Lastly, retrospective study analyzed the clinical data of 575 patients who were diagnosed PP, treated by the FY, and followed-up in our hospital from 2014–2020. The result that total of 116 important candidate targets were selected based on pharmacological analysis. Selected the top 10 values key targets such as the estrogen receptor alpha (ESR1) and insulin-like growth factor 1 (IGF1), were localized and the related gene functions were determined. Gene functions were associated with biological regulation, a cellular process, or signaling pathway, such as the Estrogen signaling pathway, MAPK signaling pathway and PI3K-Akt signaling pathway. By recognizing the five compounds related to the ESR1 and IGF1, which are Quercetin, kaempferol, Luteolin, Apigenin, and Emodin. The results of the molecular docking study further showed that the flavonoids had a strong binding affinity for ESR1 and IGF1 after docking into the crystal structure. The results showed that the FY could effectively reduce E2, LH, and FSH levels in SD rats. Furthermore, the results of the retrospective analysis of medical records showed that the FY could remarkably reduce E2 levels in girls with PP.Fu-you formula (FY), a Traditional Chinese Medicine (TCM) formula composed of 12 herbs, as an in-hospital preparation, has been used treat to precocious puberty (PP) for decades. However, the lack of phytochemical characterization and mechanism of FY remains the main limitation for its spreading. In this study, we analyze the components and mechanisms of FY in PP, based on the integrated pharmacology. Investigated main constituents, targets, pathways of FY by using an integrative pharmacology, and recognized main constituents by HPLC-MS/MS. Then, observed the levels of Follicle-stimulating hormone (FSH), luteinizing hormone (LH), and estrogen (E Precocious puberty (PP) is a common endocrine disorder among children. It occurs before the age of eight years in girls and before the age of 9 years in boys. In recent years, the annual incidence of this condition has been on the rise, and the incidence among girls is 5–10 times that among boys . At pres2 ELISA kits were obtained from Cloud-Clone Corp . The TCM standards Quercetin , Luteolin , kaempferol  and Emodin  were purchased from the National Institutes for Food and Drug Control . Apigenin  was purchased from the Shanghai Yuanye Bio-Technology Co., Ltd. .Danazol was obtained from the A&D Technology Corporation . Leuprorelin acetate microspheres for injection were purchased from Livzon . FSH, LH, and EPrunella vulgaris L. (Xiakucao); Carapax Trionycis (Cubiejia); Gentiana scabra Bunge (Longdan); Chrysanthemum morifolium (Ramat.) Hemsl (Juhua); Lycium chinense Mill (Digupi); Alisma plantago-aquatica L (Zexie); Scrophularia ningpoensis Hemsl (Xuanshen); Paeonia suffruticosa Andrews (Mudanpi); Rehmannia glutinosa (Gaertn.) DC (Shengdihuang); Hordeum vulgare L (Maiya); Concha oetreae (Muli); Thalluslaminariae (Kunbu) (1.5:1:0.6:0.6:1:1:1.5:0.6:1.2:2:3:1). All herbs were purchased from the Beijing Bencao Fangyuan Pharmaceutical Group Co. Ltd. and the FY was prepared by the Preparation Center of the Beijing Children's Hospital (Lot number: 20201202).The FY was an in-hospital preparation . It comprised a mixture of http://www.tcmip.cn/TCMIP/index.php/Home/Index/index.html) http://tcmspw.com/tcmsp.php) http://119.3.41.228:8000/tcmid/search/) http://stitch.embl.de/) http://www.swisstargetprediction.ch/) http://tcmspw.com/index.php) https://www.symmap.org/) http://www.drugbank.ca/) http://www.omim.org) https://www.disgenet.org/home/) To identify the corresponding targets of the 12 active ingredients of the FY, several approaches combining chemometric methods, information integration, and data mining were implemented. First, all active compounds were submitted to the TCM-IP platform (https://string-db.org/cgi/input.pl?sessionId=rEkaDRgfV0vC&input_page_show_search=on) PPI databases. An interactive network for the candidate drug targets and known PP-related targets of the FY was constructed based on their interaction data and was visualized using the Cytoscape software (The protein-protein interaction (PPI) data were imported from the STRING (software . Interachttp://www.funrich.org) .We performed gene ontology (GO) analysis of the non-repetitive putative targets of the FY using the database for Annotation, Visualization, and Integrated Discovery (DAVID) to gain insights into their involvement in two different categories namely, biological process and molecular function . Tissue ich.org) . We thenCharacterization of main chemical components in FY was assayed by HPLC-MS/MS (AB SCIEX QTRAP 5500). Chromatographic separation was performed on a Hypersil Gold C18 column  (Thermo Scientific), with column temperature set at 40°C. The mobile phase was solution A, 2 mM ammonium acetate in water containing 0.4‰ formic acid, and solution B, methanol. Gradient elution program was: 0–1.5 min, 60–10% A; 1.5–3.5 min, 10% A; 3.5–3.51 min, 10–60% A; 3.51–6.0 min, 60% A. The flow rate of mobile phase was 0.4 ml/min. The mass spectrometer was operated in negative ion mode with a needle potential of -4,500 V; the source temperature was set at 500°C. Nitrogen was used as the sheath gas and auxiliary gas at pressures of 50 and 40 psi. Multiple reactions monitoring (MRM) mode was used to identify the five compounds by monitoring their transitions from the molecular ions to product ions. The proper amounts of standard substance were weighed and dissolved in methanol-water  to prepare standard solutions at 1 μg/ml. Meanwhile, 10 μL of FY was mixed with 1 ml of methanol-water  by vortexing for 10 min, then centrifuge for 15 min at 15,000 rpm. The supernatant fluid was used as sample solution. The chromatograms of standard solution and sample solution were used to compounds matching.In silico molecular docking studies of bio-active peptides or chemical drug molecules that exert their action by binding with specific receptors provides evidence on binding conformation, pattern and affinity. To identify the binding ability of active constituents with PP related targets, the crystal structures of ESR1 (PDB code: 6VIG) and IGF-1 (PDB code: 1IMX) were obtained from RCSB Protein Data Bank (http://www.rcsb.org/), and three main compounds structure of Quercetin, Apigenin and Luteolin were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/) to establish molecular docking model with Discovery Studio 4.5. The CDOCKER module of Dock Ligands in Discovery Studio 4.5 was used to do the docking. The kinetic method was used to randomly search the small molecule conformation, and then the simulated annealing method was used to optimize each conformation in the receptor active site region, so as to make the docking results more accurate.ad libitum and acclimated for three days before the start of the experiments. All animal experiments were performed in strict compliance with Chinese guidelines, including the standards for Laboratory Animals (GB14925–2001), and the Guideline on the Humane Treatment of Laboratory Animals (MOST 2006a). All animal procedures were approved by the Beijing Administration Office for Laboratory Animals.At postnatal day (PND) 3, female Sprague-Dawley rats and their mothers were obtained from SPF Biotechnology Co., Ltd.  2016–0038). The rats were housed in the laboratory animal room and maintained at 24 ± 2°C, with 42 ± 5% humidity on a 12-h light/dark cycle (lights on from 07:30 to 19:30) in a specific-pathogen-free animal room. The animals were supplied food and water The animals were randomly divided into four groups: the control group, model group, positive control (leuprorelin) group, and FY group. At PND 5, the rats in the three experimental groups were given a single subcutaneous injection of 300 µg/25 µL danazol . The rats in the control group were given a subcutaneous injection of 25 µL of glycol/ethanol .After anesthesia, blood was collected from the abdominal aorta, and the serum was centrifuged at 4°C and stored at −20°C until further analysis. The serum concentrations of FSH, LH, and EChildren with PP, treated with the FY at the outpatient department of our hospital from 2014 to 2020 were also evaluated. The inclusion criteria were as follows: 1) continuous use of the FY for 1 year; and 2) evaluation of sex hormone levels every 6 months. The exclusion criteria were as follows: 1) the presence of other endocrine diseases; 2) the presence of ovarian cysts. This retrospective study was approved by the Medical Ethics Committee of Beijing Children’s Hospital, Capital Medical University.p < 0.05.All results were presented as the mean ± SD. Differences were analyzed using one-way ANOVA. The data were further analyzed and plotted using the SPSS 19.0 software . Differences were considered statistically significant at Prunella vulgaris L.and Carapax Trionycis act on the liver and relieves congestion, nourishes yin and clears heat; Gentiana scabra Bunge, Chrysanthemum morifolium (Ramat.) Hemsl, Lycium chinense Mill, Alisma plantago-aquatica L, Scrophularia ningpoensis Hemsl., Paeonia suffruticosa Andrews, Rehmannia glutinosa (Gaertn.) DC, which clear heat and removes dampness, nourishes yin, and cools the blood; Hordeum vulgare L, Concha oetreae and Thalluslaminariae act on the liver and relieves congestion, used as an adjuvant. All herbs combined act on the liver, clear congestion, nourish yin, and clear heat, can reduce the size of nodules in the breast, eliminate vaginal secretions, and dissipate scrofula, and reduce sputum production. The TCM composition are listed in Four hundred and thirteen chemical components were detected from 12 TCM in the FY. Furthermore, 37,468 predicted drug targets were obtained. The predicted target information and the characterization of each herb were derived from data on the TCM-component-targets, as shown in After removing redundant targets, 4,741 targets obtained from 12 herbs intersected with 166 disease targets to obtain 79 mapped-genes. We then explored the predicted therapeutic targets of the FY, using multiple online databases as previously described. A network of potential targets of the compounds in the FY was then constructed using the Cytoscape software, as shown in P-value, as shown in Based on the results of GO and KEGG pathway analyses, the enriched pathways were determined. A total of 2,462 hub genes were identified based on the GO analysis, which were associated with the target genes or proteins of cells (cellular component), molecular functions (function), or biological processes (in process). Gene function information is presented in P-values were selected to construct the multi-level network association diagram of the “traditional Chinese medicine-core component-key target-main pathways” for the FY in the treatment of PP in girls , indicating that the model