral changes in rats were evaluated by micro-computed tomography (micro-CT) technique. A micro-CT imaging system  was applied to perform the CT scan and trabecular morphometric analysis under the guidance of manufacturer’s application notes. A spatial resolution of 9 μm  was used to scan right femur samples. After that, CT images were reestablished by in-built software CT-vox and CTAn, respectively. With recommended order set, CTAn selected the trabecular region of interest in an unbiased batch. The aim of performing trabecular analysis was to quantify morphometric calculations and BMD.The measured direct trabecular metric parameters of right femur were as follows: trabecular thickness (Tb.Th), bone volumetric fraction (BV/TV), trabecular number (Tb.N) as well as trabecular separation (Tb.Sp). Calculations of directly measured non-metric parameters were performed as well, including the trabecular bone pattern factor (Tb.Pf), an inverse assessment of trabecular connectivity and the structural model index (SMI), an estimate of the prevalence of plate-like or rod-like trabecular.The left tibial tissue was acquired and fixed in 4% paraformaldehyde for 24 h and then decalcified for 2 weeks in 10% EDTA buffer (pH 7.0). The samples were dehydrated, embedded in paraffin and cut into slices (about 4 μm thick). Slices were processed with tartrate-resistant acid phosphatase (TRAP)-staining and alkaline phosphatase (ALP)-staining respectively, aiming to detect the number of osteoclast and osteoblast. The sections were visualized by using an optical microscope  and then photographed. The cytoplasm of osteoclasts was wine-red and the cytoplasm of osteoblasts showed grayish-black granules after ALP + TRACP staining.http://tcmspw.com/tcmsp.php) [http://bionet.ncpsb.org/batman-tcm/) [http://www.megabionet.org/tcmid/) [Radix Glycyrrhizae Preparata serves as the guide herb in ZYTLF with a small amount, greater bias was avoided by setting the standard as OB ≥ 60% and DL ≥ 0.36. The targets of ZYTLF were collected by TCMSP analysis platform and then were transferred to standard protein name in Uniprot database (https://www.Uniprot.org), setting organisms as Human [Fourteen herbs of ZYTLF were in inputted into the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP msp.php) . BAT-MANan-tcm/)  and Trad/tcmid/)  in sequeas Human .http://www.genecards.org) [http://omim.org/) [https://www.disgenet.org/) [“Postmenopausal” and “Osteoporosis” were the key words to gather PMOP connected genes from the three following online databases: Genecards (rds.org) , OMIM (him.org/) , and Diset.org/) . OverlapThe network model of “herb-compound-overlapping gene” was built by importing the overlapping target genes, their corresponding active components and herbs into Cytoscape 3.7.1 . CytoNCAhttp://string-db.org/, ver.11.0) [Construction of protein–protein interaction (PPI) network of overlapping proteins was completed through setting the condition of data analysis mode as “Multiple proteins”, the type as “Homo sapiens” (human), and the minimum mutual threshold as “high confidence (0.700)” in The STRING database (er.11.0) . The othhttps://david.ncifcrf.gov/) was utilized to conduct enrichment analysis of Gene Ontology  and Kyoto Encyclopedia of Genes and Genomes  [The Database Visualization and Integrated Discovery system  , 31, sethttps://pubchem.ncbi.nlm.nih.gov/). Procession and transformation of the 2D structure into PDB format were accomplished by PyMOL, and they were saved in PDBQT format as docking ligands. Concurrently, the collection of all the crystal structures of the key proteins from the RCSB protein data bank  and the selection of those with distinctive ligands and comparatively higher resolution were done. AutoDock Tools was used to take away the water molecules, isolate proteins and reserve them as receptors. The receptors and ligands were processed with PyMOL and Auto Dock and then docked through Vina.The binding efficiency of the overlapping proteins and major active components in ZYTLF were evaluated by computer simulation docking technology. The SDF structure of top 10 core compounds were collected in the network from the PubChem database (Radioimmunoprecipitation assay (RIPA) buffer that contained Halt Phosphatase Inhibitor Cocktail  were ground with right tibia samples, which were corrected based on the results of the bicinchoninic acid assay (BCA). 10% SDS-PAGE separated 30 µg protein. Then they were transferred to polyvinylidene fluoride (PVDF) membranes . In order to maximize the protein loading, membranes were air-dried and reactivated in methyl alcohol. According to the purpose of the experiment, they were blocked with 5% nonfat milk powder at room temperature, and incubated with primary antibody, including ERα , TGF-β1 , p38 MAPK , p-AKT , and GAPDH  at 4 °C overnight. The nitrocellulose membranes were washed three times with PBST and incubated with goat anti-rabbit IgG antibody  at room temperature for 2 h. After these procedures, they were rinsed three times and scanned for optimal density value of the target protein unit by improved Journal Pre-proof chemiluminescence analysis .All statistical analyses of this paper were performed by GraphPad Prism 8.0.2 . The data were represented by means ± standard deviation (SD). The nonparametric Kruskal–Wallis method was used for the data without Gaussian distribution based on Shapiro–Wilk test, followed by Dunn’s multiple comparisons test. One-way ANOVA analysis was used for the data with Gaussian distribution and equal SDs, followed by Dunnett’s multiple comparisons test. Brown-Forsythe ANOVA test was used for the data with Gaussian distribution and unequal SDs, followed by Dunnett’s T3 multiple comparisons test. Statistical analyses were conducted to confirm the differences between test groups and nominated control group, with the significance level at P < 0.05.To determine the threptic effects of ZYTLF on OVX rats, we collected serum from experimental rats after treatment. Related indicators in serum were detected by ELISA, which the result of were shown in Fig. To further determined the effect of ZYTLF on experimental animals, Micro-CT