Applying on a list of 120 traditional Chinese medicines (TCM), we especially identified the “TCM–pathways–cancers” triplet and constructed it into a heterogeneous network across pan-cancers. Applying them into TCMs, the computational prescribing methods deepened the understanding of the efficacy of TCM at the molecular level. Further applying them into Western medicines, CDP could promote drug reposition avoiding time-consuming developments of new drugs.Pathway-level analysis is a powerful approach enabling the interpretation of post-genomic data at a higher level than that of individual molecules. Molecular-targeted therapy focusing on cascade signaling pathways has become a new paradigm in anticancer therapy, instead of a single protein. However, the approaches to narrowing down the long list of biological pathways are limited. Here, we proposed a strategy for  Advances in RNA sequencing technologies provide unprecedented capacity to comprehensively identify altered genes and pathways implicated in tumorigenic processes, raising the hope of extending targeted therapies –3. MonitTumor innate or acquired resistance leads to treatment failure . HoweverThe high failure rate of chemotherapy and the tread toward multitarget paradigms emphasize the importance of discovering novel effective therapies. Discovered pathways have been proposed to accelerate the development and approval of effective and targeting cancer therapies, such as the PI3K/AKT/mTOR pathway in bladder cancer . Apoptosin silicoDrug  Prescription on cellular pathways in Cancers (CDP). Our CDP connected drugs  to pan-cancers on a pathway level to discover novel effective therapies. First, CDP established a pathway dysregulation profile and identified pathway modes of action. Second, CDP determined the magnitude of pathways across 20 cancers based on a generality score, and the tumor specificity of pathways based on prognostic evidence. Third, we linked pathways with 1,309 drugs and connected cancers to all potential beneficial agents. We proposed two applications of CDP in drug design. First is the reposition of well-known drugs for new indications, which can not only save time and cost in the process of research and development but also avoid the side effects. Second is the combination of traditional Chinese medicines  to prescribe new potential formula targeting cellular pathways.Here, we developed a strategy for To portray the molecular pathway-level characterization of pan-cancer, we collected and integrated case–control studies of gene expression profiles and clinical outcome data from the Cancer Genome Atlas (TCGA) including 20 cancers. The paired pan-cancer cohorts contained 1,626 samples covering more than 10 distinct malignant histologies  for 178 pathways, representing the dysregulation (enrichment or depletion) score of each pathway in each tumor type. The The molecular characterization of pan-cancer at the pathway level revealed heterogeneity among different tumor tissues and consistency between tumor and supportive tissues. Hierarchical averaged-linkage clustering of 20 tumors showed 5 subcategories, with proximal to relevant tissues and organs or with similar physical functions: 1) READ, COAD, and COADREAD; 2) breast invasive carcinoma (BRCA), UCEC, LUAD, and LUSC; 3) cholangiocarcinoma (CHOL) and liver hepatocellular carcinoma (LIHC); 4) KIRP, KIPAN, and KIRC; and 5) esophageal carcinoma (ESCA) and stomach adenocarcinoma (STAD) . For mosTo investigate the pan-cancer pathway dysregulations across pan-cancers, the 178 KEGG pathways were partitioned into three clusters based on the CNS matrix . Gain of2(fold change) | > 2, FDR <0.01) for the differentially expressed analysis. ,p < 0.05). The pathways whose members did not overlap with the DEG (upregulated or downregulated) are not shown in p < 0.05). The significant enrichments validated the aforementioned MoA hypothesis of GoF and LoF across pan-cancer. Identifying the MoA of cellular pathways was crucial to exploring the opportunities to therapeutically target pathways, because in principle, only pathways with activity  could be directly targeted with molecules able to inhibit their dysfunctions.To validate the oncogenic and tumor-suppressive characteristics of GoF and LoF pathways, we detected the differential expressed genes (DEG) in comparison of tumor and normal samples for each cancer type. Considering the robustness and the number of DEGs, we determined the optimal threshold  whether significantly mutated genes (SMG) were enriched in the GoF pathways. The assumption proposed by Gonzalez implied that mutations in certain genes provided a selective advantage to tumor cells that followed by growing and proliferating faster . Tracingp values are as shown in p values above 0.05 (hypergeometric test), SMG were enriched in 47 pathways and DT were enriched in 15 pathways. GoF pathways contributed to 11 and 7, respectively. Intersecting the upregulated and downregulated significant pathways with DT-enriched pathways showed two and eight pathways, respectively. Intersecting the upregulated and downregulated significant pathways with SMG-enriched pathways showed six and five pathways, respectively  plots corresponded to all 178 KEGG pathways across 20 cancer types. To quantify the activation of each cellular pathway in specific patients, we defined a pathway activation (PA) score by single sample extension of GSEA (ssGSEA). ssGSEA measures the overexpression of a pathway in each patient by comparing the rank of the genes in the pathway with all other genes. The patient cohort of each cancer was stratified into two groups according to a given threshold of the PA score. To obtain the optimal cutoff, we iteratively applied a series of thresholds to a log-rank test, examining the most significant differences in survival outcomes between two groups. The PA score with the lowest log-rank p < 0.001, log-rank test) difference in overall survival. Totally, 319 significant prognostic pathways were defined in 20 cancers. The number of unique prognostic pathways was 152. We detected two types of prognostic marker pathways in terms of clinical outcomes: (1) favorable prognostic pathways, for which a higher PA score of a pathway was correlated with a good/longer survival outcome, and (2) adverse prognostic pathways, for which a higher PA score of a pathway was correlated with a poor survival outcome. The proportion of favorable and adverse prognostic pathways varied in different cancer types.The cellular pathway of a cancer was defined as a prognostic pathway with PA scores higher or lower than the optimal cutoff in an individual patient which yields a significant  as a drug library. In each cancer type, we performed “cMap analysis”  obtained from the connectivity map  to measure the reversal potency of corresponding perturbations for a significant, prognostic pathway in some cancer type using the CNS and PNS matrix. The calculation was shown as follows: PS = absolute|CNS - PNS|. The construction of CNS and PNS and the calculation of PS consist of the strategy for computational drug prescription in cancers (CDP).To extend the CDP strategy to application, we focused on traditional Chinese medicines . The first application of the CDP pipeline is on drug prescription for herbal medicines in traditional Chinese medicine. There were totally 120 herbal medicines in the cMap drug library. We identified prioritized herb–pathway–tumor triplets across pan-cancers and constructed a reversing network  is a system-based approach to quantify the reversed effects of perturbations on tumors and predict the drugs showing high potency to reverse pathway activity in pan cancer.in silico drug prescription on cellular pathways in cancers (CDP) to achieve the goal of drug prescription, such as reposition of the well-known drugs for new indications and discovery of herbal components indicating for tumors. It is noted that the predicted results could only provide an alternative unless an indication was validated in vitro and in vivo.In this study, we firstly constructed a profile of molecular dysregulation at the pathway level. Then we portrayed the mode of action (MoA) influenced by compounds and herbal components on pathways and identified oncogenic, prognostic pathways in which the gene expression level is significantly unregulated in cancers and adverse to good prognosis of patients. The MoA comprehensively represented characteristics of distinct pathways, implying the capacity of becoming drug targets. LoF and GoF are commonly changed in some cancers, and drugs targeting these pathways can be shared among these cancers, while drugs targeting SoFs should be carefully investigated before further researches, since one pathway in the SoF cluster represents different profiles based on gene expressions. By reversing the expression of the upregulated, prognostic pathways, we developed a strategy for Applying CDP to TCM, we constructed the pan-cancer reversing network which consists of herb–pathway–tumor triplets. To give evidence of the application, we searched well-described relationships of herbs–cancers in the published literatures. With the corresponding pathways, the molecular mechanism of the herbs could be further explored.Taking BRCA as example, the significantly prognostic pathway signature consisted of 13 cellular pathways, of which the most upregulated, dysregulated pathway was “Basal_Transcription_ Factors.” According to the PS value calculated by the CDP strategy, we focused on the top 3 herb medicines  that