of PP was successfully established. After treatment with the FY, the E2 and LH levels in rats with PP were significantly reduced compared with the model group (p < 0.01); and FSH levels were significantly reduced (p < 0.05), compared with the model group. However, in the leuprorelin group, only the LH levels showed a reduction (p < 0.05)  .2, LH, and FSH levels were significantly reduced after 12 months treatments (p < 0.01). Significant differences were noted in E2 levels between groups (p < 0.01). The FSH levels were significantly lower at 12 months after treatment compared with 6 months after treatment (p < 0.05)  .Prunella vulgaris L. and Carapax Trionycis, which had common targets with other drugs. These findings suggest that these herbs had close synergistic effects with the other herbs. The following herbs: Gentiana scabra Bunge, Chrysanthemum morifolium (Ramat.) Hemsl., Lycium chinense Mill, Alisma plantago-aquatica L, Scrophularia ningpoensis Hemsl, Paeonia suffruticosa Andrews and Rehmannia glutinosa (Gaertn.) DC had a total of 51 targets. These findings indicate that these 7 herbs in the prescription also had synergistic effects. Furthermore, no common targets were detected for Hordeum vulgare L, Concha Etreae, and Thalluslaminariae, indicating that these 3 herbs are not key active ingredients in the formulation. Our previous study about data mining showed that the most frequently used herbs were Anemarrhena asphodeloides Bunge, Rehmannia glutinosa (Gaertn.) DC, Phellodendron chinense C.K.Schneid, Paeonia suffruticosa Andrews, and Prunella Vulgaris L. Common medicinal were cold and bitter, mostly attributed to the liver and kidney. The core of the herbs based on Zhibai Dihuangwan, and there were 4 kinds of herbs with FY.The results of the analysis of the common targets of the FY revealed a total of 97 targets for At present, GnRHa is recommended to treat CPP, but not incompleteness precocious puberty in the relevant guidelines. The premature thelarche is the most common type of incomplete precocious puberty, 14–23% of which will develop to CPP . So inteThe above-mentioned number of common targets of each herb is consistent with compatibility principles of the formulation. We identified main chemical constituents of FY using HPLC-MS/MS and confirmed that the main constituents related to the key targets in FY are Quercetin, kaempferol, Luteolin, Apigenin and Emodin. Analysis of the formulation reveals that the core components are mainly flavonoids, as well as kaempferol and quercetin, which are all phytoestrogens. Modern pharmacological studies have shown that phytoestrogens can make two-way adjustments, as they are similar to endogenous estrogen in structure and function. When the level of estrogen in the body is lower than the normal level, it can play an estrogen-like role, which can prevent and cure women's menopausal syndrome, prostate cancer, osteoporosis and cardiovascular diseases. On the other hand, when the level of estrogen in the body is higher than the normal level. such as breast hyperplasia, uterine fibroids and other diseases, it can produce estrogen antagonism and effectively weaken the response of target cells to estrogen .2 hormone binds with ESR1 to form a hormone-receptor complex that activates the estrogen signaling pathway. The MAPK signaling pathway and PI3K-Akt signaling pathway regulate the secretion of GnRH, LH, and FSH, as well as metabolic processes associated with bones. In addition, the growth hormone-insulin-like growth factor-1 (GH-IGF1) is the most important neuroendocrine factor associated with growth and development. Excessive IGF1 levels can inhibit GH secretion, and thereby inhibiting the growth of articular cartilage and epiphyseal cartilage, and retard growth in children (Among the top 10 core targets selected, ESR1, IGF1 and other direct therapeutic targets are reportedly related to the onset and development of PP , and arechildren .2 with ESR1and reduction in serum IGF1 concentrations. At the same time, animal experiments showed that the FY could reduce E2, LH, and FSH levels in rats with PP. Retrospective analysis of the medical records also showed that the FY could control the early symptoms of PP, and effectively inhibit E2 levels. The results of the animal experiments and retrospective analysis of medical records have confirmed the feasibility of studies of the mechanism of action of Chinese herbal compounds by integrative pharmacological methods.Treatment with GnRH analogues, such as Leuproline, which act by downregulating pituitary GnRH receptors , represe2, and retard bone aging and maturation. The present findings also provide a novel basis for the clinical application, and further research and development of the FY.In addition, the complex key targets of the FY and predictions based on the KEGG pathway analysis results show that the treatment may have effects on ovarian steroidogenesis, prostate and breast cancer, and other signaling pathways. These results are consistent with our previous clinical studies, which have shown that the effects of complex mixtures in girls with PP and ovarian cysts may be a more favorable intervention. Mixtures such as the FY may diminish ovarian cysts, regulate character development, improve liver function and qi stagnation, alleviate yin deficiencies and heat symptoms, reduce levels of Ein vitro, in vivo and in silico. Secondly, quantitative analysis of the synergistic effect of the main compounds should be investigated in the future.However, this study has some limitations that are worth mentioning. First, the reliability of the effects of FY against PP depends on database, so biological verification is necessary to evaluate the reliability of bioinformatics analysis In conclusion, we combined methods of big data discovery with biological validation to study the mechanism of actions of the FY in PP at the systemic level. We used the TCM-IP database for the treatment of PP and considered various components and molecular mechanisms in the preliminary analysis. We determined that the FY acts through multiple component interactions with targets. The mixture also exerts its effects through multiple pathways involved in the regulation of PP, and may thus play a role in treatment of the condition. Whether other pathways or mechanisms predicted in this network pharmacological approach also contribute to the beneficial effects of the FY requires further investigation.


To explore the effective components and mechanism of Polygonati Rhizoma (PR) in the treatment of osteoporosis (OP) based on network pharmacology and molecular docking methods.The effective components and predicted targets of PR were obtained through the Traditional Chinese Medicine Systems Pharmacology and Analysis Platform (TCMSP) database. The disease database was used to screen the disease targets of OP. The obtained key targets were uploaded to the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database for protein-protein interaction (PPI) network analysis. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of key targets. Analysis and docking verification of chemical effective drug components and key targets were performed with IGEMDOCK software.A total of 12 chemically active components, 84 drug target proteins and 84 common targets related to drugs and OP were obtained. Key targets such as JUN, TP53, AKT1, ESR1, MAPK14, AR and CASP3 were identified through PPI network analysis. The results of enrichment analysis showed that the potential core drug components regulate the HIF-1 signaling pathway, PI3K-Akt signaling pathway, estrogen signaling pathway and other pathways by intervening in biological processes such as cell proliferation and apoptosis and estrogen response regulation, with an anti-OP pharmacological role. The results of molecular docking showed that the key targets in the regulatory network have high binding activity to related active components.PR may regulate OP by regulating core target genes, such as JUN, TP53, AKT1, ESR1, AR and CASP3, and acting on multiple key pathways, such as the HIF-1 signaling pathway, PI3K-Akt signaling pathway, and estrogen signaling pathway. Osteoporosis (OP) is a systemic metabolic bone disease characterized by bone loss, bone tissue microstructure damage, bone pain and brittle fracture . The incGuwei” in traditional Chinese medicine. It is recorded in Su Wen that “If the kidney Qi is hot, the waist and spine will not lift, the bone will wither and the marrow decreases, it will develop into Guwei”. The basic principles of traditional Chinese medicine treatment are tonifying the kidney and essence, promoting blood circulation and removing blood stasis  (https://www.UniProt.org/), and a database of PR compounds and their action target genes was constructed.The active components of PR were extracted from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (msp.php) , 17. Themsp.php) . The scrhttp://ctdbase.org/), Genecards (https://www.genecards.org/) and DisGeNET databases (http://www.disgenet.org/web/DisGeNET/menu/home), “osteoporosis” was used as the search term to collect disease targets related to OP. Finally, duplicates from the three databases were removed to obtain the final OP-related targets.In the Comparative Toxicogenomics Database (CTD) (http://bioinfogp.cnb.csic.es/tools/venny/index.html), and finally, OP-related targets of the drug active ingredients were obtained for subsequent analysis.The targets related to OP and the targets related to the active components of PR overlapped. The intersecting genes and Venn diagram of PR and OP targets were obtained through the online Venn map platform  and screens the main candidate targets and monomer components.https://string-db.org). The PPI relationship is obtained, and then the core gene is obtained according to the number of node connections. Among them, the species selection was “Homo sapiens”, and high confidence = 0.9 was set as the lowest interaction score.PPIs are the basis of cell function and play an important role in regulating physiological and pathological states. In the PPI network, network nodes represent proteins, network connecting lines represent PPIs, and node size, colour, connection length and thickness represent the topology parameters of the node network . To furthttps://david.ncifcrf.gov/home.jsp) and limited the species to human. GO enrichment analysis and KEGG pathway annotation analysis were performed on the potential targets of PR for the treatment of OP to screen the important signaling pathway of PR in OP treatment.We imported the obtained potential targets of PR for the treatment of OP (the common target obtained in 1.1.3) into the target gene name list through Database for Annotation, Visualization, and Integrated Discovery (DAVID) (http://gemdock.life.nctu.edu.tw/). The three-dimensional structure