evaluation was performed on right femur. As shown in Fig. The micro-CT plain scan  of the distal femoral bone were shown in Fig. To verified the effect of ZYTLF on the activity of osteoclast and osteoblast, ALP + TRACP staining was conducted on bone biopsy. Figure A total of 1738 compounds were obtained from TSMSP platform, BAT-MAN TCM and TCMID database, among which 92 met the requirement were regarded as active components. Additional file A network of “herb-compound-overlapping gene” was established Fig. b. AdditiPerformance of GO enrichment and KEGG pathway enrichment analysis on 50 core genes and extraction of significant enrichment results (FDR < 0.05) were done by DAVID database. Totally, 62 GO-BP terms, 6 GO-CC terms, 11 GO-MF terms and 59 terms on the KEEG pathway were obtained. The enrichment results showed that the main biological processes were positive transcriptional regulation signals of RNA polymerase II promoter, negative regulation of apoptosis, positive regulation of transcription using DNA as a template, aging, positive regulation of gene expression, etc. Main cell components such as nucleus, cytoplasm and extracellular space were the major components of biological processes. Major regulated molecular functions were binding of enzyme, transcription factor and protein binding Fig. a–c. PMOPWhether the top 10 compounds were essential in regulating the 50 key proteins was verified by molecular docking simulation. A stable structure wound be formed when the ligand bound to one or more amino acid residues in the active site  of the receptor and took part in the process such as conformation change and energy complementation to combine with the receptor. This research illustrated that the top 10 compounds had sturdy association with 50 core proteins, such as AKT1 (PDB id: 6npz), MAPK14 (PDB id: 6sfo), ERα (PDB id: 4pxm), TNF (PDB id: 3m2w), TGF-β1 (PDB id: 6om2) and PTGS2 (PDB id: 5f19). These compounds were of great importance based on the result of network pharmacology , middle(p < 0.05) and high(p > 0.05) dose of ZYTLF. Moreover, ZYTLF showed similar effect to alendronate on trabecular microstructure indexes. The results indicated that ZYTLF could mitigate microstructure degeneration in OVX rats and preserve thicker, interwoven and plate-like trabecular bone. These observations demonstrated that ZYTLF could ameliorates trabecular bone resorption in OVX induced rat model with high turnover bone metabolism. Furthermore, our bone pathological analysis with ALP + TRACP staining and detection of bone metabolic markers confirmed that ZYTLF could ameliorate the bone phenotype of OVX rats, reduce the osteoclast formation and increase osteoblast differentiation. Many TCM therapy has an effect on promoting OB generation and inhibiting OC differentiation, as ZYTLF does. However, the specific mechanism of ZYTLF on osteoclastogenesis and osteoblastogenesis is still uncertain.Preliminary determination of the pharmacological compounds and complex molecular mechanisms of ZYTLF on PMOP were done by molecular docking technology and network pharmacology. Network pharmacology is a systematic approach employed to learn the complexities among compounds, targets, diseases and biosystems. It coincides with the holistic and systemic views of TCM theory . CurrentAdditional, network-based algorithm was utilized on key proteins to understand the gene ontology (GO) and KEGG pathways in the treatment of ZYTLF. From the result, we assumed that ZYTLF might manipulated multiple biological processes and molecular functions, consisted with the complexed mechanisms of PMOP. The data revealed 50 putative targets were involved in pharmacological action of ZYTLF on PMOP, in which ERα, p38 MAPK, p-AKT, TGF-β1 were considered as hub proteins. Moreover, 15 KEGG pathways were observably enriched by KEGG analysis, including estrogen signaling pathway, TNF signaling pathway, PI3K-AKT signaling pathway, MAPK signaling pathway, which were closely bound with the development and progression of PMOP. Estrogen bound with the estrogen receptor in osteoblasts and osteoclasts to act on the OPG/RANK/RANKL signaling pathway, which further propelled secretion of OPG, down-regulated the expression of RANKL, and inhibited the formation of osteoclasts . It is wIt is generally considered that estrogen deficiency is the major cause of the occurrence and development of PMOP, so the current research mainly focuses on how to elevate estrogen level. Interestingly, this research found that ZYTLF did not significantly boost estrogen level, but reduce bone loss to normal bone homeostasis. Through western-blot analysis, we found that ZYTLF treatment increased the expression of ERα protein in bone sample. ERα has been proved to play a critical role in bone metabolism. The expression of ERα in bone tissues is estrogen-dependent, and the transcription and translation of ERα are inhibited after the level of estrogen is declined . On the Compare our study with the Network pharmacology evaluation method guidance . There aIn summary, our findings illuminated that ZYTLF could ameliorate the OVX-induced bone loss, suppress the osteoclast activity and boost osteoblast ability. Moreover, the key compounds and target proteins were mined, such as quercetin, kaempferol and ERα, p38 MAPK, p-AKT and TGF-β1, which were considered as the materials of ZYTLF in treatment of PMOP. In addition, multiple signaling pathways, including estrogen, MAPK, PI3K/Akt and OPG/RANK/RANKL signaling pathways, involved the treatment mechanisms. Consequently, this study provides rational treatment of ZYTLF on postmenopausal osteoporosis with experimental evidence and preliminarily indicated its mechanism.Additional file 1: Table S1. The detailed information of active ingredients of ZYTLF.Additional file 2: Table S2. List of ZYTLF-targets, PMOP-genes, and ZYTLF-PMOP overlapping genes.Additional file 3: Table S3. Detail information of "Herb-Active ingredient-Overlapping gene" network.Additional file 4: Table S4. Results of Gene Ontology (GO) terms and pathways.Additional file 5: Table S5. The binding energies of ten core compounds and fifty key proteins.