potentially reversed the pathway of “Basal_Transcription_Factors” Table 3.Amaryllidaceae genus. Ying et al. showed the suppression on cell growth, migration, and invasion both in multiple cell lines like MCF-7, T47D, MDA-MB-231, and 4T1 and in xenograft models , which is localized in nuclei, could play roles in regulating transcription factors  in comparison of cases versus controls, cMap applies a computational method based on the Kolmogorov–Smirnov test to associated small molecules with the gene list, which reflected disease-related phenotypes. Our CDP has an advantage over cMap by taking biological pathways, especially prognostic pathways, into consideration, which approaches more to the nature of diseases.in silico drug prescription on cellular pathways in cancers (CDP) by constructing a profile of molecular dysregulation at the pathway level. Then, we portrayed the three modes of action (MoA), which were influenced by compounds and herbal components on pathways including LoF, GoF, and SoF. Application of this system to herbal components derived from TCM or herbs revealed potential indications targeting cellular pathways in pan-cancers, deepening our understanding of the molecular mechanisms of herbs. In addition, we could also repurpose the well-known (Western) drugs into new indications, which not only saves time and cost in the development process but also avoids side effects. The computational pipeline complements to the traditional target-based approaches at the pathway level, connecting cancers with potential effective drugs.In summary, we developed a score system for Paired tissue samples  across 1,626 individual cohorts were obtained from TCGA using the R package of “TCGA2STAT.” The pan-cancer-normalized gene-level RNA-Seq data for the TCGA cohorts were arranged, as shown in http://www.broadinstitute.org/cmap/).The CMap database (build 02): four types of human cancer cell lines  were treated with 1,309 distinct perturbations including US Food and Drug Administration (FDA)-approved drugs and uncharacterized bioactive compounds , for a total of 6,097 treatments. Gene  expressions from the 6,097 “instances”  were recorded in two batches of microarrays: 671 HG-U133A (Affymetrix) chips (on 407 drugs) and 5,426 HT-HG-U133A chips . Raw data were downloaded from the CMap website  in the pathway.CNS. On the basis of 1,000 trials, the empirical p values for the observed enrichment of 178 KEGG biological pathways were obtained. The false discovery rate (FDR) correction was used to deal with the accumulation of type I errors  in multiple comparisons.We employed Gene Set Enrichment Analysis (GSEA) to connect cancers to biological pathways from Kyoto Encyclopedia of Genes and Genomes (KEGG). We took the 186 KEGG pathways as the representatives of the general physiological processes. The pathway list was narrowed down into 178 pathways since those members of 8 pathways lacked gene expression levels in the paired pan-cancer cohorts. The NES  for 178 pathways were presented in a 178 × 20 table Table S1n be the total number of cancer types in a study (20) and m the number of gene sets or pathways (178). For each pathway i in a collection of pathways, both a and b statistics are respectively computed corresponding to enriched pathways and depleted pathways, giving ia and ib. Actually, the generality score could be calculated for any gene sets or pathways. For simplicity and easy understanding, we only referred to pathways.Generality is a measure that quantifies the deregulated commonness of a gene set or a pathway among multiple cancer types. Let i, giving ikr the rank of gene set i in the thk cancer type, where k = 1, 2,…, n. A rank matrix (m rows × n columns) of the position (1… n) of each pathway in certain cancer was constructed. The ranks ikr of n cancer types for m pathways in the current analysis are contained in the All cancer types in an ascending order were sorted by the normalized enrichment score of the gene set a and b statistics were weighed using the proportion of significant enrichments and depletions in n cancer types of pathways i. Significant enrichments of pathway i require that the NES is positive and significance p is less than 0.05 in certain cancer type k. Significant depletions of pathway i require that the NES is negative and significance p is less than 0.05 in certain cancer type k. The number of cancer types for pathway i was counted according to the significant enrichments and deletions, giving 1w and 2w.The i across n cancer types are computed:The following two statistics corresponding to the enrichments and depletions of pathway iS = ia, if |i|a > |ib|, set iS = ib if |ia| < |ib|. The generality score is set to zero where ia and ib have the same value, which means that pathway i is not a generally deregulated pathway across these n cancer types.Set p of the log-rank test. If the activations of a pathway were measured for each patient, his or her survival time and status  will be used to distinguish the prognostic implications.All the patients along with the survival information were included in the Kaplan–Meier (KM) survival analysis. The patients who lacked survival information were excluded from survival analysis. The KM curves show the differences in overall survival for patients stratified by an ssGSEA score  of a cerPathway classification was identified using hierarchical clustering 178 pathways with the normalized enrichment score (NES) across all tumor types. The hierarchical clustering used the Spearman correlation metric and average linkage. The modes of action of pathways were identified using agglomerative hierarchical clustering of pathways with an SD > 1.0 on the log2 scale across all pathways. Consensus was commutated using 500 samplings of the pathways, 80% of the pathways per sample, agglomerative hierarchical clustering, Euclidean distance metric, and average linkage.0 called variance).To test for the differential expression across two groups , we used the R package samr on gene expression profiles  from TCGA RNA-seq datasets. The p values were adjusted for multiple testing based on the false discovery rate (FDR). For comparison of two sample groups, we applied SAM  which consists of a modification of the standard t-test by the inclusion of an extra variable sThe original contributions presented in the study are included in the article/ZZ, YR and YL designed the work. KeZ, QX, RR,YP collected the data, FM, KZ, KenZ, CY, and YZ analyzed the data, YS, FM and ZZ interpreted results and wrote the manuscript, ZZ, YL, and HG reviewed the manuscript. FM participated in the preparation of the figures and tables. All authors contributed to the article and approved the submitted version.This work was supported by grants from the National Key R&D Program of China(No. 2017YFF0106006) and the National Natural Science Foundation of China (No. 82002647).Authors FM, YP, KZ, QX, RR, YZ, YS, YL, HG, and YR were employed by CapitalBio Technology.The remaining 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.


The chemical ingredients of ANW were retrieved from TCMSP, TCMID, and literature. We predicted the potential targets of active ingredients by PubChem, Swiss Target Prediction, and STITCH databases. The targets related to ischemic stroke were retrieved using GeneCards, DisGeNET, DrugBank, TTD, and GEO databases. Subsequently, Venn diagrams were used to identify common targets of active ingredients and ischemic stroke. Protein-protein interaction (PPI) network was structured with STRING platform and Cytoscape 3.8.2. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of key targets were performed in the Metascape database. Finally, molecular docking was conducted by AutoDock Tools and PyMOL software. β-estradiol, berberine, wogonin, and β-sitosterol, and the key targets were also identified, including IL-6, AKT1, MAPK3, PIK3CA, and TNF. The biological process (BP) results indicated that ANW may have therapeutic effects through response oxidative stress, inflammatory response, cellular response to lipid, and response to nutrient levels. Furthermore, the ingredients of ANW were predicted to have therapeutic effects on ischemic stroke via the HIF-1 signaling pathway, FoxO signaling pathway, chemokine signaling pathway, fluid shear stress and atherosclerosis, and neurotrophin signaling pathway. The molecular docking results all showed that the core ingredients were strong binding activity with the core targets.  