of the protein encoded by the top 15 core target genes from the Protein Data Bank (PDB) database (https://www.rcsb.org/) was downloaded as the protein receptor. The two-dimensional structure of the core active compound of PR was downloaded from the TCMSP database. The binding energy can be obtained by introducing them into IGEMDOCK. The binding ability of ligands and receptors is evaluated by the binding energy. If the binding energy is less than 0, it means that ligand and receptor can spontaneously bind, and the smaller the value, the higher the binding activity.The target proteins and ligand molecules were docked by IGEMDOCK docking software , subsequent analysis was not carried out. There were 84 corresponding targets of active components (excluding repeated targets). See With “osteoporosis” as the search term, 36180 related disease genes were retrieved from the CTD, 2916 were retrieved from the Genecards database, and 1098 were retrieved from the DisGeNET database. After merging the results from the three databases and removing duplicates, a total of 23530 genes related to OP were obtained. The relevant disease genes of the above three databases can be found in The results for drug active ingredient targets and disease targets were intersected, and 84 drug active ingredient targets related to OP were obtained for subsequent analysis Figure 1Cytoscape 3.7.1 software was used to construct the active component target interaction network of 9 PR active monomers and 84 overlapping target genes. The green node represents the active component of PR, and the red node is the action target of the active component, with a total of 272 edges, representing the interaction between the target and the chemical component, reflecting the multicomponent and multitarget characteristics of PR Figure 2The 84 intersecting genes obtained in “2.3” were imported into STRING 11.0 for analysis. The PPI results are shown in p value and the number of enriched genes, the top 10 enriched BPs, CCs and MFs were selected for visualization, and a bubble diagram was generated , 46 cellular component (CC) and 79 molecular function (MF) terms. According to the enerated Figure 5p value, and the KEGG functional enrichment bubble diagram was drawn  can be found in the article/WY and LZ conceived this project. JZ, FL, and GL designed the study. ML and YH searched the literature data. NX and YH extracted the data. JZ, WY, and GL, ML, and JP drafted the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the National Natural Science Foundation of China , the National key research and development program (2021YFC1712804), the Science and Technology Research Project of Guangdong Provincial Hospital of Chinese Medicine (YN2019ML08), China Postdoctoral Science Foundation (No. 2018M633036) and Medical Scientific Research Foundation of Guangdong Province (No. B2019091).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This is a study that is based on network pharmacology, focusing on the pharmacological mechanism of fentanyl in easing pain. Through PPI, GO, and KEGG network pharmacology, the potential pharmacological mechanism of fentanyl was studied. This study compared and analyzed the overlap between the target genes of the active ingredient of fentanyl and the pain treatment target genes. After constructing PPI based on fentanyl, the GO and KEDD pathways were analyzed for enrichment. On the basis of overlapping genes, we constructed the PPI, GO, and KEGG, and analysis showed that the mechanism was likely to be related to some biological process. This study preliminarily identified the important proteins and metabolic pathways related to fentanyl in pain treatment and expected to provide new evidence and research ideas for the use of fentanyl, enhancing effects and alleviating adverse drug reactions. Pain is a common human malady that affects everybody and often impairs function , 2. WhatFentanyl is an effective fat-soluble opioid that is the most commonly used opioid in the perioperative period worldwide, as shown in As is known to all, the network interaction method helps to deepen the understanding of the function of drugs in multilayered information when studying active pharmaceutical ingredients . The speThe rest of this study is organized as follows. Through PPI, GO, and KEGG network pharmacologic analysis, the possible pharmacological mechanism of fentanyl was obtained in https://www.swisstargetprediction.ch/) and PharmMapper (https://lilab-ecust.cn/pharmmapper/) with “Fentanyl” as the keyword. The threshold for data filtering from SwissTargetPrediction is probability∗ >0, and there was no filtering performed on the data from PharmMapper. Then, the data downloaded from PharmMapper were converted into corresponding genes through UniProt database (https://www.uniprot.org/). The retrieved genes from two databases were integrated, and repeated targets were removed.Data were retrieved and downloaded from SwissTargetPrediction (https://genecards.org/) and OMIM (https://omim.org/) with the keywords “ease pain.” The standard for data filtering of GeneCards was relevance score ≥ 3, and there was no data filtering performed on the data from OMIM. The retrieved targets were integrated, and repeated targets were removed to construct a pain-related target database.Data were searched in GeneCards (https://bioinfogp.cnb.csic.es/tools/venny/index.html), and gene intersection of fentanyl and pain was obtained.The target genes of fentanyl and the target genes of pain were matched in the Venn diagram through Venny 2.1 (https://string-db.org) for protein-protein interaction (PPI) analysis to obtain the target protein interaction network relationship. The study species was defined as “Homo sapiens.” The minimum interaction requirement score was 0.9 and set the deletion of outliers. Through Cytoscape3.7.2 software, form the PPI network by importing PPI data, and the network graph of intersection target was obtained.The data of the same targets of fentanyl and pain were input into the STRING database platform (https://www.r-project.org/) and its background database “org.hs.e.g.db” were used to obtain the gene ID (entrezID) of the potential target. And then, through “DOSE,” “clusterProfiler,” and “Pathview” package (Bioconductor), with p value cutoff = 0.05 and value cutoff = 0.05, KEGG signal pathway analysis and GO enrichment analysis for these potential targets were performed. The results were displayed through graphics.R software (Swiss Target Prediction and PharmMapper databases were used to retrieve fentanyl targets. After removing repeated targets, a total of 166 targets meeting the standards were obtained.The target data were obtained by retrieving “ease pain” in GeneCards and OMIM databases. The data obtained from the two databases were combined, and the duplicated items were deleted to obtain a total of 706 pain-related targets.Venny 2.1 was used to draw a Venn diagram, and the detailed information is shown in For a deep mechanism study of fentanyl's effects on easing pain, by importing the STRING database, this study performed PPI network analysis on 52 drug-disease interaction targets. Then, we imported the outcomes into Cytoscape software to construct a PPI network and visualized the analysis findings. The detailed information is shown in As shown in As shown in The signal pathway was displayed by KEGG pathway diagram, where each node represents a protein, gene, or enzyme. Arrows represent upstream and downstream relationships. Red indicates the protein, gene, or enzyme in the regulatory network. White indicates that the protein, gene, or enzyme was not present in the regulatory network. The detailed information is shown in Fentanyl is an opioid receptor agonist, which is a powerful narcotic analgesic. Its mechanism of action has not been fully understood. It has been observed that opiates can selectively inhibit the impulse transmission of some excitatory nerves, play a competitive inhibitory role, and thus relieve the feeling of pain and the accompanying psychological behaviour reaction. Fentanyl's mechanism of action is similar to morphine, but the effect is 60–80 times stronger than morphine. Its effect is quicker, and its maintenance time is short compared with morphine and pethidine. It does not release histamine and has a little effect on cardiovascular function. It can inhibit the stress response during endotracheal intubation. When fentanyl is consumed in excess, however, it can cause drowsiness, confusion, and nausea, followed by addiction, low blood pressure, and finally, death from respiratory depression. At present, the specific mechanism of fentanyl action is not clear.μ receptors, fentanyl has a higher content of μ receptors and has a good anesthetic effect [As is known to all, a network-based interaction method promotes further understanding of the drug's role in multilayered information when studying active pharmaceutical ingredients . Throughc effect .In PPI and Venn diagram, we explored the top five targets, namely, ALB, SRC, EGFR, IGF1, and ESR1, which were important targets for fentanyl to relieve pain. Because fentanyl use varies greatly among individuals, sensitivity testing of patient-related targets can be used as a standard for assessing fentanyl effectiveness and safety when the role of these targets is fully confirmed by studies. The study of related targets also contributes to the development of new analgesic sedatives. According to the bar chart and bubble chart of the GO database, the reaction of patients to antibiotics and sterol hormones will affect the action pathway of fentanyl. This may provide a new direction for our clinical work. When using fentanyl for analgesia, the patient's current medication, such as the use of relevant antibiotics and hormones, should be fully considered. This allows for more precise medical interventions to ease pain and prevent overdoses or underdoses. For the addiction and adverse reactions of fentanyl, we should not only treat the symptoms but also look for breakthroughs from the target of drug action. We found that fentanyl interacts with the neuroactive ligand-receptor interaction pathway, dopaminergic synapse, and calcium signal pathway signaling pathway. They are all related, so we can start with those pathways and find breakthroughs.Last but not least, although the study has some limitations due to incomplete database information, the unclear concentration of compounds, and interactions among compounds, it can provide new ideas and new directions for further exploration of related experiments.This study preliminarily identified the important proteins and metabolic pathways related to fentanyl in pain treatment, which is expected to provide new evidence and research ideas for the use of fentanyl, enhancing effects and alleviating adverse drug reactions.