Screen the biologically active components and potential targets of SNFYT through Traditional Chinese Medicine Systems Pharmacology (TCMSP), Traditional Chinese Medicines Integrated Database (TCMID), and related literature. In addition, DrugBank, OMIM, DisGeNET, and the Therapeutic Target Database were searched to explore the therapeutic targets of IS. The cross-targets of SNFYT potential targets and IS treatment targets were taken as candidate gene targets, and GO and KEGG enrichment analyses were performed on the candidate targets. On this basis, the SNFYT-component-target network and protein-protein interaction (PPI) network were constructed using Cytoscape 3.7.2. Finally, AutoDock was used to verify the molecular docking of core components and core targets. P < 0.05), including the IL-17 signal pathway, TNF signal pathway, and HIF-1 signal pathway. The molecular docking results of the core components and the core targets showed good binding power.  We screened out 95 potentially active components and 143 candidate targets. SNFYT-component-target network, PPI network, and Cytoscape analysis identified four core active ingredients and 14 core targets. GO enrichment analyzed 2333 biological processes, 79 cell components, and 149 molecular functions. There are 170 KEGG-related signal pathways ( SNFYT may achieve the effect of treating ischemic stroke through its anti-inflammatory effect through a signal pathway with core targets as the core. Ischemic stroke is a kind of disease caused by various causes of local brain tissue blood supply obstruction, which leads to brain tissue ischemia, hypoxia, and necrosis and then produces a variety of clinical neurological impairment symptoms. Stroke is one of the most common causes of death and disability worldwide . AccordiRehmanniae Radix Praeparata , Cornus Officinalis Sieb. Et Zucc. , Rhizoma Rhizoma Dioscoreae , Hedysarum Multijugum Maxim. , Cortex Moutan , Angelicae Sinensis Radix , Radix Paeoniae Rubra , Radix et Rhizoma Rhodiolae , and Pheretima . The previous research group conducted a series of studies on the specific pharmacological mechanism of this formula, and the clinical trial results showed that SNFYT could significantly reduce the blood viscosity of patients and improve their clinical symptoms [In China, Traditional Chinese Medicine (TCM) was widely used more than 2,000 years ago to treat various diseases, including stroke. Different from modern medicine, the treatment of ischemic stroke by traditional Chinese medicine is not targeted at one or several action targets but multisystem and multilevel regulation of the human body. In addition, compared with thrombolytic drugs and anticoagulant drugs, the side effects of traditional Chinese medicine are fewer. Shennao Fuyuan Tang (SNFYT) is a Chinese medicine formula for the treatment of ischemic stroke, which was authorized by Professor Hu Guoheng, a famous Chinese medicine doctor in Hunan Province, China. The formula is based on Buyang Huanwu Decoction, which is composed of symptoms . Animal symptoms , neurotrsymptoms –13, antisymptoms , cerebrasymptoms , promotisymptoms –18. Cellsymptoms . AlthougNetwork pharmacology is based on the theory of systems biology and the fusion of computer technology to study the interaction of drugs, genes, proteins, etc., at the system level. The research concept of network pharmacology coincides with the holistic view of Traditional Chinese Medicine. It is used to predict and identify the biologically active components and targets of Chinese herbal medicine and to discover new indications through active components screening, target prediction, network construction, and analysis. The method has unique advantages and potential, which will help the public to further understand the role of Chinese medicine. This study aims to further reveal the potential mechanism of SNFYT in the treatment of ischemic stroke through network pharmacology and lays the foundation for future pharmacological and clinical research on ischemic stroke. The protocol of our experimental process is shown in https://old.tcmsp-e.com/tcmsp.php) [https://119.3.41.228:8000/tcmid/) [Pheretima has too little active ingredient and related target information in the two databases mentioned above, the active ingredients and related targets were searched manually [https://www.uniprot.org/) [The components of herbs of SNFYT were obtained from TCMSP (msp.php)  and TCMI/tcmid/) . Screenimanually . The Uniot.org/)  was adophttps://go.drugbank.com/) [https://www.omim.org/) [https://www.disgenet.org/) [https://db.idrblab.net/ttd/) [IS-related genes were collected from four databases: DrugBank database (nk.com/) , OMIM (him.org/) , DisGeNEet.org/) , and Theet/ttd/) . We retrThe intersection between SNFYT-associated targets and IS-associated targets was considered as the candidate targets of SNFYT for treating IS. Hence, we obtained these candidate targets and generated a corresponding Venn diagram by using the R software (version 4.0.3).Intersection genes of SNFYT-related targets and IS-related targets were entered into Cytospace software (version 3.7.2) as candidate targets to construct the component-target network. Degree and betweenness centrality are important topological parameters of networks and were calculated in Cytoscape to measure the topological importance of target and component nodes. The nodes were identified as core targets and core components according to the “degree” and “betweenness centrality” values, which were larger than the average degree