A total of 2391 targets were identified for 230 active ingredients of ANW, and 1386 of them overlapped with ischemic stroke targets. The key active ingredients were mainly quercetin,  In conclusion, the bioinformatics and pharmacological results reveal that counteracting oxidative stress, suppressing inflammation, inhibiting the development of AS, and even protecting neurological function are critical pathways for ANW in the treatment of ischemic stroke. These results may help to elucidate the mechanism of ANW on ischemic stroke for experimental studies and clinical applications. Ischemic stroke is a common cerebrovascular event due to an abrupt cerebral artery occlusion, resulting in insufficient perfusion, which then causes edema, inflammation, and necrosis of the affected tissue and severely damages to neurological function. The World Health Organization reports that ischemic stroke is the main cause of death and long-term disability in the world, which causes a tremendous psychological and financial burden on patients . HoweverMoschus, Realgar, Curcumae Radix, Borneolum, Scutellariae Radix, Coptidis Rhizoma, Gardeniae Fructus, Bovis Calculus, Bubali Cornu, Margarita, and Cinnabaris. Studies indicated that ANW had effect on reducing infarct size, protecting the integrity of the blood-brain barrier (BBB), improving antioxidant capacity, and inhibiting inflammation injury to produce neuroprotection; furthermore, it may improve the development of early atherosclerosis (AS) by suppressing inflammation [Notably, many of the Chinese herbs have been proven to produce therapeutic effects on ischemic stroke in clinical research . As a faammation –7. HowevIn this study, we aim to elucidate the possible mechanism of ANW on ischemic stroke and reveal the interaction between ANW, target, and ischemic stroke from a holistic perspective through a network pharmacology approach. The workflow diagram of the study is presented in https://tcmspw.com/tcmsp.php) [http://www.megabionet.org/tcmia/), and literature. The active compounds were screened for oral bioavailability (OB), drug-likeness (DL), and blood-brain barrier permeability (BBB) prediction. The selection of OB, DL, and BBB referred to the recommendations of the TCMSP database. Therefore, we finally screened the compounds with OB ≥ 0.2, DL ≥ 0.1, and BBB ≥ -0.3, which were considered as parameters for selecting potentially pharmacological ingredients [The effective ingredients of ANW were searched through TCMSP (msp.php) , TCMID  and TCMID and inputted them into the PubChem, Swiss Target Prediction (http://www.swisstargetprediction.ch/) [http://stitch.embl.de/) database to obtain potential targets of bioactive ingredients. The target was further standardized in UniProtKB database (http://www.uniprot.org) [We retrieved SMILES number or 3D structure of each ingredient from the PubChem database (ion.ch/) , and STIrot.org) .https://www.genecards.org/), DisGeNET (http://disgenet.org/), DrugBank (https://go.drugbank.com/), GEO (https://www.ncbi.nlm.nih.gov/geo/), and TTD (http://db.idrblab.net/ttd/) [http://bioinfogp.cnb.csic.Es/tools/venny/index.html) was used to identify common potential targets between ischemic stroke and the active ingredients of ANW.The disease targets correlated with “cerebral ischemic stroke” and “cerebral infarction” were identified through GeneCards (et/ttd/) . After dhttps://string-db.org/) [Protein-protein interaction (PPI) network was constructed through the STRING database (db.org/)  with a cdb.org/) . In addihttps://metascape.org) [P < 0.01. In addition, the bioinformatics platform (http://www.bioinformatics.com.cn/) was used to visualize GO and KEGG enrichment analysis with the bubble charts.The Gene ontology (GO) including biological process (BP), molecular function (MF), and cellular component (CC), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the Metascape database (ape.org) . The staAn ingredients-targets-network was constructed by Cytoscape software. The key active ingredients of ANW were sorted and screened according to the value of degree, betweenness centrality, and closeness centrality based on topological analysis.http://www.rcsb.org/). The AutoDockTool 1.5.6 was used to add hydrogen and optimize protein structure for molecular docking after removing water and original ligands.The 3D structures of candidate ingredients were obtained from PubChem, which were transformed by Open Babel Toolkit (version 2.4.1) into a mol2 file format. The 3D structures of the core target were downloaded from the PDB database (Borneolum (16 ingredients), Bovis Calculus (19 ingredients), Coptidis Rhizoma (17 ingredients), Moschus (32 ingredients), Bubali Cornu (22 ingredients), Realgar (3 ingredients), Curcumae Radix (44 ingredients), Margarita (16 ingredients), Gardeniae Fructus (22 ingredients), Cinnabaris (2 ingredients), and Scutellariae Radix (37 ingredients). Detailed active ingredients of ANW are shown in A total of 230 active ingredients were obtained through the database after eliminating duplicates. These active ingredients were mainly derived from https://www.malacards.org/) [A total of 4963 potential targets were obtained of ischemic stroke, and 1386 common targets were obtained after intersecting with 2391 potential targets of the active ingredients . The topds.org/) , so theyAs shown in GO enrichment results include 296 BP terms, 99 MF terms, and 92 CC terms. The key items of BP mainly included response to oxidative stress, inflammatory response, cellular response to lipid, and response to nutrient levels. The main results of MF included oxidoreductase activity, cytokine receptor binding, lipid binding, and neurotransmitter receptor activity, and CC mainly included neuronal cell body, dendritic tree, axon, and postsynapse. We individually selected top 20 remarkably enriched terms in BP, MF, and CC classification as presented in KEGG pathway enrichment analysis may elaborate the mechanism of ANW on ischemic stroke. 139 signal pathways were obtained based on the 130 core targets. After removing pathways associated with cancer and unrelated to disease, the main results of KEGG pathways included the HIF-1 signaling pathway, FoxO signaling pathway, chemokine signaling pathway, fluid shear stress and atherosclerosis, and neurotrophin signaling pathway. 20 significantly enriched pathways were selected as shown in β-estradiol, berberine, wogonin, and β-sitosterol. The results suggested that the 5 key ingredients all had a strong affinity with IL-6, AKT1, MAPK3, PIK3CA, and TNF, and the results of the docking were visualized by PyMOL software (We selected the core targets, including IL-6, AKT1, MAPK3, PIK3CA, and TNF for molecular docking with the quercetin, software .Stroke is classified as ischemic or hemorrhagic. Cerebral hemorrhage and cerebral ischemia have the possibility to cause serious inflammatory response, cerebral edema, and neurological deficits , 20. Theβ-estradiol, berberine, and β-sitosterol showed strong binding activity to the IL-6, AKT1, MAPK3, PIK3CA of the core targets. IL-6 is a pleiotropic cytokine that plays a crucial role in host defense [β-estradiol from Moschus reversed neuronal damage by regulating the JAK-STAT3 pathway and protected neurons from acidosis-mediated neurotoxicity and ischemic cerebral injury, thus promoting remodel and repair after brain injury [β-sitosterol inhibited the secretion of inflammatory factors such as TNF-α, IL-1β, IL-6 to suppress the inflammatory response. TNF is a versatile pro-inflammatory cytokine involved in all stages of ischemic stroke. The study confirmed that quercetin from Coptidis Rhizoma and Gardeniae Fructus attenuated TNF-induced inflammation by suppressing the NF-κB pathway [The results of pharmaceutical ingredient analyses and molecular docking showed that the main ingredients quercetin,  defense . However defense . The stun injury , 28. Lian injury  demonstr pathway . MAPK is pathway , 32.AKT1, as a threonine protein kinases, is an important regulator of the AKT-mTOR signaling pathway that controls the tempo of newborn neurons during adult neurogenesis. PIK3CA is involved in the cell signaling of various growth factors. Yan et al.  demonstrAccording to the results of KEGG enrichment analysis, ANW is considered to affect important pathways that are closely related to the pathogenesis of ischemic stroke, including HIF-1 signaling pathway, FoxO signaling pathway, chemokine signaling pathway, fluid shear stress and atherosclerosis, and neurotrophin signaling pathway. The results of GO enrichment were also closely related to response to oxidative stress, inflammatory response, cellular response to lipid, and response to nutrient levels. Furthermore, the BP analysis revealed that the subnetworks were mainly associated with inflammatory response, response to lipid, neuroapoptosis, and development.α is a primary modulator of cellular and systemic homeostatic reactions to hypoxia. Evidence showed that HIF-1 facilitated the transcription of various prosurvival proteins engaged in energy metabolism, angiogenesis, and neurogenesis, exerting a neuroprotective effect against ischemic stroke in ischemic conditions [α and VEGF protein expression [HIF-1nditions . Researcpression . The Foxpression . Zhang epression  found thpression .Atherosclerosis is a major cause of stroke onset or recurrence, and blood flow-induced shear stress has become an essential characteristic of atherosclerosis. The fluid resistance exerted on the vessel wall is mechanically translated into biochemical signals that lead to alter vascular behavior. Therefore, the maintenance of physiological laminar shear stress is essential for normal vascular function . QuercetThis research based on a pharmacological network explored the potential mechanisms of ANW for the treatment of ischemic stroke. The findings highlighted the improvement of the inflammatory response, immune defense, and neuroprotection of ANW against ischemic stroke. Our results were consistent with published studies that upregulation of HIF-1 signaling pathway, FoxO signaling pathway, and neurotrophin signaling pathway and downregulation of chemokine signaling pathway had positive effects on cerebral ischemia , 44–47. In conclusion, the bioinformatics and pharmacological results reveal that counteracting oxidative stress, suppressing inflammation, inhibiting the development of AS, and even protecting neurological function are critical pathways for ANW in the treatment of ischemic stroke. These results may help to elucidate the mechanism of ANW on ischemic stroke for experimental studies and clinical applications.