Objective: The aim of the present study was repositioning of ivermectin in treatment of gastric cancer (GC) by computational prediction based on gene expression profiles of human and mouse model of GC and validations with in silico, in vitro and in vivo approaches.Methods: Computational drug repositioning was performed using connectivity map (cMap) and data/pathway mining with the Ingenuity Knowledge Base. Tissue samples of GC were collected from 16 patients and 57 mice for gene expression profiling. Additional seven independent datasets of gene expression of human GC from the TCGA database were used for validation. In silico testing was performed by constructing interaction networks of ivermectin and the downstream effects in targeted signaling pathways. In vitro testing was carried out in human GC cell lines (MKN74 and KATO-III). In vivo testing was performed in a transgenic mouse model of GC (INS-GAS mice).Results: GC gene expression “signature” and data/pathway mining but not cMAP revealed nine molecular targets of ivermectin in both human and mouse GC associated with WNT/β-catenin signaling as well as cell proliferation pathways. In silico inhibition of the targets of ivermectin and concomitant activation of ivermectin led to the inhibition of WNT/β-catenin signaling pathway in “dose-depended” manner. In vitro, ivermectin inhibited cell proliferation in time- and concentration-depended manners, and cells were arrested in the G1 phase at IC50 and shifted to S phase arrest at >IC50. In vivo, ivermectin reduced the tumor size which was associated with inactivation of WNT/β-catenin signaling and cell proliferation pathways and activation of cell death signaling pathways.Conclusion: Ivermectin could be recognized as a repositioning candidate in treatment of gastric cancer. The computational drug repositioning can be conducted as repurposing with a defined purpose, repurposing with a strategy, and repurposing with confidence by utilizing reference datasets which are disease-based, drug-based, or knowledge-based  is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indications. Repositioned drugs may reveal new targets and pathways that can be further exploited . Advantage-based .Ivermectin was identified in late 1960s, first approved as veterinary medicine and then human medicine in 1980s for the control of parasitic infection. The discovery and development of ivermectin by William C. Campbell and Satoshi Ōmura were recognized by Nobel Prize in Physiology or Medicine in 2015 . IvermecGastric cancer (GC) is the fifth most common malignant disease worldwide with the third highest incidence and mortality rate among all cancers . The 5 yin silico, in vitro and in vivo validations , everolimus (RAD001) and devimistat (CPI-613) in treatment of GC . The aimidations .Surgical biopsies were collected from 16 patients who underwent total/subtotal or distal gastrectomy because of GC since 2012 at St. Olav’s Hospital, Trondheim, Norway . Four biin vivo testing.The mouse model of GC, i.e., the transgenic INS-GAS mice which spontaneously develop gastric cancer, was used . Stomachhttps://www.humane-endpoints.info/en/council-directive-2010-63-eu). The study was approved by The Norwegian Food Safety Authority (Mattilsynet).Animals were housed as four to five mice per cage on wood chip bedding with a 12 h light/dark cycle in a specific pathogen free environment with room temperature of 22°C and 40–60% relative humidity. Animals were inspected daily by investigators and authorized veterinarian using a scoring sheet. Animals should be euthanized at score of 10 if they are emaciated, underconditioned in five consecutive days, or show poor clinical signs  before end of the study. This was done according to the Directive 2010/63/EU in which human primary endpoint are defined as “the earliest indicator in an animal experiment of  pain and/or distress that, within the context of moral justification and scientific endpoints to be met, can be used to avoid or limit pain and/or distress by taking actions such as humane killing or terminating or alleviating the pain and distress ”  were uploaded to Ingenuity Pathway Analysis  together with log2-fold change, p-values and q-values . For mouse samples, RNA sequencing was performed using Illumina HiSeqNS500 instrument (NextSeq 500) at 75 bp with paired end (PE) reads using NS500H flow cells with 25M reads/sample. Paired end forward read length (R1): 81, reverse read length (R2): 81. Downstream processing and analysis of the data was performed in the Bioconductor environment in R. For humans, a total of 47,323 transcripts was assigned to analysis in which 37,489 transcripts were mapped and 9,834 transcripts unmapped by Ingenuity Pathway Analysis (IPA) . For mice, a total of 54,460 transcripts was loaded in which 54,162 were mapped/298 transcripts were unmapped in IPA. For mouse GC after ivermectin treatment, 54,416 transcripts were loaded in which all were mapped in IPA. Filtering of datasets included species (mouse or human) and p-value cut-off (p < 0.05). Gene expression was analyzed using a t-test between tumor and normal tissue in patients, between INS-GAS and WT mice and between INS-GAS mice with and without ivermectin. Genes with a p-value of less than 0.05 were considered to be differentially expressed. Transcriptomics datasets were analyzed using IPA. Molecular networks and canonical pathways were algorithmically constructed based on known connectivity and relationships among genes/proteins/metabolites using Ingenuity Knowledge Base. Local and regulatory z-scores for canonical pathways and diseases and biofunctions that overlapped with the experimental data of the present study were calculated using the formula described previously . Fischer’s exact test was used to calculate a p-value determining the probability that the association between the genes in the datasets from human GC and mouse GC and the canonical pathway or disease/function by chance alone.Total RNA was extracted from the surgical biopsies of patients and harvested stomachs of mice. RNA quality and quantity were obtained using NanoDrop One  and Agilent Bioanalyser. For human samples, RNA microarray of GC samples, including 24 tumors of intestinal, diffuse and mixed types from seven patients and 37 normal tissue from six patients, was performed using Illumina platform as described earlier . Illumineviously . IPA hasn = 7 GC vs. n = 6 normal tissue).The concept of a Connectivity Map (cMap) was recently developed, whereby genes, drugs, and disease states are connected by virtue of common gene expression signatures . To idenz-scores were calculated in IPA based on the dataset’s correlation with the activated state. Negative z-scores indicate a decrease in activity, positive z-scores indicate an increase in activity. Canonical pathways were identified using statistical cut-offs at p < 0.05.Data mining was performed using the gene expression profile data of 61 samples from 16 patients, 26 samples from 26 mice, and 324 samples from seven independent datasets from the TCGA database. Furthermore, knowledge-based pathway mining was used based on previous studies that showed WNT/β-catenin signaling pathway as one of the important pathways in gastric tumorigenesis . Custom-in silico tool integrated with the MAP-function was employed to predict effects on the network after gene inhibition and/or stimulation in the ivermectin cluster. Connections between genes were then algorithmically generated based on their interrelationships including both direct and indirect interactions like chemical-protein interactions, ubiquitination, molecular cleavage, translocation, localization, phosphorylation, expression, protein-protein interactions, activation, regulation of binding, inhibition, membership, reaction, protein-DNA interactions, transcription and modification. Network clusters of WNT/β-catenin pathway was constructed based on the transcriptomic data of INS-GAS mice . The build-tool was used to connect nodes using edges, i.e., relationships. Categorical values were set to each gene/node using a semi-quantitative method to quantify the color-change resulting from in silico inhibition. Local z-scores were calculated in IPA based on the dataset’s correlation with the activated state. Negative z-scores indicate a decrease in activity, positive z-scores indicate an increase in activity. Canonical pathways were identified using statistical cut-offs at p < 0.05.The expression data from mouse GC was compared to all genes in the pathway. The molecule activity predictor (MAP)-function was used to predict activation/inhibition between the nodes in the network. The 2 and 37°C. For proliferation assay, MKN74 and KATO-III were seeded in 96-well plates  and incubated overnight. Ivermectin (MW: 875.09 g/mol) was dissolved in DMSO (100%) to 50 mM stock solution. Cells were treated with ivermectin (0–50 µM) or vehicle control (0.45% v/v DMSO) for 24, 48, and 72 h. Proliferation was measured using a commercial CCK-8 Kit  with absorbance read at 450 nm. For cell cycle analysis, KATO-III cells were seeded as 3.0 × 105 cells/well in 6-well plates and incubated for 72 h with medium change after 48 h. Ivermectin was added to the wells as final concentrations of 12, 15 or 18 µM for 24 h. Cells were harvested by trypsin, washed twice in room tempered PBS, resuspended in ice cold ethanol (70%) and kept at −20°C for minimum 15 min. Cells were washed twice in cold PBS and centrifuged , and resuspended in freshly prepared PI staining solution  and 200 μg/ml RNAase) for minimum 30 min. Cell cycle distribution was analyzed using FACS. Single cells were gated to exclude doublets and clustered cells. 2.0 × 104 cells were counted per sample, and percentage cell distribution was derived from obtained histograms in the FACSDiva software. Results are presented as means of n = 3 replicates/treatments. Data was analyzed using Microsoft Excel 2010.GC cell lines included human gastric cancer cells MKN74  and KATO-III (diffuse type) , and stomachs were collected as described previously  and controls . Ivermectin was reconstituted from lyophilized powder in DMSO to 50 mM solution and then diluted in saline before use. The treatment regimen was designed to let the mice tolerate the procedure easily, i.e., intraperitoneal injection at a dose of 10 mg/kg in a volume of about 0.5 ml/mouse with 27G needle once per day for 5 days, followed by no treatment for 5 days and then injection once per day for 10 days. This regimen was repeated 10 days later. The total duration of treatment was 2 months (2 × 30 days). Vehicle treatment was not performed because neither vehicle eviously . Tumor veviously . The tist-test was used. For comparison of multiple groups, one-way ANOVA with Tukey’s or Dunnett’s post-hoc tests were used . SPSS version 26.0 for Windows  was used and a p-value < 0.05 was considered to be statistically significant. Other methods are stated in corresponding figure legends.Values are expressed as means ± SEM or SD . For comparison of two independent groups, student independent A cMap was created according to the gene expression signature . A totalz-scores (shown in orange)  .in silico interaction network of ivermectin with WNT/β-catenin signaling pathway was constructed . The mice had no serious side effects of ivermectin and no mice that were treated with ivermectin were killed according to the human primary endpoints which include stressful behavior, abdominal pain and impaired physical activity. The tumor size was reduced by ivermectin treatment (z-scores from 1.151 (mouse GC without treatment) down to −1.789 (mouse GC after ivermectin treatment) (2 fold-changes (A treatment regimen using ivermectin at 10 mg/kg for 2 months was established based on the reatment . Comparireatment . The geneatment)  and log2-changes .Expression analysis in IPA revealed that cell proliferation was activated in mouse GC without treatment and inactivated in mouse GC with treatment. On the other hand, cell death including apoptosis was inactivated in mouse GC without treatment but activated in mouse GC with treatment .https://clue.io/connectopedia/l1000_gene_space and https://clue.io/connectopedia/core_cmap_cell_panel).The next generation connectivity map (cMap) has been recently developed and should be acknowledged that the cMap methods and data are available without restriction to the research community . As poinIn addition to the hypothesis generation approach by cMap, we further utilized data mining and pathway mining of knowledge-based datasets to identify the potential drugs in connection with a broad concept ranging from molecular entities (such as genes and proteins) to biological phenomena . Based on a better understanding of GC biology and signaling pathways, in the present study we focused on the WNT/β-catenin pathway by utilizing the algorithms of IPA which is built on a comprehensive, manually curated content of the QIAGEN Knowledge Base .The results of the present study showed that the potential molecular targets and the mechanisms of action of ivermectin in GC differed from those in parasites in which ivermectin causes an influx of Cl-ions through the cell membrane of invertebrates by activation of specific ivermectin-sensitive ion channels . In the in silico modelling to show that ivermectin could inhibit the WNT/β-catenin signaling pathway including HIPPO signaling pathway, which is known to interact each other  (data not shown). However, the impacts of ivermectin treatment after a longer period of treatment alone and/or in combination with chemotherapy on resistance, migration and invasion could be worthwhile for future investigation. The results of the present study showed evidence of possible involvement of WNT/β-catenin signaling pathway in connection with the anti-cancer effect of ivermectin. For instance, prediction of ivermectin was successfully made by the WNT/β-catenin signaling pathway mining but not cMap. Validation of ivermectin was significant in silico model of the WNT/β-catenin signaling pathway. Up-regulation of WNT/β-catenin signaling pathway took place in patients, human cell lines and mouse model of GC. Ivermectin treatment induced downregulation of the WNT/β-catenin signaling pathway in the mouse GC. However, additional evidence is needed to demonstrate that the effect of ivermectin is dependent on WNT/β-catenin signaling pathway. For instance, it would be worthwhile to further investigate how modulation of the WNT/β‐ catenin signaling pathway with specific inhibitors and activators will affect the response to ivermectin treatment in vitro and in vivo.There were several limitations of the present study. The cell proliferation and apoptosis in the The results of the present study show that ivermectin is a promising drug candidate for treatment of GC. The results may indicate an alternative mechanism of action of ivermectin, i.e., inhibition of the WNT/β-catenin signaling pathway in mammals rather than it acts on glutamate-gated chloride channels, which are common in nematodes, insects and ticks, thereby paralysing pharyngeal and somatic muscles. As ivermectin is exceptionally safe for mammals because of the blood/brain barrier, further pre-clinical and clinical studies of repositioning ivermectin for GC are warranted.