and average betweenness centrality of all nodes in the network.https://string-db.org/) [To comprehensively screen the core targets, we use String 11.0 (db.org/)  to buildThe “cluster profiler” package, “colorspace” package, and “stringi” package of R software (version 4.0.3) were used to analyze the GO and KEGG enrichment annotations of candidate targets. The GO analysis included three levels of biological process, cell component, and molecular function, and the top 10 with the highest enrichment were selected, respectively. The top 20 signal pathways with the highest enrichment were also selected for KEGG analysis.https://pubchem.ncbi.nlm.nih.gov/), imported it into the Chem3D software (version 19.0.0.22) for molecular structure energy optimization, and saved it as a mol2 format file from the PDB database (https://www.rcsb.org/) download the crystal structure of the core target protein, used Pymol software (version 4.6.0) to delete the water molecule of the target protein and the ligand located in the active pocket, and selected the small molecule ligand specific to the target protein as the active center. In the AutoDockTools software (version 1.5.6), added hydrogen atoms, calculate charges, set atoms, adjust charges and bond twists on small molecules, and saved the data in the “pdbqt” file. Used Autodock Vina [Seven targets with the highest degree in the PPI network and seven core targets in the component-target core network were selected for molecular docking with four core components in the component-target core network. Download the “sdf” file of the 3D structure of the core component from the PubChem database  between common targets . Then, tBecause the Clusterprofiler in R has high classification and enrichment accuracy and a visualization module for the results, it can be used to further study the target of SNFYT on IS. The above 143 targets are used for GO enrichment analysis and KEGG enrichment analysis. As shown in P < 0.05). The top 20 pathways are shown in Using R software to perform KEGG enrichment analysis on 143 candidate genes, 170 results were obtained , VEGF receptor Flk-1 and vascular stromal adhesion protein [Rehmanniae Radix Praeparata, Cornus Officinalis Sieb. Et Zucc., Rhizoma Rhizoma Dioscoreae, Angelicae Sinensis Radix, Radix a common component of Paeoniae Rubra, can achieve neuroprotective effects by regulating cellular autophagy [We firstly identified 95 active components of SNFYT  and thenoxidants . Basic soxidants , antiapooxidants , antioxioxidants , promoteoxidants , and preoxidants , 40, witand PARP , 41–46, and PARP , NF-κB [PARP [κB , and Aktammatory , antioxiammatory , and apoammatory  effects  pathway . Beta-Si protein . Stigmasutophagy , 55, a putophagy .Based on the degree value from largest to smallest, seven core targets were selected from the component-target core network , which wκB signaling pathway [α can reduce blood-brain barrier damage during acute cerebral ischemia [κB signaling pathway and can inhibit microglia M1 polarization and promote M2 polarization, thus achieving neuroprotective effects [PTGS is a key enzyme in the synthesis of prostaglandins and affects platelet aggregation. PTGS1 and PTGS2 are two isozymes of PTGS, which differ greatly in expression regulation and tissue distribution. PTGS2 acts only when stimulated by cytokines, growth factors, NO, etc., and is mainly involved in the inflammatory response, also known as the inducible enzyme . It has  pathway . PPARG p pathway , and its pathway , 62. In  pathway . ADRB2 i pathway , and inhischemia . CALM1 mischemia . AR is aischemia , potentiischemia . JUN, a ischemia  and is aischemia , inflammischemia , and repischemia . TNF, alischemia , 74. The effects , 76. TP5 effects  and is a effects . MAPK1,  effects , and inh effects . Some st effects , and it α is degraded by E3 ubiquitin ligases containing pVHL [α subunit is not recognized by pVHL and can accumulate in large amounts to bind to β to form HIF-1 [γt, thereby further accelerating TH17 maturation and differentiation [α can reduce the expression of TNF-α and IL-6 and increase the expression of IL-10 [α in CD8+ T cells lacking HIF-1a is subsequently reduced [α can indirectly regulate TNF-α production by macrophages [GO enrichment analysis showed that the biological processes involved in SNFYT are mainly focused on the body's response to oxidative stress and nutrient levels, and the cellular components involved are mainly cell membrane and endosomal systems, and the molecular functions are mainly DNA transcription. KEGG enrichment analysis showed that SNFYT prevention and control of IS mainly involves IL-17 signaling pathway, TNF signaling pathway, HIF-1 signaling pathway, IL-17 signaling pathway, and TNF signaling pathway, which are all important inflammatory pathways, and studies have shown that 70%–80% of nerve damage is associated with inflammatory responses after stroke . Both ILing pVHL , and undrm HIF-1 , which irm HIF-1 , affectirm HIF-1 . Interesrm HIF-1  and interm HIF-1  but alsorm HIF-1 , which intiation . Furtherof IL-10 , 96, tharophages . These rIn summary, we have explored the core components and core targets of SNFYT using network pharmacology and molecular docking methods and elucidated that the intervention of SNFYT on the inflammatory cascade of IS may be a potential pathway of action of SNFYT in the treatment of IS, laying an experimental foundation for further study of the “multicomponent, multitarget, and multichannel” action of SNFYT in the treatment of IS.