Huangqi Gegen decoction (HGD), a Chinese herb formula, has been widely used to treat diabetic nephropathy in China, while the pharmacological mechanisms are still unclear. Therefore, the present study aims to explore the underlying mechanism of HGD for treating diabetic nephropathy (DN).  Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), UniProt, and SwissTargetPrediction databases were used to search the active ingredients and potential targets of HGD. In addition, multiple disease-related databases were used to collect DN-related targets. Common targets of the protein-protein interaction (PPI) network were established using the STRING database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the DAVID database. At last, AutoDockVina was used to conduct molecular docking verification for the core components and targets.  A total of 27 active ingredients and 354 putative identified target genes were screened from HGD, of which 99 overlapped with the targets of DN and were considered potential therapeutic targets. Further analysis showed that the HGD activity of quercetin, formononetin, kaempferol, isorhamnetin, and beta-sitosterol ingredients is possible through VEGFA, IL6, TNF, AKT1, and TP53 targets involved in TNF, toll-like receptors, and MAPK-related pathways, which have anti-inflammatory, antiapoptosis, antioxidation, and autophagy effects, relieve renal fibrosis and renal cortex injury, and improve renal function, thus delaying the development of DN. The molecular docking results showed that quercetin, formononetin, kaempferol, isorhamnetin, beta-sitosterol had a good binding activity with VEGFA, IL6, TNF, AKT1, and TP53.  This study demonstrated that HGD might take part in the treatment of DN through multicomponent, multitarget, and multichannel combined action. Diabetic nephropathy (DN) is one of the most important microvascular complications of diabetes, accounting for about 20%–40% of all diabetic patients . It has Astragalus membranaceus (Huangqi) and Pueraria (Gegen). It has been found that Astragalus membranaceus has a therapeutic effect on DN characterized by renal insufficiency and pathological changes by reducing blood glucose and antioxidation [Pueraria extract exerts an antidiabetic effect by activating a variety of mechanisms, such as reducing insulin resistance, increasing insulin release, inhibiting glucose absorption and reabsorption, and improving insulin sensitivity, glucose uptake, and metabolism [Huangqi Gegen decoction (HGD), derived from ancient books, has the effects of invigorating the spleen and replenishing Qi, nourishing Yin and clearing heat, invigorating body fluid, and relieving thirst. It can be used in hypertension and diabetes due to deficiency of both Qi and Yin. The whole prescription is composed of xidation . Pueraritabolism . HGD cantabolism . HoweverNetwork pharmacology integrates multidisciplinary technology and content to construct a drug-target-disease network to explore the relationship between diseases and drugs . At the Astragalus membranaceus and Pueraria were obtained by searching “Huangqi” and “Gegen” in the TCMSP database. According to absorption, distribution, metabolism, and excretion (ADME) parameters, the active components of Astragalus membranaceus and Pueraria were obtained under the conditions of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18, combined with related literature reports. The potential target information of active components was retrieved one by one through the UniProt database to obtain the corresponding gene names of drug targets. For other active ingredients that have not been retrieved, first, the 2D structure of their chemical components was downloaded from the PubChem website and saved in SDF format and then submitted to the SwissTargetPrediction database one by one to obtain the corresponding target of compounds.The active components of The active components and drug targets of HGD obtained in 2.2 were used to construct the active ingredients-target network through Cytoscape software.Here, “diabetic nephropathy” was used as the keyword to search DN-related targets in three databases: GeneCards, OMIM, and CTD. In the integration of disease targets, the larger the score value of the target is, the closer the relationship between the target and the disease is. Therefore, the target is screened with score ≥10 as the standard in the GeneCards database, and the target database of DN is established by combining the targets in the three databases.Homosapiens”, select the medium confidence data with score condition greater than 0.40, hide the isolated proteins in the map, and finally derive the visual PPI network map. At the same time, the network is imported into Cytoscape to further analyze the core targets.Using the online tool of Venny, the potential targets of active components and the disease targets of DN were introduced, and the predictive targets of active components of HGD acting on DN were screened, and Venn maps were drawn. Upload the prediction target to the STRING database, limit the species to “Homo sapiens.” Finally, GO functional annotation and KEGG pathway enrichment analyses were performed using the Weshengxin online platform, and a P value less than 0.05 was employed for further analysis.DAVID database was used to analyze the enrichment of GO and KEGG pathways, and the gene type was “official gene symbol,” and the species type was “The main active components, DN key targets, core targets, and signal pathways of HGD were introduced into Cytoscape to construct the network of main active components-DN key targets-core targets-signal pathways.The core components of HGD were docked with the top five targets of the degree in 1.5. Firstly, the 3D structure of the target protein was downloaded from the PDB database, and the unique ligands similar to small molecules were found as protoligands. The target protein is then removed from water molecules and original ligands by PyMOL and introduced into AutoDockTools for hydrogenation; its charge is calculated; then, it is combined with nonpolar hydrogen and and saved into “pdbqt” format. Finally, AutoDock Vina was run for molecular docking, and PyMOL was used to visualize the results.Astragalus membranaceus and 18 active components of Pueraria were found by the TCMSP database. After screening by ADME parameters, 20 active components of Astragalus membranaceus and four active components of Pueraria were obtained. By consulting the literature, it was found that Astragalus polysaccharides can inhibit the TGF-β/Smad pathway and protect the kidney in DN model rats [β1/Smad/miR-192 pathway in rat glomerular mesangial cells and DN model rats, thus reducing renal injury [Twenty-seven active components of del rats . AccordiThree hundred fifty-four targets and 27 active ingredients were imported into the Cytoscape software to construct the active ingredient-target network, and a network diagram composed of 351 nodes and 717 edges was obtained. The node size was proportional to its degree value, as shown in The DN target is obtained using three databases: GeneCards, OMIM, CTD. Among them, 3320 targets were obtained by GeneCards, and 655 targets were screened by GeneCards under the condition of score ≥10. Two hundred fifty-one targets were obtained by OMIM, and 114 targets were obtained by CTD. A total of 845 DN disease targets were obtained by combining and deduplication of the above targets.The potential targets of active components of HGD were intersected with DN disease targets by Venny2.1.0 online tool, and finally, 99 main targets were obtained, and the Venn diagram was drawn, as shown in P < 0.01, a total of 125 biological processes, 10 molecular functions, 13 cell components, and 81 signal pathways were obtained.The above 27 core targets were uploaded to the DAVID database for enrichment analysis of GO and KEGG pathways. Setting P value, the top 10 functional information sets are selected for analysis and mapping (According to the size of the  mapping . The maiP value and the literature, the enrichment results of 10 KEGG pathways were selected. The specific information is shown in According to the size of the The network of the active components-DN-core targets-signal pathways was constructed by Cytoscape software, as shown in π-π conjugation with TYR-59 amino acid residues in TNF, quercetin formed hydrogen bonds with TYR-151, TYR-119, and TYR-119 amino acid residues in TNF, formononetin formed hydrogen bonds with TYR-119 and LEU-120 amino acid residues in TNF, and formononetin formed hydrogen bonds with TYR-229 amino acid residues in AKT1. Quercetin forms a hydrogen bond with three amino acid residues of LYS-276, LEU-295, and GLY-294 in AKT1, and kaempferol forms a hydrogen bond with three amino acid residues of LYS-179, LEU-295, and GLY-294 in AKT1. These interactions make the structure tend to be stable.The core components in HGD were docking with the top five targets with a degree value of 1.5. The results showed that the binding energies were all less than −5 kcal/mol, indicating that they had good binding activity . The speIn this study, we screened out 27 active components of HGD and 99 targets related to DN by the network pharmacology method. It shows that HGD can play a role in treating DN through multicomponent and multitarget combination, which has research significance for DN treatment. According to the topological analysis results of the component-disease-core target-signal pathway network, the top five core components were quercetin, formononetin, kaempferol, isorhamnetin, beta-sitosterol.β1, and mitochondrial/cytochrome c-mediated apoptosis pathway and improve the recovery of mitochondrial membrane potential [κB signaling pathway in diabetic rats [Quercetin is a kind of flavonoid widely existing in various plants. It has anti-inflammatory and antioxidation activities, reduces blood glucose, blood pressure, and lipid, and has other pharmacological effects , 23. It otential . Isorhamotential . A recentic rats . Beta-Sitic rats . To sum In order to predict the mechanism of HGD in the treatment of DN, we carried out the enrichment analysis of the GO and KEGG pathways. The results showed that HGD could regulate the cellular response to hypoxia, positive regulation of transcription, angiogenesis, positive regulation of nitric oxide biosynthetic process, positive regulation of gene expression, and other biological processes, including extracellular region, cytosol, extracellular space, caveola, and nucleoplasm. At the same time, the activity is produced through molecular functions such as enzyme binding, identical protein binding, transcription factor binding, protein binding, and core promoter sequence-specific DNA binding. TNF signal pathway, toll-like receptor signal pathway, MAPK signal pathway, and other signal pathways are important pathways of HGD on DN, which are mainly involved in inflammation, immunity, autophagy, and oxidative stress.α can cause inflammation of renal tissue, collagen deposition, and glomerular sclerosis, leading to kidney injury [α, and MAPK, aggravate the inflammatory response, and lead to renal injury. It has been proved that toll-like receptor .0 induces the activation of NF-κB, leading to renal cortex inflammation and renal fibrosis [The activation of the TNF signaling pathway can lead to glomerular vasoconstriction, a decrease of renal blood flow and glomerular filtration rate, and participation in the development of DN . Moreovey injury . Toll-lifibrosis . MAPK sifibrosis , 36. In HIF-1 signaling pathway mediates hypoxia-induced cell response by regulating genes involved in cell metabolism, glucose utilization, angiogenesis, oxidative stress, apoptosis, and proliferation, inhibits the expression of HIF-1 in a high glucose environment, affects podocyte survival, and leads to glomerular injury . VEGF siIn conclusion, the results of this study show that HGD can initially explore the potential mechanism of HGD in the treatment of DN through multitarget and multipathway combined action of DN. Through network pharmacology analysis, our study revealed that HGD might take part in the treatment of DN through pathways associated with insulin resistance, PI3K-Akt, toll-like receptors, MAPK, and TNF. Then, we validated the core targets and key components by molecular docking technology. The results showed that quercetin, formononetin, kaempferol, isorhamnetin, and beta-sitosterol had good binding activity with VEGFA, IL6, TNF, AKT1, and TP53. VEGFA, IL6, TNF, AKT1, and TP53 may be potential targets of HGD in treating DN. These results suggest that the antidiabetic nephropathy effect of HGD may be related to its direct or indirect regulation of the above potential targets and pathways, which provides a theoretical basis and research ideas for the clinical application of HGD in the treatment of DN. After all, so far, due to the complexity of traditional Chinese medicine compounds, although HGD has been used in the prevention and treatment of DN, its pharmacodynamic material basis and mechanism of action are still unclear. This study reveals the pharmacodynamic material basis and potential mechanism of HGD in the treatment of DN. Of course, some limitations exist in this study due to experimental conditions, time, and other constraints; we are unable to perform in vivo or in vitro experiments to validate the results in a short time. We will definitely conduct in-depth experimental research on the key targets and pathways of this article when conditions permit at a later period.