This study was aimed at exploring the regulatory mechanism of Xiaoyao San (XYS) and its main compound, Stigmasterol, in the biological network and signaling pathway of ovarian cancer (OC) through network pharmacology-based analyses and experimental validation.  The active compounds and targets of XYS were studied by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The GeneCards and OMIM databases were used to screen common targets of XYS in the treatment of OC. Combined with the STRING database and Cytoscape 3.6.0, the core compounds and targets of XYS were obtained. GO and KEGG pathway enrichment analyses of core target genes were carried out by using the Metascape and DAVID databases. Molecular docking has been achieved by using the AutoDock Vina program to discuss the interaction of the core targets and compounds of XYS in the treatment of OC. The effect of Stigmasterol on proliferation and migration were assessed by CCK8 and wound healing assay. Western blot and qRT-PCR were used to analyze the protein and mRNA expressions of PI3K, Akt, and PTEN after treatment of Stigmasterol.  A total of 113 common targets of XYS for the treatment of OC were obtained from 975 targets related to OC and 239 targets of XYS's effect. The main compounds of XYS include Quercetin, Naringenin, Isorhamnetin, and Stigmasterol, which mainly regulate the targets such as TP53, Akt1, and MYC and PI3K/Akt, p53, and cell cycle signal pathways. At the same time, molecular docking showed that Stigmasterol and Akt1 had good docking conformation. Stigmasterol inhibited OC cell proliferation and migration in vitro and reduced the protein and mRNA expressions of the PI3K/Akt signaling pathway.  Stigmasterol as the one of the main compounds of XYS suppresses OC cell activities through the PI3K-Akt signaling pathway. Ovarian cancer (OC) is the most frequently diagnosed and highest mortality in gynecological cancers. The 5-year overall survival rate was about 40% when most patients were late at the time of discovery because the lack of symptoms was not obvious , 2. CurrXYS from Prescriptions of Peaceful Benevolent Dispensary can soothe the liver, nourish the blood, invigorate the spleen, and harmonize the heart. It is mostly used for hepatic stagnation, spleen deficiency, and blood deficiency syndrome. However, the mechanism of XYS in the treatment of OC is unknown and needs further discussion.With the rapid development of bioinformatics, systems biology, and polypharmacology, network-based drug discovery is considered to be an economical and effective drug development method . In receIn this study, several drug target prediction databases were used to analyze XYS, and the protein-protein interaction network (PPI) of the main targets of XYS in the treatment of OC was constructed to explore its main biological functions and signaling pathways. Finally, the effect of Stigmasterol on the PI3K/Akt signaling pathway was verified by a cell experiment in vitro, which provides a new basis for the development and application of XYS and its compound Stigmasterol.v/v).Fetal bovine serum (FBS) was obtained from HyClone (USA), and RPMI  1640 medium, penicillin, streptomycin, and all other reagents were obtained from GIBCO (USA). Stigmasterol  was dissolved in DMSO and stored at -20°C. The final concentration of DMSO in cultures was ≤0.1% (http://www.tcmspw.com/tcmsp.php) was used to collect the active compounds of XYS, including Bupleurum, Radix Paeoniae Alba, Angelica sinensis, Atractylodes macrocephala, Poria cocos, Glycyrrhiza uralensis, ginger, and peppermint. The active compounds and their targets in the composition of XYS were selected by ADME evaluation systems, the main parameters of which were oral bioavailability (OB ≥ 30%) and drug-likeness (DL ≥ 0.18). Through the STRING database (https://string-db.org/) [https://www.genecards.org/) and OMIM (https://omim.org/) were utilized to collect genes and targets related with OC [http://bioinformatics.psb.ugent.be/webtools/Venn/)  .https://www.helixlife.cn/.The PPI network was obtained from the above 113 common targets by STRING and “Homo species” was selected. The minimum interaction threshold was selected as “medium confidence >0.4.” The Cytoscape 3.6.0 software was used to analyze the core targets of the network, and “PNG” format was the output . The tophttp://metascape.org/) and DAVID database (https://david.ncifcrf.gov/); P < 0.05 was significant.The GO and KEGG enrichment analyses were obtained from the above 113 common targets by the Metascape database (Cytoscape 3.6.0 was used to construct the “herb-active compound-target” network of XYS in the treatment of OC. We identify the top 10 active compounds as the core compounds of XYS in the treatment of OC.http://www.rcsb.org/), and the 3D structures of the core compounds were downloaded from the TCMSP database. The molecular docking and conformation scoring were carried out in the AutoDock Vina.The interaction between the core compounds and targets of XYS in the treatment of OC was checked by using molecular docking technology. The target protein structures were downloaded from the PDB website  and transformed them into transcripts per million read (TPM) format and then analyzed them according to the molecular expression.We utilized R3.6.3 software to carry out the receiver operating characteristic curve (ROC curve) and Kaplan-Meier curve (KM curve) enrichment analyses. We prepared the RNAseq data and clinical data from the ovarian serous cystadenocarcinoma (OV) project in TCGA (v/v) FBS and 100 U/ml penicillin/100 mg/ml streptomycin at 37°C in the 5% CO2 atmosphere.A2780 and SKOV3 cell lines were cultured in RPMI1640 medium with 10%  Stigmasterol for 24, 48, and 72 hours at 37°C. The OC cells were replaced with a 10% Cell Counting Kit-8  diluted with normal culture medium and incubated for 1 h. The absorbance of each well was measured with a Motic digital medical image analysis system  at 450 nm. All tests were repeated three times [Ovarian cancer A2780 and SKOV3 cells were seeded into 96-well plates with gradient concentration . qRT-PCR was performed by using a Real-Time PCR System  in reaction mixtures (25 μl) containing cDNA, primer pairs  according to the manufacturer's instructions. RNA concentration and purity were assessed by spectrophotometry at 260 nm. A total of 1 er pairs , and TB β-actin . Next, the membranes were incubated with secondary antibodies of goat anti-rabbit IG  for 1 h at 37°C. The intensity of protein bands was estimated via executing the Fusion FX5 Spectra .A2780 and SKOV3 cells were treated with Stigmasterol for 48 h. After washing with PBS three times, the cells were lysed for 30 min on ice and centrifuged at 10000 × g for 5 min at 4°C. The contents of segregated proteins in cell lysates were quantified using an ND-1000 Spectrophotometer . Equal amounts of protein samples were electrophoresed through 10% SDS-PAGE (polyacrylamide gel electrophoresis) and transferred to PVDF membranes . After 5% BSA block for 2 h, they were immunoblotted with primary antibodies for overnight at 4°C. The primary antibodies include p-Akt , Akt , p-PI3K , PI3K , PTEN , and In The GO and KEGG pathway of targets of XYS in the treatment of OC were involved in 338 signaling pathways, including the PI3K-Akt signaling pathway, bladder cancer, pancreatic cancer, hepatitis B, microRNAs in cancer, and HIF-1 signaling pathway. The top 20 pathways are shown in This network graph directly evaluates the mechanism of XYS in the treatment of OC through multicompound and multitarget synergistic action. The herb-active compound-target network has shown the top 10 active compounds of XYS in the treatment of OC including Quercetin, Kaempferol, and Stigmasterol .The top 10 main compounds of XYS were docked with the protein structures of Akt1, TP53, and MYC. The results showed that Stigmasterol was the best binding ability to Akt1 and the Vina score (ΔG) was -10.60 kca1/mol . The molWe further explored the critical efficiency of Akt1 in the survival of patients with OC. The ROC curve analysis for Akt1 in OC determined that AUC of the ROC curve is 0.702. The Kaplan–Meier curve and log-rank test analysis revealed that the increased Akt1 mRNA levels were significantly associated with the overall survival (OS) of OC patients . Higher P < 0.05). The IC50 values are shown in μM and 50 μM for 48 h.The CCK8 assay result suggested that Stigmasterol could significantly inhibit A2780 and SKOV3 cell proliferation in a dose-dependent manner   .P < 0.05)  . This reIn traditional Chinese medicine, XYS has been proposed for anticancer activities and was used in the treatment of breast hyperplasia, breast cancer, and OC by reinforcing qi and nourishing the blood. XYS is a classic Chinese prescription which was first described in the Prescriptions of Peaceful Benevolent Dispensary in the Song Dynasty (A.D. 1151).β-sitosterol, Kaempferol, and Stigmasterol. Previous studies indicated that Quercetin has the anticancer activities involved in downregulating the levels of RAS, BCL-2, mutant P53, and upregulating BAX [β-sitosterol has the wide anticancer properties such as inducing apoptosis and inhibiting proliferation, invasion, metastasis, and angiogenesis in colon cancer, ovarian cancer, lung cancer, breast cancer, and prostate cancer. β-Sitosterol exerted against ovarian cancers by estrogen reabsorption. Also, β-sitosterol decreased the expression of β-catenin, PCNA (proliferating cell nuclear antigen), and Bcl-2 in colon cancer [In this study, as the result of the network pharmacology, XYS possessed the multicomponent, multitarget synergistic effect in the treatment of OC. The main active compounds of XYS in this network include Quercetin, ting BAX . Studiesn cancer . Kaempfen cancer ; Stigmasn cancer .The PPI network of XYS in the treatment of OC indicated that the core targets include Akt1, TP53, and MYC. Therefore, we used the database to analyze the expression of the above 3 hub genes in OC tissues and normal tissues. The results showed that Akt1 and TP53 were highly expressed in OC tissues and MYC expression was lower in OC tissues than in normal tissues. GO and KEGG pathway enrichment analyses of XYS in the treatment of OC also found that the PI3K/Akt signaling pathway plays a role in OC signaling pathways. From the molecular docking result, Stigmasterol showed a strong interaction with Akt1. Meanwhile, the expression of Akt1 in OC is closely related to OS of clinical patients. Therefore, we predict that Akt1 may be a potential core target of Stigmasterol for the treatment of OC.In clinical researches, the dysregulations of the PI3K/Akt signaling pathway were found in 70% OC, involving PTEN deletion and PIK3CA mutations . GeneticBased on these results, we further verified the effect of Stigmasterol on the PI3K-Akt signaling pathway in OC cell lines A2780 and SKOV3. According to the results of the CCK8 assay and the wound healing assay, the effect of Stigmasterol could significantly inhibit the proliferation and migration in A2780 and SKOV3 cells. Furthermore, Stigmasterol can reduce the levels of PI3K and Akt and increase the expression of PTEN in A2780 and SKOV3 cells. This research not only provides a theoretical and experimental basis for more in-depth studies but also offers an efficient method for the rational utilization of a series of Chinese medicine's active ingredients as anti-tumor drugs.In summary, Stigmasterol, as the main active compound of XYS, could predict a candidate compound in OC therapy by targeting the PI3K/Akt signaling pathway.