Osteoporosis (OP) is a highly prevalent orthopedic condition in postmenopausal women and the elderly. Currently, OP treatments mainly include bisphosphonates, receptor activator of nuclear factor kappa-B ligand (RANKL) antibody therapy, selective estrogen receptor modulators, teriparatide (PTH1-34), and menopausal hormone therapy. However, increasing evidence has indicated these treatments may exert serious side effects. In recent years, Traditional Chinese Medicine (TCM) has become popular for treating orthopedic disorders. Erxian Decoction (EXD) is widely used for the clinical treatment of OP, but its underlying molecular mechanisms are unclear thanks to its multiple components and multiple target features. In this research, we designed a network pharmacology method, which used a novel node importance calculation model to identify critical response networks (CRNs) and effective proteins. Based on these proteins, a target coverage contribution (TCC) model was designed to infer a core active component group (CACG). This approach decoded the mechanisms underpinning EXD's role in OP therapy. Our data indicated that the drug response network mediated by the CACG effectively retained information of the component-target (C-T) network of pathogenic genes. Functional pathway enrichment analysis showed that EXD exerted therapeutic effects toward OP by targeting PI3K-Akt signaling (hsa04151), calcium signaling (hsa04020), apoptosis (hsa04210), estrogen signaling (hsa04915), and osteoclast differentiation (hsa04380) via JNK, AKT, and ERK. Our method furnishes a feasible methodological strategy for formula optimization and mechanism analysis and also supplies a reference scheme for the secondary development of the TCM formula. Osteoporosis (OP) is a metabolic bone disorder characterized by bone-mass reduction, bone microstructural degeneration, increased bone fragility, decreased bone strength, and increased fracture risk . ApproxiCurculigo orchioides Gaertn. , Epimedium brevicornu Maxim. , Angelica sinensis (Oliv.) Diels , Morinda officinalis F.C. How , Phellodendron chinense C.K. Schneid , and Anemarrhena asphodeloides Bunge . Of these herbal components, BJT is rich in saccharides, iridoids, organic acids, and other components, which are believed to enhance immunity and reproductive functions, improve OP, and mediate depression and oxidation acrylamide), EXD37 (asperglaucide), EXD20 (lignoceric acid), EXD21 (oleic acid), and EXD157 , and the targets of the five components accounted for 47.92% of effective proteins. Furthermore, targets of 37 components and 56 components contributed to 90.38% and 100% of effective proteins; therefore, we selected these latter 56 components as CACG . This hip < 0.05), and pathogenic gene-enriched pathways numbered 129 (p < 0.05). CACG target-enriched pathways covered 77.52% of pathogenic gene-enriched pathways. These CACG-mediated targets were enriched in neuroactive ligand-receptor interactions (hsa04080), PI3K-Akt signaling (hsa04151), cAMP signaling (hsa04024), MAPK signaling (hsa04010), estrogen signaling (hsa04915), osteoclast differentiation (hsa04380), etc. These results indicated that combining the CRN with the TCC model to optimize herbal formulae was reliable, and predicted CACG potentially triggered therapeutic roles by identifying cell proliferation-related osteoblast and osteoclast pathways.To analyze EXD effects toward OP treatment at the functional level, pathway analyses were conducted  using CATo decode EXD combination effects, all CACG targets were enriched by GO analyses . This apFor this analysis, we identified 100 pathways shared by CACG and pathogenic genes, including PI3K-Akt signaling (hsa04151), estrogen signaling (hsa04915), and osteoclast differentiation (hsa04380). While calcium signaling (hsa04020) and apoptosis (hsa04210) were important processes in OP pathogenesis based on the literature. Increasing evidence has indicated these pathways are related to OP pathogenesis or are therapy targets for OP. To identify EXD mechanisms for OP therapy from a system perspective, we generated an integrated signaling pathway using five molecular pathways .To define EXD target pathway positions, we considered the first three columns as upstream and the rest as downstream positions of the integrated pathway. PI3K-Akt signaling (hsa04151) was identified as one of the top pathways implicated in OP treatment with EXD. EXD regulated 24 targets upstream of PI3K-Akt signaling (hsa04151), e.g., RTK, TLR2/4, IGTA/B, and GCPR, and 55 targets in downstream pathways, e.g., PI3K, AKT, and ERK. EXD may be putatively activated downstream of the PI3K and AKT proteins via upstream TLR2/4, resulting in a downstream GSK3, CCND1, and BCL-2 cascade, which are closely related to bone cell proliferation and apoptosis. Most EXD targets regulating estrogen signaling (hsa04915) were downstream of the pathway, e.g., PKA, AKT, and RAF-1. Also, we observed  that EXDα, and TNFR affect a series of prosurvival and apoptosis process of bone-related cell. 18 of 35 genes in osteoclast differentiation pathways are enriched in the apoptosis pathway , and 14/35 genes in osteoclast differentiation pathways are also enriched in the PI3K/AKT pathway . Recently, a multiscale embedded gene coexpression network analysis (MEGENA) suggested that osteoclast differentiation may contribute to postmenopausal OP pathogenesis [Calcium signaling (hsa04020) and apoptosis (hsa04210) are vital pathways in OP treatments mediated by EXD. Those targets regulated by EXD are downstream of the integrated pathway. For example, EXD affected upstream GPCR, ROC, and CALM and also activated downstream CAMK to affect OP-mediated apoptosis in the calcium signaling pathway (hsa04020). Additionally, 26 EXD targets, including BCL-2, CASP3/6/7, and P53, were located downstream of the apoptosis pathway (hsa04210). EXD also activated downstream JNK, AKT, and ERK via upstream TNF-ogenesis . CombineThe fundamental mechanism underlying OP involves imbalanced bone homeostasis related to osteoblast-osteoclast coupling. Currently, OP therapeutics mainly include bone formation promoters, bone resorption inhibitors, and bone mineral agents, e.g., strontium ranelate, teriparatide acetate, bisphosphonate, and calcium agents. However, some of