Glioma is the most common primary intracranial tumor in adult patients. Among them, glioblastoma is a highly malignant one with a poor prognosis. Flavonoids are a class of phenolic compounds widely distributed in plants and have many biological functions, such as anti-inflammatory, antioxidant, antiaging, and anticancer. Nowadays, flavonoids have been applied to the therapy of glioma; however, the molecular mechanism underlying the therapeutic effects has not been fully elaborated. This study was carried out to explore the mechanism of selected active flavonoid compounds in treating glioma using network pharmacology and molecular docking approaches.  Active ingredients and associated targets of flavonoids were acquired by using the Traditional Chinese Medicine Database and Analysis Platform (TCMSP) and Swiss TargetPrediction platform. Genes related to glioma were obtained from the GeneCards and DisGeNET databases. The intersection targets between flavonoid targets and glioma-related genes were used to construct protein-protein interaction (PPI) network via the STRING database, and the results were analyzed by Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed and displayed by utilizing the Metascape portal and clusterProfiler R package. Molecular docking was carried out by iGEMDOCK and SwissDock, and the results were visually displayed by UCSF Chimera software.  Eighty-four active flavonoid compounds and 258 targets overlapped between flavonoid targets and glioma-related genes were achieved. PPI network revealed potential therapeutic targets, such as AKT1, EGFR, VEGFA, MAPK3, and CASP3, based on their node degree. GO and KEGG analyses showed that core targets were mainly enriched in the PI3K-Akt signaling pathway. Molecular docking simulation indicated that potential glioma-related targets-MAPK1 and HSP90AA1 were bounded more firmly with epigallocatechin-3-gallate (EGCG) than with quercetin.  The findings of this study indicated that selected active flavonoid compounds might play therapeutic roles in glioma mainly through the PI3K-Akt signaling pathway. Moreover, EGCG had the potential antiglioma activity by targeting MAPK1 and HSP90AA1. Glioma, originated from the neuroepithelium, accounts for 40%∼50% of brain tumors and is one of the most common primary intracranial tumors; among them, glioblastoma is a highly malignant one with poor clinical outcome . ExtensiIn this study, we tried to systematically identify the molecular mechanisms of flavonoids' antiglioma effects based on findings from network pharmacology and molecular docking. The flowchart of this study is shown in https://tcmsp-e.com/) is a unique systematic pharmacology platform for Chinese herbal medicines and is characterized by exploring relationships between compounds, targets, and diseases. TCMSP has been utilized to screen active flavonoid ingredients [st January 2016 to 30th May 2021. The active flavonoids were confirmed by PubChem (https://pubchem.ncbi.nlm.nih.gov/). The structures of compounds saved in SDF format were used to predict potential targets of active molecules by using Swiss TargetPrediction platform (http://www.swisstargetprediction.ch/), and the species were set as “Homo sapiens” and the probability was set larger than 0. All the targets from UniProt database (https://www.uniprot.org/) were in standardized format.TCMSP  and DisGeNET (https://www.disgenet.org/home/) databases with the keyword “glioma.” Flavonoid targets and glioma-related genes were imported into Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/) to acquire common targets as the potential targets for further analyses.Glioma-related targets were retrieved by using the GeneCards (https://string-db.org/), and “Homo sapiens” and Medium Confidence (0.4) were set. All the information derived from the STRING database was then imported into Cytoscape software (version 3.8.2) for visual display.A protein-protein interaction (PPI) network was constructed using the STRING platform  is an integrated database of genomic, chemical, and system functional information and is extensively used to capture significantly enriched biological pathways [http://metascape.org/gp/index.html/) for enrichment analysis. The top 20 GO and KEGG pathway enrichment analysis results were visualized by clusterProfiler R package as the histogram or bubble graph with p < 0.01 [GO is wildly used to study gene functions, including the biological process (BP), molecular function (MF), and cell component (CC) . KEGG (hpathways . The comp < 0.01 .https://www.rcsb.org/) with high resolution and score, water was removed, while hydrogens were added by MGLTools software (version 1.5.6). Verified compounds in.mol2 format were acquired from the TCM@Taiwan database (https://tcm.cmu.edu.tw/). Molecular docking was carried out using iGEMDOCK software (version 2.1) with default parameters. We selected the most potential proteins which had the lowest energy and determined their docking ligands using the SwissDock platform (http://www.swissdock.ch/docking/). The results were visually displayed by UCSF Chimera software (version 1.15).The most potential pathway and its related genes of flavonoids treating glioma were obtained. Then, these targets with the most promising ingredients were reconfirmed by using molecular docking. Crystal structures of related proteins were obtained from the RCSB Protein Data Bank  for further analysis. The network is shown in The potential targets were AKT1, EGFR, VEGFA, MAPK3, CASP3, SRC, HRAS, TNF, MAPK1, CCND1, ESR1, HSP90AA1, and MTOR as their degrees were above 100 . Node deGO terms were enriched by the Metascape platform. The results showed that BP terms enriched in glioma-flavonoids overlapping targets mainly included peptidyl-tyrosine phosphorylation and modification, response to oxidative stress and oxygen levels. The top five enriched CC terms were membrane raft, membrane microdomain, membrane region, neuronal cell body, and transferase complex, transferring phosphorus-containing groups. MF terms displayed the intersection genes that were mainly enriched in protein tyrosine kinase activity, protein serine/threonine kinase activity, transmembrane receptor protein kinase activity, transmembrane receptor protein tyrosine kinase activity, and phosphatase binding .KEGG pathway enrichment analysis of the 258 intersection gene targets was carried out by Metascape. The main pathways among these genes included PI3K-Akt, Ras, HIF-1, and Neurotrophin signaling pathways .The result of the KEGG pathway enrichment analysis indicated that the PI3K-Akt signaling pathway was the main pathway through which flavonoids affected the glioma. Among the potential targets, AKT1, EGFR, MAPK1, MAPK3, CCND1, MTOR, VEGFA, HRAS, and HSP90AA1 were enriched in the PI3K-Akt signaling pathway. We selected seven potential active molecules, including quercetin, epigallocatechin-3-gallate, isoliquiritigenin, genistein, apigenin, kaempferol, and luteolin, to dock with nine target proteins, and chose temozolomide as the control. Lower binding energy indicates a stabler conformation. We used the quantitative value of fitness to evaluate the binding level. Fitness is the total energy of a predicted pose in the binding site. The empirical scoring function of iGEMDOCK is estimated as follows: Fitness = vdW + Hbond + Elec. The vdW term is van der Waal energy; Hbond and Elect terms are hydrogen bonding energy and electrostatic energy, respectively . The resIn recent years, flavonoids are widely used for antiglioma treatment. The mechanisms of flavonoids are very complex because they have multiple potential targets and active components. Network pharmacology together with bioinformatics has superiority in the systematic elucidation of the mechanism of TCM at the molecular level and representation of interactions between active compounds, potential targets, and various pathways.μmol/L) could suppress cancer cells by inducing apoptosis and by inhibiting autophagic processes [In our study, potential targets of active components analysis revealed that quercetin, EGCG, isoliquiritigenin, genistein, apigenin, kaempferol, and luteolin interacted with multiple targets in the network. These findings showed that they may play important roles in the treatment of glioma. As reported, these seven active flavonoid compounds have distinct ways of treating glioma. Quercetin, a flavonol, had the most potential targets in this study. It plays antiglioma effects by inducing cell apoptosis , inhibitrocesses  and regurocesses . MTOR isrocesses . Furtherrocesses . Isoliqurocesses , 23. Genrocesses . Genisterocesses . Apigenirocesses , 29. It rocesses . Kaempferocesses , 79. Lutrocesses , 80. It rocesses .in vivo at the protein and mRNA levels [Immune factors have been considered as a significant factor contributing to the development and progression of glioma . In the A levels .in vivo [To further indicate the potential mechanism of flavonoids in treating glioma, KEGG analysis discovered that PI3K-Akt was the main signaling pathway. It is a classic signal transduction pathway involved in cell proliferation, apoptosis, migration, invasion, and angiogenesis in glioma and plays an important role in the occurrence and development of glioma . The resin vivo . Kim et in vivo . Heat Shin vivo . To evalin vivo . Thus, tin vitro and in vivo experiments are needed for verification. In short, our study portrayed the ground view of flavonoids in the treatment of glioma.Although there is an abundance of information and the analysis process is complex, some useful and credible conclusions have been drawn. Due to limitations of compounds screening and accuracy of target prediction, the results obtained in this study are general, and This study elaborated the mechanisms of active flavonoids on antiglioma using network pharmacology and molecular docking by constructing a compound‒target‒pathway network. Active components have particular advantages in curing glioma by targeting MAPK1, MAPK3, EGFR, MTOR, AKT1, VEGFA, CCND1, HSP90AA1, and HRAS. In addition, EGCG can target HSP90AA1 and MAPK1 via the PI3K-Akt signaling pathway. These findings offered a research foundation for further investigation of flavonoids on antiglioma.