An increasing number of studies now produce multiple omics measurements that require using sophisticated computational methods for analysis. While each omics data can be examined separately, jointly integrating multiple omics data allows for deeper understanding and insights to be gained from the study. In particular, data integration can be performed horizontally, where biological entities from multiple omics measurements are mapped to common reactions and pathways. However, data integration remains a challenge due to the complexity of the data and the difficulty in interpreting analysis results.p values) and fold changes. Context-sensitive panels provide information on the currently selected entities, while interactive heatmaps and clustering functionalities are also available. As a case study, we demonstrated how GraphOmics was used to interactively explore multi-omics data and support hypothesis generation using two complex datasets from existing Zebrafish regeneration and Covid-19 human studies.Here we present GraphOmics, a user-friendly platform to explore and integrate multiple omics datasets and support hypothesis generation. Users can upload transcriptomics, proteomics and metabolomics data to GraphOmics. Relevant entities are connected based on their biochemical relationships, and mapped to reactions and pathways from Reactome. From the Data Browser in GraphOmics, mapped entities and pathways can be ranked, sorted and filtered according to their statistical significance (https://graphomics.glasgowcompbio.org/. It can be used to integrate multiple omics data horizontally by mapping entities across omics to reactions and pathways. Our demonstration showed that by using interactive explorations from GraphOmics, interesting insights and biological hypotheses could be rapidly revealed.GraphOmics is fully open-sourced and freely accessible from  The availability of high-throughput technologies means many studies are increasingly producing large-scale untargeted measurements of different biological entities, such as transcripts, proteins and metabolites. Combining the diverse set of omics data produced from different measurement platforms is often required as the initial step of an integrated analysis. Data integration has been shown to reveal stronger findings compared to analysing a single dataset alone, with wide-ranging successes from studying the human microbiome to identifying cancer biomarkers –3.Omics integration approaches can be divided into two types: vertical where integration is performed by using multiple omics data from the same biological sample; and horizontal where the integration is performed by mapping shared or related entities from different biological samples . One popDuring vertical integration, often it is required for different omics measurements from the same sample to be matched. However in some instances, existing data cannot be matched in this manner, since not all omics types were measured due to limitations in the study. Horizontal integration offers an alternative scheme, where integration is performed by mapping shared or related entities from one omic dataset to another without requiring for samples to be aligned. Instead biological pathways could serve as the shared context onto which entities are mapped.In recent years, Web-based tools to perform horizontal integration using pathways have been gaining popularity. For example MetaboAnalyst , consideDespite this abundance of tools, data integration remains a challenge due to the complexity of the data, and the difficulty in relating analysis results to biological interpretations. A common approach employed by many tools is to present an analysis outcome as a complex network graph –10. NetwHere we introduce GraphOmics, a Web application that accepts measurements of transcripts, proteins and metabolites and performs data integration horizontally using Reactome  as the gFigure DeSEQ2 _vs_[group2] for fold change information, and padj_[group1]_vs_[group2] for p values, with [group1] and [group2] referring to the different experimental groups. For more details on the input format, please refer to Supplementary S1.Differential analysis results from outside GraphOmics can also be included during upload. This takes the form of additional fold-changes and statistical significance . This retrieves the connections between transcripts, proteins, metabolites to reactions and pathways of the given species in Reactome, constructing a network graph of entities, reactions and pathways involved in the dataset. Entities in this network graph are connected to one another: transcripts are linked to the proteins they encode, proteins and compounds are linked to the reactions they are involved in, and reactions are linked to the pathways that contain them.Mapping is done using Reactome based on the species that users selected during data upload. The list of species is currently limited to the 84 species that Reactome supports (database version 77) at the time of writing. Mapping coverage in GraphOmics could grow as Reactome is regularly updated to incorporate more species and biological entities. Once mapping is completed, the results are stored in the SQLite database and presented to users in the Linked Data Browser. GraphOmics uses other databases such as Ensembl, Uniprot, ChEBI and KEGG; these are not used for mapping, but instead are used to retrieve additional contextual information about selected entities in the Info Panel.The Data Browser is the primary interface in GraphOmics that facilitates linked exploration of the integrated data. Instead of presenting an often-massive network graph, the main components of the Data Browser are five interactive tables: one for each supported omics type  as well as for reactions and pathways Fig. .Users interact with the Data Browser by navigating through the tables. Clicking an entity in the Data Browser selects it, and multiple entities can be selected in this manner. Selections from one table will filter entries in other tables, such that only connected items are shown according to the links between entities. As more entities of different omics types are added to the current selection, the number of entities displayed across tables are reduced to meet the filtering criteria.In this manner, users can explore the data starting from a global view where all entities are shown, and successively narrowing down to more specific entities that are related to the selected items. This ‘drill-down’ interactivity in the Data Browser could help reveal the relationships among biological entities of interest and their reactions and pathways across omics.p values from the Data Browser. Significant features could now be explored in relation to active pathways , and in relation to clustering with other significant features in the integrated Clustergrammer views. This provides a starting point for hypothesis generation.In the case where users explore the data with no particular features in mind, GraphOmics allow users to perform differential analyses to highlight significantly changing entities, as well as pathway activity analyses to highlight potentially interesting biological processes. This generates an initial list of significantly changing features, which could be ranked by fold changes and Selected entries in the Data Browser are also associated to contextual information under each table Fig. . This inp values. This can be used to explore the most significantly changing entities across omics that are differentially expressed (DE). In conjunction with linked interactions, the interface allows users to easily navigate through the top DE entities from one omics and inspect if they are linked to DE entities from other omics. Entities are also connected to pathways, which can be subjected to enrichment analysis within GraphOmics. In this manner, users can easily rank DE entities and determine which enriched pathways they are connected to. Additionally the Query Builder in GraphOmics allows for complex queries to be defined on the data  of each constituent single-omics query.All interactive tables in the Data Browser allow entities to be ranked and sorted according to their fold changes and ata Fig. . From thGraphOmics allows for any set of entities that have been selected by users to be saved as a selection group. These groups can later be loaded for future use. A group of related entities  can be defined, saved and loaded for future analysis. Selection groups can be easily visualised and plotted. For transcriptomics data, gene ontology analysis can be performed using the Python package GOATools  to discop values) of entities could be uploaded as part of the data loading process. Otherwise from the Inference page in GraphOmics, users can execute standard uni-variate t-tests . Additionally, widely-used methods such as DeSEQ2 and limma can also be run as an option. The resulting statistical significance from performing DE analyses are shown in the interactive tables of the Data Browser, alongside the entity names and measured values.A common task in omics data analysis is to find entities that are differentially expressed (DE) across different experimental conditions. If users have performed their own DE analysis, the statistical significance  in the Data Browser is associated to a Clustergrammer component Fig. . ClusterClustergrammer integration means users can generate a heatmap and perform cluster analysis for any selections in the Data Browser. For instance, this includes the ability to display the heatmap of entities in a pathway , or to discover the clusters of proteins and metabolites linked to top DE transcripts. The interaction also goes the other way, such that selecting a cluster in Clustergrammer also selects its member entities in the Data Browser. This allows users to examine the DE members of a cluster and their connections to reactions and pathways.PCA can be used to assess the global similarity of samples across different conditions. In GraphOmics, a PCA analysis is created from the Inference page by selecting the omics type and the number of components to use. The results from PCA analysis include plots of the projected samples for the first two principal components, as well as a scree plot showing the percentage of variance explained by the different components. The latter plot can be examined to determine how many components to retain for