these drugs have low specificity, high costs, and considerable side effects. TCM strategies have been used for OP treatment because they are less toxic and have limited side effects. Many EXD ingredients exert significant therapeutic effects toward OP, to effectively promote bone formation, inhibit bone absorption, and increase bone density. TCM is advantageous for treating complex diseases, as it is comprised of many components and focuses on many targets. TCM constitutes a highly complex regulatory network and involves synergistic effects, antagonistic effects, and also certain toxic side effects. Thus, optimizing these relationships, determining key synergistic components, and removing toxicity and side effects are viable starting points and main drivers of TCM component optimization.Network pharmacology analyses, using a systems biology approach to improve our understanding of drug behavior on cell and organ at the molecular level, accelerate the identification of drug targets and importantly help discover new biomarkers. Using this approach, scientists can systematically predict and explain drug effects, optimize drug design, determine factors affecting drug effectiveness and safety, and thus design multitarget drugs or drug combinations. However, few studies have used network pharmacology to optimize TCM formulae to identify functional components. Currently, scientists mine gene, protein, metabolite, and microorganism data from different experimental approaches to share information in assorted public databases. This approach is highly beneficial for analyzing pathogenic and therapeutic targets of disease. Such network pharmacology strategies and high-throughput data facilitate the exploration of TCM therapeutic mechanisms for complex disease treatment.In this report, we designed a network pharmacology strategy to optimize CACG in EXD for OP therapy and to identify hidden molecular mechanisms. The model included a new node importance calculation method and a dynamic programming algorithm. The CRN and effective proteins were derived based on the new node importance calculation method, with the CACG derived using a dynamic programming algorithm according to effective proteins in CRN. Finally, underlying mechanisms of EXD toward OP were determined based on CACG. When compared with other network pharmacology strategies, our strategy had three main advantages.The first was that we used a new node importance algorithm to extract CRN and effective proteins to treat OP with EXD. The important feature of this algorithm was that the importance of nodes in the network was related to the bridging ability and leadership of nodes. The bridging ability of nodes referred to the number of times a node acted as the shortest bridge between two other nodes. The higher the number of times a node acted as a “bridge,” the greater its bridging abilities. We divided the number of times a node spanned the shortest bridge by the number of paths. When the bridging ability of one node was high, this meant that many or even all of the shortest paths between other nodes must have transited through it. If the node disappeared, communication between other nodes would have become difficult and possibly disconnected. Leadership referred to the radiation and control ability of a node in the whole network. Specifically, in a network, leadership may be interpreted as the probability that a node influences or radiates (to) other nodes in the network. When compared with the average leadership of the network, leadership nodes could easily become the adjustment center of other nodes, and nodes with very high leadership were more likely to organize functional units or modules to influence other nodes. If the leadership was high, the distance between these nodes and every other node in the neighborhood was very short when compared with their diameter, which was easy to form radiation and influence on other nodes. If node leadership was low, this meant the node was peripheral or isolated.To our surprise, the enrichment pathways and GO terms of effective proteins determined by our method accounted for 99% and 97.35% of intervention pathways and intervention GO terms, respectively. To test whether effective proteins in a CRN could be substituted by disease-specific targets, component-specific targets, or essential common targets for further optimization, the pathway enrichment analysis of effective proteins, disease-specific targets, component-specific targets, and essential common targets showed that the ratio of effective protein-enriched pathways to pathogenic gene-enriched pathways was as high as 80.62%, which is 27. 91%, 14.73%, and 5.88% higher than that of other three datasets, respectively. These data proved that our method retained as many potential therapeutic targets as possible and also confirmed method reliability and accuracy. In addition, when comparing our method with other calculation methods such as degree, betweenness centrality, and clustering coefficient models, we observed that the enrichment pathways of effective proteins captured by our method accounted for 99% of intervention pathways, which was higher than comparator models, at 2%, 2%, and 84%, respectively. The enriched GO terms of effector proteins of our method accounted for 97.35% of intervention GO terms, which was 6.56%, 14.08%, and 47.32% higher than comparator models, respectively. These data validated the reliability and accuracy of our method.p < 0.05), and the pathogenic gene-enriched pathways numbered 129 (p < 0.05). CACG target-enriched pathways covered 77.52% of pathogenic gene-enriched pathways.Secondly, we used the TCC model to screen CACG based on effective proteins, which was an effective approach. Our analyses showed that the number of CACG target-enriched pathways was 186 (β-sitosterol (EXD8) is a common component of five herbs including HB, BJT, DG, YYH, and XM and strongly regulates apoptosis processes [κB), mitogen-activated protein kinase (MAPK), and mammalian target of rapamycin (mTOR) signaling pathways [The third advantage was that our data were supported by the literature. The results of shared and specific components of herbs in EXD provided a basis for CACG optimization, e.g., rocesses , 81 whicrocesses  and apoprocesses . It has