Bison bonasus hybrid revealed various spatial arrangement and number of gustatory and mechanical papillae between parental species and their hybrid. Furthermore, scanning electron microscopy analysis of gustatory papillae indicated the variable distribution of fungiform papillae (Fu) over the surface of the tongue, which could be significant in differentiated taste perception during feeding in studied wild living and domesticated husbandry ruminants. To specify the detailed microstructure of Fu papillae with connective tissue cores (CTC) and intraepithelial taste buds system, the first time the three-dimensional computer-aided analysis of serial histoslides resulted in the rendering of 3D reconstructions of Fu papillae.Our recent macro- and scanning electron microscopic study of tongue conducted on domesticated cattle, wild living European bison, and The comparative analysis of 3D models Fu papillae conducted in six areas of lingual mucosa of each tongue revealed information about, microstructural diversity of Fu papillae in studied ruminants. The estimation of number and density of Fu papillae on tongues, rate of protrusion of papillae over mucosa, and a number of taste buds per papilla allowed to state the ventral surface of the lingual apex and posterolateral surfaces of the lingual torus as regions important in taste perception, as in the preselection of taken food, as well in the analysis of food during rumination, respectively. On the 3D models were observed three structural types of CTC of different distribution on the tongue in studied species. The quantitative data of the number of taste buds on Fu papillae have regional functional differences in the taste system important in feeding and veterinary practice. Moreover, our analysis determined specific features in examined hybrid and showed similarities of some studied features with cattle, i.e., maternal species.The 3D reconstruction method used for the first time in the field of study of the lingual papillae and taste buds system can be considered as an innovative and effective tool in assessing of the microstructures of Fu papillae, and it could be suitable for further studies of taste system structures in normal and pathological condition.The online version contains supplementary material available at 10.1186/s12917-021-03111-5. Among lingual papillae observed on mammalian tongues, fungiform papillae (Fu papillae), vallate papillae (Vp papillae), and foliate papillae (Fo papillae) belong to gustatory papillae . The occPrevious macro- and microscopic studies of lingual papillae revealed that in mammalian species Fu papillae are distributed on the dorsal and ventral surface of the apex and along the body of the tongue . MoreoveThe studies on the microstructure of Fu papillae in mammals mostly based on observations in a scanning electron microscope (SEM) and histological sections in light microscopy (LM). In them Fu papillae were described as dome-shaped structures with the convex or flat dorsal surface, which raise above the mucosa or are deeply embedded in an interpapillary stratified squamous epithelium, so on SEM electronograms, these structures are encircled by epithelial furrow , 31, 33.The main function of Fu papillae is taste perception by receptor cells of taste buds, which function is not only connected with the process of the choice of the food in terms of nutrients, but also with animal protection against toxins –40. So fBison bonasus hybrid were observed on both the dorsal and ventral surface of the tongue. Evenly dispersed Fu papillae on the whole dorsal surface of the apex and lingual body in Bison bonasus hybrid differ from the dense arrangement of these papillae on lateral areas of the lingual body in cattle and European bison. Significant diversities were also noticed on the ventral surface of the apex. In Bison bonasus hybrid observed V-pattern distribution of Fu papillae, whereas these papillae in cattle and European bison were arranged marginally. In turn, a similar dispersed arrangement of Fu papillae in all examined ruminants was observed on the dorsal surface of the medial and posterior part of lingual prominence and its lateral surfaces [Our previous microscopic studies of the tongue and mechanical and gustatory papillae showed the differences between closely related species of ruminants . The difsurfaces .The aim of the present study was a detailed analysis of 3D models presented microstructure of Fu papillae in different areas of the tongue in three mentioned earlier species of ruminants with the histomorphometric data of the papillae. We also want proof if three-dimensional analysis will be an efficient tool in identifying many microstructures of lingual papillae as spatial relation between papillae and surrounded epithelium, the shape of internal skeleton, detailed arrangement, and a number of taste buds in Fu papillae, which will be helpful in normal, pathological, or clinical assessments.Cross-sections of Fu papillae and 3D models with distribution of taste buds taken from particular parts of studied ruminants tongues are presented in Fig. 2 were spread regularly between filiform papillae on the dorsal surface of the apex .The keratinized stratified squamous epithelium protects Fu papillae in ruminants against mechanical damage during chewing and rumination of food. According to our analyses, the average height of stratified squamous epithelium on Fu papillae was the highest in European bison, which may relate to the access to structurally diverse food available in a wild environment, changing with seasons. It is opposite to livestock, which feeds on structurally similar forage. The highest papillary epithelium coverFu papillae on the medial part of the torus in European bison and its hybrid, and the lowest values were noted on the ventral surface of the apex. The average thickness of a keratinized layer of epithelium on Fu papillae in the studied species is 21 μm, which is ca. 16% of the total height of the epithelium. In contrast, on the ventral surface of the tongue, these values decrease to 9-14 μm and 9.5-10.5% what is important in exposes taste pores on the Fu papillae surface during the food selection.The connective tissue core (CTC) of Fu papillae are the internal skeleton for capillary networks and nerve bundles, strongly attached with mucosal epithelium. So far, the shapes of CTCs as collagenous skeleton were studied in scanning electron microscopy after NaOH maceration of epithelia and free cells of connective tissues in rabbit, koala, hippopotamus, goat, cattle, Reeves’ muntjac deer, Japanese badgers, pig, or primates , 28, 51.Bison bonasus hybrid.Correlative morphometric analysis and 3D modeling of Fu papillae allowed to distinguished rounded in outline Fu papillae with three types of CTCs. The balloon-shaped CTCs were characterized by wide base narrowing in the medial part and often expanding near or over the surface of the interpapillary epithelium. Columnar-like type of CTC have quite similar dimensions on its dorsal and medial part, but some wider on the basal part, whereas in cone-like CTC, the width becomes smaller from base to the top of papillae. There were all three CTC types in European bison, whereas in cattle, only balloon-like and cone-like CTCs. The columnar and balloon-like CTCs were typical for Analyzing the spatial distribution of Fu papillae and their CTC we revealed that on both surfaces of the lingual apex in each examined ruminant are balloon-like or columnar-like CTCs. On the torus of the tongue in cattle and European bison, there were often the cone-like CTCs, which correspond with smaller protrusion of Fu papillae over the mucosa surface. However, in European bison hybrid on all surfaces of lingual torus were only balloon-like types of CTCs. On the surface of CTC models in all studied species, separate elongated connective tissue processes, get deep into the thick interpapillary epithelium. Such superficial microstructures in shape/form of vertical plices, folds, and striations providing the well attachment of the epithelia were studied previously in scanning electron microscopy , 28, 51.During the microstructural studies of Fu papillae in mammalian species, in determining the number and distribution of taste buds in these papillae, three methods so far were used. The analyses of taste buds were primarily based on 2D observations of the cross-sections of Fu papillae or counting of taste pores visible on scanning electron microscopic electronograms. Such studies were conducted in Egyptian fruit bats, wild boar, pig, hippopotamus, goat, yak, wolf, and Japanese badgers , 34, 49.Bison bonasus hybrid, were found no taste buds in Fu papillae.In current studies, we applied for the first time the three-dimensional analysis of taste buds allowed on a spatial description of distribution and counting of taste buds as a more efficient method used to describe the taste buds system in Fu papillae. Observations of serial histoslides resulted in determining the diameter of taste buds, ranging from 33 μm to 48 μm, which may specify the size of taste buds for ruminants independent of the thickness of the stratified squamous epithelium. Studies on 3D models of Fu papillae with marked taste buds revealed that among three examined ruminants number of taste buds differs from 1 up to 64. On the dorsal surface of the apex in the Bison bonasus hybrid and equals 6659 and 6620, respectively, while in European bison was estimated ca. 60% fewer taste buds, i.e., 2608. Previous publication about the number of taste buds in bovine was estimated from 1580 to 1838 taste buds in Fu papillae, which seems underestimated compared to our results [Previously, the number of taste buds counted from histoslides in mammalian species was often described as “numerous, several or few” taste buds , 33, 41. results . In othe results , 53.Bison bonasus hybrid tongue was a significant decrease in the number of taste buds of 1-3 in a single papilla. The calculation showed that in cattle and European bison, as parental species, were around 94%, and almost 82% more taste buds in a single papilla than in their hybrid.According to data about the predominance of Fu papillae on the ventral surface to dorsal surface on the lingual apex we established in all examined ruminants, the number of taste buds on the ventral apex ranged from 900 to 4000 and was 60-95% higher as on the dorsal surface of the apex. Furthermore, that the calculated number of taste buds in the anterior part of the tongue in cattle, European bison, and their hybrid concerning taste buds on all torus surfaces was higher by 60, 46, and 67%, respectively. A characteristic feature observed in Fu papillae on the anterior dorsal part of the Bison bonasus hybrid compensate a phenomenon of a reduced number of taste buds