analysis.Enrichment of a pathway often suggests relevant biochemical activities happening in that pathway. In GraphOmics, pathway activity analysis can be performed by considering a single omics dataset separately, or from multiple omics datasets at once. To prioritise changing pathways in single omics data, we developed a Python library named PALS  that preThe three pathway ranking methods in PALS represent a diverse approach to enrichment analysis. ORA is widely used to assess the probability of over-representation of DE entities in a pathway using the Hypergeometric test. GSEA is considered a ‘second-generation’ method that takes into account the correlation between sets of entities to assess DE pathways. Finally PLAGE is a method based on singular value decomposition which was found to be best performing  in returp values of significantly changing pathways are collected and displayed with pathway names in the Data Browser. This allows pathways to be ranked, sorted and filtered in the same manner as entities.From the Inference page, users can choose to run any of these methods on the GraphOmics server. For any of the pathway ranking methods, the GraphOmics offers a way to perform pathway analysis separately on each omics, and integrate the results at the end. The separate pathway analysis results run on different omics datasets and can be combined with an AND operator in the Query Builder. For instance from the Query Builder, users can easily filter pathways that are significantly changing based on the transcriptomics AND proteomics AND metabolomics measurements.p values, which defaults to p values are retrieved in GraphOmics and displayed on the Data Browser for sorting and filtering. Note that Reactome will delete a submitted analysis on their server after a period of inactivity (7 days). In this case, users could resubmit the analysis from GraphOmics to Reactome to generate updated Reactome links that work.For a different approach that considers multiple omics data together during analysis, users can run the Reactome Analysis Service, which offers a high-performance multi-omics over-representation analysis using the Reactome server . The IDsp values, fold-changes). For tabular results, this can be accomplished by clicking on the Export button in the respective tables of the Data Browser. Results from interactive heatmap and clustering could also be exported by clicking on the ‘Take snapshot’ button in each Clustergrammer component.GraphOmics allows users to export the mapping results of all entities, as well as their corresponding secondary information , while in PaintOmics3 explorations are centered around DE pathways as the starting point. The linked views in GraphOmics reveal the explicit individual connections between all connected entities for easy inspections, while in PaintOmics3 these connections are summarised as edges between pathways in the network graph. From the Information Panel, GraphOmics displays more contextual information for each selected entity than PaintOmics3. Integration with Clustergrammer also means any clusters of entities can be identified and visualised as heatmaps, and their connections to others displayed in the Data Browser. This is a capability not present in PaintOmics3.Using a public Zebrafish dataset , we demoThe processed transcriptomics, proteomics and metabolomics data from the original study was retrieved. For each omics type, a measurement CSV was created where rows corresponded to the entities and columns were the samples. Each row was identified by a unique identifier column, with ENSEMBL gene ID, UniProt ID and KEGG ID used for identifying transcripts, proteins and metabolites respectively. Positional memory is established by molecules that exist in a gradient along the uninjured appendages, so the measured samples were divided into three experimental conditions according the proximity in the fins where the sample was obtained: proximal, middle and distal . Following the original study, we focused on the comparison of distal-vs-proximal where the largest differences could be seen.CSV files for the multi-omics Zebrafish data was uploaded to GraphOmics. Automated mapping was performed by GraphOmics, resulting in 8690 transcripts linked to 8010 proteins and 462 compounds across 6995 reactions and 1272 Reactome pathways. The original processed transcriptomics data already contained DeSEQ2 analysis results comparing distal to proximal which were retained during upload and used as the DE results for the transcripts. This demonstrates how additional analysis from an external workflow could be easily incorporated into GraphOmics.limma to perform the DE analyses of proteins and metabolites. PLAGE was used to perform DE analysis of pathways using each omics data separately as the input, resulting in different sets of p values for each pathway depending on the source data used. This was all performed from the Inference tab in GraphOmics. All results from DE analysis in form of p values and fold-changes (if available) are displayed in the Data Browser alongside the entities.Differential expression analysis is often used to highlight significantly changing entities that could be of biological interest. From the original study, DE results were already available for the transcripts and so they were used. For the protein and metabolite data, we employed p values, and having at least aldh1a2 which catalyses the synthesis of retinoic acid, as well as muc5.2 found to be retained in both uninjured and early stages of injuries. Both genes were hypothesised in the original study to be involved in establishing positional memory in zebrafish.Here we showed how GraphOmics easily characterised the set of DE transcripts linked to DE proteins. This could be used to identify the important transcripts and proteins that are involved in establishing positional memory of zebrafish. The following query was formulated from the Query Builder: filter for transcripts and proteins with a threshold of 0.05 on the pts Fig. . The resoxidation-reduction process (GO:0055114) was found to be significantly-enriched in the top-4 GO results for biological processes , clear block structures could be observed across the distally-enriched and proximally-enriched entities. These are the transcripts and proteins that could potentially contribute to patterned regeneration in zebrafish tissues. The clustering structure in the linked compounds are less clear, suggesting that the relationship between transcript and protein expression to metabolism during regeneration is a complex process. For more details, refer to Supplementary Figure S2limma) that are also linked to highly active pathways (as determined by PLAGE). A threshold of p values of both DE metabolites and pathways. This resulted in 45 DE metabolites spread across 57 DE pathways, listed in Supplementary Table S3. Among the significant pathways of interests are Alanine metabolism which makes sense as both alanine and glutamate were DE in the data. Consistent with the original study, Arginine is observed to be producing the largest DE amongst the significant compounds, alongside other compounds like glutamine and leucine. These are explained in the original study as promoting wound healing and encouraging cellular growth [The original study  did not r growth .p values G protein-coupled receptor signaling pathway (GO:0007186) and signal transduction (GO:0007165), showing that the activity level of signalling pathways are high. The findings here support the hypothesis in the original study on the influence of signalling pathways towards positional memory.To obtain descriptive terms that characterise the overall biological processes of these metabolic pathways, we performed GO analysis on the 236 DE transcripts  study on the sera of Covid-19 patients . The purThe original study aimed to characterise the proteome and metabolome of a cohort of 28 severe Covid-19 patients in comparison to a cohort of 28 healthy patients. Processed protein and metabolite data from the original study were retrieved. The protein data was provided in a format acceptable to GraphOmics (with rows identified by their UniProt ID) and could be readily uploaded. For metabolite data, each compound was identified by its chemical name in the original data. An automated script (available from our repository) was created to map from compound names to KEGG ID using the Bioservices library . Of the limma, while PLAGE was used to analyse pathway activity levels on both omics types.Similar to the previously analysed Zebrafish data, DE analysis were performed on the Covid19 protein and metabolite data using p values p values Once the initial data integration has been performed in GraphOmics, users could interactively explore the data to reveal biologically relevant hypotheses. Firstly to discover significantly changing entities, the Query Builder was used to filter for DE proteins  and Alternative complement activation (R-HSA-173736). Note that while pathway analysis in the original study was performed using a completely different proprietary software [IPA, Platelet degranulation (R-HSA-114608) pathway. This was also found to be significant in our results, and it could be explained by how platelets produced in the lung were activated in response to lung injury in the severe patients. All these significant pathways and their connections to DE entities can be browsed through GraphOmics.Among the significant pathways in the results, two were related to the activation of the complement system, including are IPA, , our resPlatelet degranulation pathway from the Data Browser. This selected the DE proteins linked to that pathway and all their related entities. From the corresponding Clustergrammer view, two clusters of proteins that are either up-regulated or down-regulated in the severe-vs-healthy comparison could be observed . The protein P02776  was a member of the down-regulated cluster. The presence of PF4 in the down-regulated cluster was interesting because changes to PF4 was noted in the original study to be a prognosis marker in severe acute respiratory syndrome [Platelet degranulation and to members of this cluster, and the down-regulation of serotonin could be interactively seen and explored from the Data Browser.We further illustrated how GraphOmics could identify other significant entities that are linked to those groups of DE proteins discovered above. Keeping the same filtering criteria, we selected the syndrome . Its dowp values Finally we investigated the metabolomics data by filtering from the Query Builder for DE metabolites linked to DE pathways , and otProject name: GraphOmics.https://graphomics.glasgowcompbio.org/.Project home page: Archived version: 10.5281/zenodo.5017219.Operating system(s): Platform independent.Programming language: Python, JavaScript.Other requirements: Python 3 or higher.License: MIT.Any restrictions to use by non-academics: none.Additional file 1. Supplementary data S1–S6.