rocesses  but alsorocesses . Furtherrocesses . Diosgenrocesses  and imprrocesses . Berberirocesses . Kaempfepathways . Curculipathways . Therefoβ-sitosterol effectively inhibited osteoclast differentiation [β-sitosterol induced apoptosis in several cell lines [According to our C-T network analyses, most of these components were reportedly related to OP-mediated calcium homeostasis and apoptosis pathways, e.g., ntiation  and promntiation . Moreovell lines , 75, 76.ll lines . Apigenill lines . Zhao etll lines . Luteolill lines . Also, lll lines .By analyzing EXD targets, we hypothesize that EXD acts synergistically to mediate OP via calcium regulation and apoptosis, e.g., several studies have shown that ESR1 variants are related to postmenopausal OP –99, and To explore potential EXD mechanisms for OP treatment, CACG targets were used for GO enrichment analyses. We showed that targets regulated by EXD were enriched in calcium ion regulation processes. Also, some other genes involved in apoptosis pathways included BCL2, HSP90B1, and PIK3CB, with higher associations with OP –109. AduOP is an age-related disease associated with estrogen in postmenopausal women. Its clinical manifestations are bone mass reduction and bone structure destruction, which are related to calcium ion regulation. As important mechanical force sensors, G-protein-coupled receptors, via calcium ion and cAMP signaling, appear to influence osteocyte mechanobiology behaviors. PI3K-Akt signaling, estrogen signaling, osteoclast differentiation, calcium signaling, and apoptosis pathways were selected to construct comprehensive pathways to illustrate EXD mechanisms in OP. Increasing evidence has indicated these pathways are related to OP pathogenesis or therapy targets. For example, PI3K-Akt signaling (hsa04151) mainly regulates protein synthesis, cell proliferation, and apoptosis. Besides regulating cell proliferation and apoptosis, calcium signaling (hsa04020) also regulates metabolism. Apoptosis is a genetically programmed process which eliminates damaged or redundant cells via caspase (aspartate-specific cysteine proteases) activation. The balance between pro- and antiapoptotic signals ultimately determines whether cells undergo apoptosis, survival, or proliferation. Estrogen is expressed via “nuclear-initiated steroid signaling” and “membrane-initiated steroid signaling” regulation and includes a plethora of physical processes in mammals including reproduction, cardiovascular protection, bone integrity, cellular homeostasis, and behavior. Several herbs have demonstrated anti-OP roles by regulating osteoblast proliferation and osteoclast differentiation (hsa04380) , 111–113in vivo and/or in vitro studies.In this study, the network pharmacology model proposed by us has good advantages in using algorithms to remove noise and retain CRN; our network pharmacology model exploited algorithms to remove noise and retain CRN, using dynamic programming to select key component groups and verifying model reliability by using the reliable documentary evidence. Thus, our model is a powerful tool for investigating TCM compatibility and mechanisms in disease. Our study had some limitations, e.g., we should have considered more major active components during our EXD research. However, going forward, we will corroborate our active ingredient data for OP treatments using


This study aimed to explore the mechanisms of Hippophae fructus oil (HFO) in the treatment of tympanic membrane (TM) perforation through network pharmacology‐based identification.The compounds and related targets of HFO were extracted from the TCMSP database, and disease information was obtained from the OMIM, GeneCards, PharmGkb, TTD, and DrugBank databases. A Venn diagram was generated to show the common targets of HFO and TM, and GO and KEGG analyses were performed to explore the potential biological processes and signaling pathways. The PPI network and core gene subnetwork were constructed using the STRING database and Cytoscape software. A molecular docking analysis was also conducted to simulate the combination of compounds and gene proteins.A total of 33 compounds and their related targets were obtained from the TCMSP database. After screening the 393 TM‐related targets, 21 compounds and 22 gene proteins were selected to establish the network diagram. GO and KEGG enrichment analyses revealed that HFO may promote TM healing by influencing cellular oxidative stress and related signaling pathways. A critical subnetwork was obtained by analyzing the PPI network with nine core genes: CASP3, MMP2, IL1B, TP53, EGFR, CXCL8, ESR1, PTGS2, and IL6. In addition, a molecular docking analysis revealed that quercetin strongly binds the core proteins.According to the analysis, HFO can be utilized to repair perforations by influencing cellular oxidative stress. Quercetin is one of the active compounds that potentially plays an important role in TM regeneration by influencing 17 gene proteins. The mechanism of hippophae fructus oil in treatment of tympanic membrane perforations was explored by the technology of network pharmacology and molecular docking. Hippophae fructus oil may repair perforations by influencing the regulation of cellular oxidative stress, and the quercetin may be one of the possibly effective compounds in tympanic membrane repair. This study provided a novel idea to explore new drugs in tympanic membrane repair. Network pharmacology combines pharmacokinetic and pharmacodynamic properties with the field of systems biology to study drugs, protein targets, and their pharmacological activities.22.1http://tcmspw.com/tcmsp.php) with the keywords Hippophae Fructus.https://www.uniprot.org/) to obtain gene symbol annotation.Detailed information on the compounds, targets and other secondary data related to HFO was obtained from the Traditional Chinese Medicine System Pharmacology Database  database,https://www.genecards.org/) database,https://www.pharmgkb.org/) database,http://db.idrblab.net/ttd/),https://www.drugbank.ca/) database.The TM‐related targets were mined by searching the following five databases: Online Mendelian Inheritance in Man  analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was performed to elucidate the underlying mechanism through assessments of biological