and in final calculations showed that the number of places of taste perception in the hybrid may be comparable to its parental species, and precisely to cattle. On torus, the total number of taste buds was grown from the medial to the posterior part about 3-5 times depending on species and reached 280-1110. Moreover, in European bison and European bison hybrid, the data showed 41 and 30% higher number of taste buds on lateral surfaces than the posterior dorsal part of the torus. In mentioned species, the total number of taste buds on the whole dorsal surface of the torus is smaller than on lateral surfaces.The well-developed system of taste buds noticed in the anterior part of parental species tongues, i.e., apex and body, support data about the high number and density of Fu papillae, especially on the ventral surface of the lingual apex. It also confirms that the anterior part of the tongue is an important functional area in the initial analysis or preselection of food during food intake. On the other hand, a small number of taste buds per single papilla in the anterior dorsal surface of the examined hybrid tongue seems to be inconsistent with these assumptions. However, a large number of Fu papillae on the dorsal surface of the apex and lingual body in the Functionally, the lingual torus is associated with the process of mastication of food, which was confirmed in our analysis by the smallest total number of taste buds in Fu papillae in the medial part of the torus in all studied ruminants. In some earlier studies, giant Fu papillae in the posterolateral surface of the torus were observed , 54–57. The analysis of 3D models of Fu papillae allowed us to observe for the first time the exact distribution of taste buds on the dorsal surface of Fu papillae. The obtained results are exclusive because we defined the arrangement of taste buds as dispersed manner, but often as diagonal and vertical stripes or clusters in various shapes. In cattle, numerous taste buds were evenly dispersed or formed clusters of taste buds in the middle part of papillae, whereas in European bison, the strips were in the middle part or on borders of Fu papillae. The sparse taste buds in European bison hybrid were positioned most often centrally. If we compare the distribution of taste buds in many parts of the tongue, one observation seems to be exceptional. The taste buds in Fu papillae on the lingual body and on lateral surfaces of lingual torus formed strips mostly arranged perpendicularly to the direction of passage of food along with the tongue. Such tendency of grouping taste buds may allow frontal contact during transportation of food and enhance the taste perception of masticated plant food on the lingual body or during passage grinding food particles in an area between teeth and torus.Bison bonasus hybrid, which is rare in the case of interspecies ruminant achieved in 1970 in Poland and still occurred in farms. The Bison bonasus hybrid called żubroń was crossbred as a farm ruminant due to the desire to establish a new breed of beef cattle adapted to free breeding [Bison bonasus hybrid, quite surprising was ca. 3.5 times larger total amount of Fu papillae than in parental species, and the fact that on each part of the Bison bonasus hybrid tongue, the number and density of Fu papillae per 1 cm2 exceeded values observed in cattle and European bison. However, the protrusion of Fu papillae on each part of tongue was ca. 50% lower papillae then in its parental species. The observations of 3D models of Fu papillae also indicated a small amount or a lack of taste buds in Fu papillae on the lingual apex or the presence of only balloon-like CTCs in Fu papillae on the lingual torus. Our research has shown an interesting similarity of many traits of Fu papillae between the Bison bonasus hybrid and the maternal species i.e., domesticated cattle. These common features are almost equal number of taste buds in cattle and the hybrid as number and density of Fu papillae on the particular parts of torus as well the thickness of the stratified squamous epithelium on Fu papillae. These listed features in Bison bonasus hybrid and earlier described anatomical data might be considered as species-specific, which could be functional implications and/or are consequences arising after hybridization.The obtained comparative data revealed some species-specific features in the breeding . ConsideQuantitative and qualitative data from microscopic observation and computer-aided analysis of 3D models of Fu papillae in three species of ruminants provided new data about the variety of the distribution and microscopic features of Fu papillae over the surface of the tongues. In this respect, two regions of the tongue, i.e., the ventral surface of the lingual apex and posterolateral surfaces of the lingual torus, were designated. The regional differences obtained an evaluation of amount, the density of Fu papillae, and taste buds stay with feeding habits and kind of forage typical for domesticated farm species like cattle and wild living European bison. The characteristic of taste buds system in terms as a number, as well its different arrangement indicated an efficient system of receptions of taste in cattle and less developed complex system in European bison hybrid, which may correlate with evolutionary changes in frame of domestication. The 3D models of Fu papillae in all studied species of ruminants allowed to describe firstly three types of CTC of Fu papillae,As an innovative method, the three-dimensional analysis proved to be a comprehensive tool for evaluating detailed structures of Fu papillae, i.e., the papillae outline, the shape of CTC, and distribution and number of taste buds. Based on numerous results obtained from 3D models of Fu papillae, we state that the application of this modern visualization technique is the most suitable tool to describe gustatory papillae with taste buds system in mammals and could be helpful in normal, pathological, and clinical assessments.Bison bonasus hybrid and its parental species: cattle and European bison. The cattle tongue was obtained in a cattle slaughterhouse near Poznań, the European bison was from Poznań zoological garden, while the hybrid was from the farm in Karolew, near Poznań in Poland. Each tongue was fixed in 10% neutral formaldehyde. A total of 41 Fu papillae were dissected from the dorsal and ventral surface of the apex, body of the tongue, and for first time from three areas i.e., dorsal, posterior, and lateral surfaces of torus of tongue for three-dimensional analysis.The observations and 3D models of Fu papillae were conducted on one tongue of Tissues dissected for histology and 3D reconstructions proceeded into paraplast. Embedded tissues were serially cut on a microtome (Leica RM 2055) into 8 μm slices. All slides were dye using Masson-Goldner method and closed using synthetic resin DePex. Each slide was observed and documented using a light microscope Axioskop 2 plus (Zeiss) with digital camera Gryphax. For each section, magnification × 1.25 was used. A record of lost or unsuitable slices within serial slides was conducted. Depend on the size of Fu papilla 100-300 slices per papilla were used for reconstruction. In order to create 3D reconstructions of Fu papillae, their CTCs, and taste buds, a conversion to a single channel (green channel), an alignment, a recalculation into uniform coordinates, a segmentation, a resampling in X, Y, and Z direction, a surface generation and adjustments steps in professional computer software, called Amira were conducted . Moreover, to better visualization of 3D models, one animation of the Fu papilla from the ventral surface of the apex in cattle also in Amira were prepared . Average values of obtained data with its graphical analysis were estimated in Microsoft Excel (2010). The number and density of Fu papillae were established from macroscopic pictures of particular parts of examined tongues, whereas the number and density of taste buds were obtained from 3D models of Fu papillae.Anatomical nomenclature of the structures of the tongue was taken from Nomina Anatomica Veterinaria (2017) .Additional file 1.


Caesalpinia sappan L. (CS) is widely used to treat diabetic complications in south-east Asia, specifically in traditional Chinese medicine. This study intends to explain the molecular mechanism of how chemical constituents of CS interrelate with different signaling pathways and receptors involved in T2DM. GC-MS was employed to identify the chemical compounds from the methanol extract of CS wood (MECSW). Lipinski’s rule of five was applied, and 33 bioactive constituents have been screened from the CS extract. After that, 124 common targets and 26 compounds associated with T2DM were identified by mining several public databases. Protein–protein interactions and compound-target network were constructed using the STRING database and Cytoscape tool. Protein–protein interactions were identified in 121 interconnected nodes active in T2DM and peroxisome proliferator-activated receptor gamma (PPARG) as key target receptors. Furthermore, pathway compound target (PCT) analysis using the merger algorithm plugin of Cytoscape revealed 121 nodes from common T2DM targets, 33 nodes from MECSW compounds and 9 nodes of the KEGG pathway. Moreover, network topology analysis determined “Fisetin tetramethyl ether” as the key chemical compound. The DAVID online tool determined seven signaling receptors, among which PPARG was found most significant in T2DM progression. Gene ontology and KEGG pathway analysis implied the involvement of nine pathways, and the peroxisome proliferator-activated receptor (PPAR) pathway was selected as the hub signaling pathway. Finally, molecular docking and quantum chemistry analysis confirmed the strong binding affinity and reactive chemical nature of fisetin tetramethyl ether with target receptors exceeding that of the conventional drug (metformin), PPARs agonist (rosiglitazone) and co-crystallized ligands, indicating that fisetin could be a potential drug of choice in T2DM management. This study depicts the interrelationship of the bioactive compounds of MECSW with the T2DM-associated signaling pathways and target receptors. It also proposes a more pharmaceutically effective substance, fisetin tetramethyl ether, over the standard drug that activates PPARG protein in the PPAR signaling pathway of T2DM. Type-2 diabetes mellitus (T2DM) is a global epidemic attributed to the dysregulation of carbohydrate, lipid, and protein metabolism resulting from impaired insulin secretion, insulin resistance, or a combination of both. According to the International Diabetes Federation (IDF) report in 2021, one in ten adults (20 to 79 years) has diabetes mellitus. The IDF also estimated this rate to rise to 643 