Active compounds of LJP were examined established on the analysis platform, Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. DrugBank identified drug targets and annotated them on UniPort and GeneCards. Besides, the COM-related genes were identified on GeneCards. The network of the drug, main active compounds, targets, and diseases was built utilizing Cytoscape. STRING was utilized to build the protein-protein interaction network. Moreover, the KEGG and GO pathway enrichment analysis were applied to analyze biological function.  23 active compounds of LJP were screened, and 204 drug targets and 686 COM-related genes were identified. Forty-five intersection genes were overlapped from 204 drug targets and 686 COM-related genes. The drug-active compounds-target protein-diseases network was established based on 23 active compounds of LJP and 45 intersection genes. Moreover, the interaction of 45 intersection genes was explored by the PPI network, and the drug-active compounds-target protein-diseases network was formed grounded by 23 active compounds of LJP, 45 intersection genes, and PPI network. The KEGG and GO pathway enrichment analysis specified that 45 intersection genes primarily enriched in immune-related pathways and oxidative stress-related pathways.  In the research done, the main active compounds of LJP and drug targets in the treatment of COM were identified. Our findings might provide the ingredient option of LJP and drug targets of LJP in COM treatment.  Chronic osteomyelitis (COM) is an autoinflammatory condition which characteristics as osseous infection and with or without neighboring soft tissues infection . It is dLonicera japonica (LJP) is a TCM that is widely used for the treatment of fever, carbuncle, and tumors, and so on; including COM, it significantly exhibits functions of antioxidation, antibacteria, anti-inflammation, and immunoregulation [In recent years, traditional Chinese medicines (TCMs) show obvious therapeutic effects and advantages in many disease treatments , 6. For gulation , 10, 11.Network pharmacology is a combined multidisciplinary idea investigating synergistic impacts and potential methods of various mixtures via evaluating complicated and multilayered networks, that is a pharmacology and pharmacodynamics-based novel research field . In the https://tcmspw.com/index.php) [LJP active components were examined from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  ; furtherdex.php)  identifidex.php)  was to dLJP active compound targets were screened in DrugBank , and thehttps://www.GeneCards.org) through querying with keywords chronic osteomyelitis and COM.The COM-related genes were screened by GeneCards databases  was incorporated to analyze COM-related target proteins for PPI network construction. Protein-protein interactions with confidence score ranges were predicted from the STRING database  [Online STRING 11.0 (ce >0.9) .P value <0.05  [KEGG pathway analysis and GO function were completed using cluster Profiler R package 3.8.1 with FDR <0.2 and ry rate) . GO annoThe TCMSP database (satisfied the criteria of OB ≥ 30% and DL ≥ 0.18) was applied for the identification of twenty-three active compounds of LJP . Among tβ, IL-10, IFNG, NLRP3, CYBB, TERT, and TNFSF11 , chemokines , and other genes. We further constructed the network of drugs, compounds, targets, and diseases. The network indicated that beta-sitosterol, beta-carotene, luteolin, kaempferol, and quercetin were the primary active compounds of LJP in COM treatment. Moreover, the PPI network of 45 intersection genes was constructed. ALB, AKT1, IL-6, VEGFA, MMP9, CCL2, IL-1β release and inflammasome activation [β and IL-6 are the main COM-related genes identified in our study. As important proinflammatory cytokines, IL-1β and IL-6 directly participate in osteoclast activity regulation and bone desorption that happen in osteomyelitis and could consequently be a vital part of its pathogenesis [β. In the study of Soung, postoperative exudates of chronic suppurative osteomyelitis demonstrated constantly enhanced angiogenesis-related proteins expression, including VEGFA [Staphylococcus aureus (S. aureus) internalization by osteoblasts [According to our result, luteolin is a main activate compound from LJP, and its target proteins include IL-6, IL-10, MMP9, VEGFA, AKT1, EGFR, and so on. IL-10 mostly bears immune regulatory functions. Sigrun et al. reported that reducing the expression of IL-10 contributes to enhanced subsequent-1tivation . IL-1β aogenesis . So, we ng VEGFA , which ing VEGFA . It was ng VEGFA , and a cng VEGFA , 33. MMPng VEGFA . It has ng VEGFA . Ji et aeoblasts , which iS. aureus infection enhanced TLR2 expression and triggered both JNK MAPK and ERK signaling pathways [S. aureus is a major bone infection triggering pathogen that could generate a sizable quantity of powerful pathogenic ingredients, staphylococcal protein A (SPA). Wang et al. found that c-FOS and NFATc1 expression was upregulated by SPA via MAPK signaling activation to promote osteoclastogenesis [α and IL-1β result in NOS2-pathway activation, and pathway-derived NO promotes cytokines and inflammation-induced bone loss [According to our result, chryseriol is another main activated compound from LJP, and its target proteins include MAPK4, NOS2, PTGS2, and so on. The MAPK signaling pathway has been recognized to be engaged in osteomyelitis and can regulate differentiation, cell growth, and various types of inflammatory reactions . Wu et apathways . S. aureogenesis . So, we one loss . Howeverone loss , 40.In summary, LJP targets and active compounds in COM treatment were identified in our study. Luteolin, chryseriol, and other chief active components may perform therapeutic properties through IL-6, IL-10, MMP9, VEGFA, AKT1, EGFR, MAKP, and so on targets regulation. Additional research needs to be accomplished to clarify LJP active compounds' specific therapeutic mechanisms. These works may have a great help for precise treatment and efficient drug selection in COM.


The application of network pharmacology and molecular docking aimed to exhibit the drug targets and pharmacological mechanisms of luteolin. In this study, we found the top 20 up- and downregulated gene expressions in PC patients. Enrichment data demonstrated anti-inflammatory effects, where improvement of metabolism and enhancement of immunity were the main functions and mechanism of luteolin in treating PC and COVID-19, characterized by associated signaling pathways. Additional core drug targets, including MPO and FOS genes, were computationally identified accordingly. In conclusion, luteolin may be a promising treatment for PC and COVID-19 based on bioinformatics findings, prior to future clinical validation and application.Coronavirus disease 2019 (COVID-19) is a serious epidemic, characterized by potential mutation and can bring about poor vaccine efficiency. It is evidenced that patients with malignancies, including prostate cancer (PC), may be highly vulnerable to the SARS-CoV-2 infection. Currently, there are no existing drugs that can cure PC and COVID-19. Luteolin can potentially be employed for COVID-19 treatment and serve as a potent anticancer agent. Our present study was conducted to discover the possible drug target and curative mechanism of luteolin to serve as treatment for PC and COVID-19. The differential gene expression of PC cases was determined  Epidemiological findings suggest that coronavirus disease 2019 (COVID-19) has evolved and mutated around the world, leading to higher fatality rates . Therefoin vitro data suggest that luteolin restrains viral-caused inflammatory stress | >1 to identify the differential expression genes. The top 20 significantly upregulated and downregulated genes were obtained for drawing the differential gene volcano map. The associated genes of COVID-19 were selected from the GeneCards database, Online Mendelian Inheritance in Man (OMIM) database, and NCBI gene function module. Finally, these genes were compared to obtain the overlapping targets in prostate cancer and COVID-19 through the “Venn diagram” package of R language , Comparative Toxicogenomics Database (CTD), Swiss Target Prediction, and PharmMapper  were usep- and q-values was set to 0.05 for enriching and plotting the bubble chart, bar chart, and circle chart , biological process (BP), and KEGG pathways of the overlapping gene targets of luteolin in PC/COVID-19. The cutoff value for both the le chart . Cytoscale chart .via the STRING database to obtain the network interaction relationship between the target–target function-related proteins and the target protein–protein interaction (PPI) network diagram  and FOS core targets were selected for molecular docking. The structure of luteolin was obtained from the PubChem database, and the protein structure associated with core genes was acquired from the PDB database. Using the ChemBio3D Draw module in the ChemBioOffice 2010 software, three-dimensional structures of these compounds were optimized for the molecular force field 2 (MM2). The Autodock Tools 1.5.6 tool of Autodock software was used to process the corresponding proteins, including hydrogenate, Gasteiger charge, and merge non-polar hydrogen. The original pdb file format was converted into the pdbqt file format recognized by the Autodock Vina program, performing a ligand basis for molecular docking. Based on the root mean square deviation (RMSD) between the docked and original ligand molecule, the rationality of the docking parameter settings could be assessed. It was generally concluded that RMSD ≤4 Å was the threshold for the conformation of the ligand to match the conformation of the original ligand after docking , 26.TCGA-RNA sequencing analysis showed that a total of 550 samples, consisting of 52 normal samples and 498 tumor samples, were obtained for comparative determination. The data included 2,618 significantly differentially expressed genes in PC. Furthermore, the top 20 distinctly upregulated and downregulated genes were used to display the differential gene volcano map Figure 1The drug targets of luteolin were searched through the TCMSP database, CTD, and other databases, and 294 targets of luteolin were ascertained after the removal of duplicates and correction by the UniProt database. Finally, the drug target and disease intersection target were mapped and uploaded to the Venn diagram analysis, and a total of 18 intersection targets were obtained Figure 3After performing GO and KEGG pathway enrichment analyses using intersection targets through R language-related packages, output data were summarized and exhibited in the GO-linked bubble chart (P < 0.05) were chiefly involved in hepatitis B, influenza A, focal adhesions, colorectal cancer, human immunodeficiency virus 1 infection, endocrine resistance, choline metabolism in cancer, AGE-RAGE signaling pathway in diabetic complications, pancreatic secretions, Chagas disease (American trypanosomiasis), parathyroid hormone synthesis, secretion and action, HIF-1 signaling pathway, TNF signaling pathway, sphingolipid signaling pathway, AMPK signaling pathway, Yersinia infection, relaxin signaling pathway, aldosterone-regulated sodium reabsorption, apoptosis, and estrogen signaling pathway. In addition, drug targeting genes–GO findings, including the BP, CC, and MF-pathway–disease, were exhibited in the visualization graph , National Natural Science Foundation of China (No. 81770759), Major Project of Guangxi Innovation Driven (No. AA18118016), and Guangxi Key Laboratory for Genomic and Personalized Medicine .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.