processes (BPs), cellular components (CCs), molecular functions (MFs), and key signaling pathways. The intersecting proteins were then evaluated by bioinformatics annotation using the “clusterProfiler” and “bioconductor” packages of R software. The significantly enriched GO and KEGG terms were identified, and 2.4http://string‐db.org/) databaseFor identification of the potential targets of HFO and the interactions between them, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) (2.5https://pubchem.ncbi.nlm.nih.gov/),https://www.rcsb.org/).The compound with the largest number of critical genes related to TM was selected for further analysis based on molecular docking. The 2D structure of the molecular ligands was downloaded from the PubChem database ‐catechin, schottenol, ent‐epicatechin, CLR, quercetin, ‐17‐((1R)‐1,5‐dimethyl‐4‐methylenehexyl)‐4,4,10,13,14‐pentamethyl‐2,3,5,6,7,11,12,15,16,17‐decahydro‐1H‐cyclopenta(a)phenanthren‐3‐ol, and 5,7‐dihydroxy‐2‐(3‐hydroxy‐4‐methoxyphenyl)methoxy‐chroman‐4. Moreover, the 22 related gene proteins were PTGS1, PTGS2, PGR, NR3C1, MMP2, CASP3, CYP3A4, ESR1, MAP2, SLC6A2, CYP1B1, MMP3, EGFR, MMP9, IL6, TP53, RAF1, IL1B, CXCL8, HSPB1, NFE2L2, and SPP1.As shown in Figure 3.3p‐value < 0.05 and q‐value < 0.05, a total of 1208 significantly enriched GO terms were obtained. The top 10 terms are illustrated in Figure The GO enrichment analysis revealed the underlying BPs, CCs, and MFs of the 22 target genes. Using the criteria p‐value < 0.05 and q‐value < 0.05, a KEGG enrichment analysis was performed to elucidate the related pathways. The results revealed that 103 potential signaling pathways were enriched, and the top 30 of these pathways are shown in Figure Using the filters 3.4Twenty‐two overlapping genes associated with TM and HFO were inputted into the STRING database, and a PPI network diagram was established after selecting “Homo sapiens.” As shown in Figure 3.5Referring to the results of the core gene network, we identified quercetin as a ligand of CASP3, MMP2, IL1B, TP53, EGFR, CXCL8, PTGS2, and IL6 protein receptors. A further calculation was then conducted to simulate the molecular docking of quercetin with these eight gene proteins, and the docking affinity values are listed in Table 4Tympanic membrane perforation can lead to severe morbidity and disability.The results from the GO enrichment analysis revealed that the mechanism through which HFO promotes TM regeneration may be associated with cellular oxidative stress. During the process of tissue wound healing, the biological regulation of cellular oxidative stress generally plays a critical role in cell proliferation and migration.The PPI network diagram reflected the protein–protein relationships among the 22 overlapping gene proteins. Moreover, a further critical subnetwork indicated that the nine core gene proteins might play a major role in the biological process of TM regeneration in response to HFO. Among these nine proteins, the EGFR gene protein reportedly plays an important role in TM healing. EGFR consists of an extracellular ligand‐binding domain, a transmembrane region, and an internal tyrosine kinase domain. This protein is localized in the basal layer of the stratified squamous epithelium of the TM and is involved in the regulation of cell growth and differentiation.In addition to the EGFR protein, other gene proteins also play important roles in the TM healing process. Among these core proteins, IL1B and IL6 participate in the process of histological damage and inflammation, as demonstrated with acute animal models.According to our analysis, 21 ingredients of HFO may contribute to the regeneration of the TM. Among these ingredients, quercetin can be considered the most likely active compound of HFO that promotes TM healing because it influences 17 gene proteins. Although no direct evidence shows the effectiveness of quercetin in TM regeneration, studies have revealed healing benefits from the application of quercetin to a wound both in vivo and in vitro.As a nonsurgical method for TM perforation treatment, HFO therapy can be easily administered in clinics. Gao et al utilized a sterile cotton patch with HFO to cover TM perforations. Similar to topical growth factor therapy, this treatment of traumatic perforations with HFO also does not require hospitalization, which results in less inconvenience and lower medical costs for the patients.Although HFO shows great healing potential for TM perforations, the number of studies on the clinical application of HFO is not as high as that of studies that investigated other bioactive materials, which may be due to its complex composition and unknown mechanism related to the TM.In addition to exploring the mechanism and efficacy of HFO in further studies, investigations should assess whether the ingredients of this mixture can also be applied for TM regeneration based on our analysis. Quercetin, for instance, has been utilized as a medicine‐loaded vehicle for topical delivery in wound healing according to previous studies.In this study, we employed network pharmacology to elucidate the multitarget effects of HFO on TM perforations. Additionally, we discussed several potential herb monomers for TM perforation treatment, which could contribute to the development of new therapeutic strategies. However, the possible effects of HFO revealed from the network analysis require further verification by in vitro biological experiments. This analysis provides theoretical evidence regarding the mechanism of HFO in TM regeneration that can be further explored in future studies.5According to the analysis, HFO can be utilized to effectively repair TM perforations by influencing the regulation of cellular oxidative stress. Quercetin is one of the active compounds of HFP that potentially plays an important role in TM regeneration by influencing 17 gene proteins. This pharmacology‐based approach helps elucidate the underlying mechanisms of HFO in repairing TM perforations, but further experimental exploration is needed for verification of these mechanisms.None.All persons designated as the authors have participated sufficiently in the work to take public responsibility for the content of the manuscript. All the authors ensured that they all gave substantial contributions.