million by 2030 and 783 million by 2045. Such a significant increase will come from the economic transition from low-income to middle-income levels [T2DM is a complex chronic disease involving genetic predisposition, environmental and behavioral risk factors ,4. Upon Caesalpinia sappan, which has been reported as having anti-diabetic activity in several recent studies [Pharmacologically significant agents, such as biguanides, sulfonylureas, meglitinides, thiazolidinediones, alpha-glucosidase inhibitors, incretin-based therapies, dipeptidyl-peptidase IV inhibitors, insulin analogs, and bromocriptine, have been reported as potential for T2DM patients in clinical scenarios ,13,14,15 studies ,22,23,24Caesalpinia sappan L. is also known as Biancaea sappan (L.) Tod and belongs to the Caesalpiniaceae family. It is commonly recognized as sappan wood and Indian redwood. It has traditionally been utilized in Ayurvedic diabetes therapy [C. sappan showed significant anti-diabetic activity in alloxan-induced diabetes mellitus in rats and alpha-glucosidase inhibitory activity [C. sappan wood (CSW), we intend to predict its interaction mechanism with metabolic pathways and genes that express proteins involved in the progression of T2DM.  therapy . Water e therapy . CSW exh therapy ,28,29,30 therapy ,32,33. Mactivity ,35. Brazactivity . Fructosactivity . Furtheractivity ; dibenz[activity ; and homactivity —were extactivity ,42. CSW activity . Hence, A more efficient and safer treatment for T2DM demands a proper explanation of its molecular etiology. Network pharmacology is a well-defined method to investigate the interactions between target compounds, genes, and proteins associated with the disease ,45. It cCaesalpinia sappan wood (CSW) was collected from Chuncheon local market and was authenticated by Dr. Dong Ha Cho, plant biologist and Professor, Department of Bio-Health Convergence, College of Biomedical Science, Kangwon National University. A voucher number (CRT 215) was stored at Kenaf Corporation in the Department of Bio-Health Convergence, and the collected material can be used only for research purposes. The collected dried wood (500 g) was grinded into a coarse powder using an automated grinder. The fined powder (100 g) was soaked in 500 mL of methanol . The mixture was put in a sealed bottle and placed for continuous shaking and stirring (for 3 days) through an automated shaker machine (repeated 3 times for the highest yield). The mixture was filtered  and evaporated using a vacuum evaporator. The evaporated sample (MECSW) was dried under a hot water bath  at 40 °C. The yield was found 3.4 g, which was preserved in a refrigerator (−4 °C) for further GC-MS analysis.In order to perform the GC-MS study on MECSW, we adopted the GC-MS equipment together with an analytical capillary column DB-5MS (30 m × 0.25 µm × 0.25 mm). Our prior research outlined the whole methodology in precise detail .https://pubchem.ncbi.nlm.nih.gov/, accessed on 27 July 2021) database.The drug-likeness approach (Lipinski’s rule of five) was used to screen MECSW’s bioactive compounds (identified by GC-MS), which overcome ADME  restrictions while securing oral bioavailability score > 0.50. An online program called Swiss ADME  was usedhttp://sea.bkslab.org/, accessed on 2 August 2021) and STP (Swiss Target Prediction)  databases, respectively. The DisGeNeT  [https://www.malacards.org/, accessed on 6 August 2021) [https://www.ncbi.nlm.nih.gov/omim, accessed on 6 August 2021) [https://bioinfogp.cnb.csic.es/tools/venny/, accessed on 15 August 2021) identified and exhibited the common overlapping targets between MECSW bioactive compounds and T2DM targets.By using the Homo sapiens mode, we collected targets linked to the filtered bioactive compounds by putting their SMILES into the SEA (Similarity Ensemble Approach) (st 2021) , Malacarst 2021) , and thest 2021)  databasehttps://string-db.org/, accessed on 24 August 2021) for protein queries. The Cytoscape 3.8.2 software program [Homo sapiens with a confidence level of >0.4 in protein interactions, the intersected targets between the compound-related gene and T2DM target genes were included into the STRING database Version 11.0b  network was created using the preprocessing output of bioactive chemicals from MECSW and frequent T2DM targets that intersected with the MECSW. This network was built using Cytoscape’s merger algorithm plugin (Cytoscape 3.8.2). An analysis of network topology parameters was performed with the help of the network analyzer. Nodes represented bioactive compounds, targets, and pathways, and interactions between these components were shown along the edges. The degree also referred to the prevalence of a node’s interconnected neighbors. The greater the percentage of a node’s that are directly linked to each one, the greater the impact .https://david.ncifcrf.gov/tools.jsp, accessed on 19 September 2021)  database to determine their role in signaling pathways. For this enrichment study, the identifier was OFFICIAL GENE SYMBOL, and the species was Homo sapiens. This database is exceptionally crucial in network pharmacology because it shows targets implicated in disease underlying molecular mechanisms, and the GO database exhibits the descriptive biological terms of those targets, such as biological process (BP), cellular component (CC), and molecular function (MF) [p-value <0.05 was used as a cutoff for GO and pathway enrichment. The p-value was corrected using a false discovery rate (FDR) error control technique, and the result was known as the Q value. It was possible to visualize the KEGG pathway bubble plot map visually by using Origin Pro 2021 to examine the routes.The KEGG pathway interpretation and molecular functional annotation of all intersected targets were undertaken on the DAVID (ion (MF) . A p-valhttps://www.rcsb.org/, accessed on 11 October 2021) and UniProt database , respectively. Following our previously reported processes, we set the Schrödinger Suite-Maestro v1. 5 integrated Protein Preparation Wizard tools after the 3D crystal structure was located in the RCSB database [The PubChem chemical library was used to obtain the .sdf files of the preferred ligands revealed via compound-target network research along with conventional pharmaceuticals, such as metformin, and co-crystallized protein ligands. As previously reported, the constituents were indeed prepared for molecular docking investigations incorporating the LigPrep tool in Schrödinger Suite-Maestro v12.5 . PPARG , we used Glide tools integrated in Schrödinger Suite-Maestro version 12.5 software . Their dUtilizing the Jaguar panel of Maestro 12.5 software, the key compound (fisetin tetramethyl ether) and standard drugs’ (metformin) structural variables were absolutely optimized using the Lee–Yang–Parr (B3LYP-D3) correlation functional technique at the 6–31G++  level basis set . This opThe data obtained from the gas chromatography-mass spectrometry (GC-MS) study revealed 33 significant bioactive constituents in the MECSW . Table 1The primary bioactive ingredients from MECSW were screened based on the drug-likeness properties of recorded small molecules. Those properties were stated as molecular weight must not be over 500, a number of H-bond donor definitely below 10, contrarily H-bond acceptor must not exceed 5, moriguchi octanol–water partition coefficient value must be below or equal to 4.15, and ‘Abott Bioavailability Score’ should be under 0.1 standard value. Remarkably, all of the identified small molecules (33) occupied the above-mentioned criteria and were classified as significant bioactive substances without infringing more than one of the features mentioned earlier .The screened bioactive compounds were subjected to acquiring compound pertaining targets from public databases. In parallel, each component’s SMILES code was acquired from the PubChem chemical library and entered into the SEA and STP database queries. The removal of duplication targets conveyed the presence of 844 compounds linked to targets from the STP database and 489 targets from the SEA database . The resT2DM targets were gleaned from accessing three disease-related public databases, including DisGeNeT, OMIM and Malacards, which resulted in the procurement of 3081 disease-related targets . After tTo unearth the potential mechanistic insight of MECSW to treat T2DM, we introduced those 124 common targets into the STRING database to build up a network within them. Meanwhile, the STRING algorithm expressed those 121 nodes connected by forming 596 edges . The aveTo ascertain the most significant key components among the detected ingredients from MECSW, we constructed a pathway compound target network utilizing Cytoscape. This network displayed how pathway, compound and T2DM targets correlated among them. The topological parameter analysis of PCT network via network analyzer apps integrated into Cytoscape revealed that this network consists of a total of 166  nodes along with interacting within them through 319 edges , whereasThe GO and KEGG pathway appraisal truly reflects the intersected 124 common targets engaged in the T2DM functional process through relevant targets molecular function (MF), the biological process (BP) in which it takes part, and its cellular localization (CL). We utilized a web-based tool, “DAVID”, to analyze GO and KEGG pathways. With respect to GO, we determined the top 10 MF, BP, and chemical contents based on the proportion of targets that were enriched in those categories , where Mp-value < 0.05. Further, we took into account the rich factor and adjusted the FDR value  to define the pathway enrichment analysis, in which the rich factor unveils the amount of pathway enrichment with a substantially lower Q value  demonstrated a greater binding affinity of −6.092 Kcal/mol than thiazolidine derivatives , co-crystallized ligand  and metformin  via interacting eleven H-bond of ARG-288, SER-342, ILE-262, GLY-258, LEU-340, and GLU-259 residues; and twelve H-phobic bonds of ARG-288, ILE-249, LEU-333, LEU-255, ARG-280, ILE-281, CYS-285, and ILE-341 residues with fisetin tetramethyl ether A.The docking feature of PPARA (PDB ID: 1K7L) also reflected the advancement stabilization of 1K7L—fisetin tetramethyl ether interface complex having −5.563 Kcal/mol of seven hydrogen bonds ; seven hydrophobic interactions , and one pi-sulfur bond (MET-320) in contrast to co-crystallized ligand , fibrate drug , rosiglitazone  and the standard medicine metformin  B.In the same way, PPARD (PDB ID: 5U3Q) had the most apparent binding affinity towards fisetin tetramethyl ether aiming at −5.58 Kcal/mol, which was markedly different from rosiglitazone , metformin  and co-crystallized ligand  molecules binding energy. Of that prominent interaction was worked out forming eight hydrogen bonds of MET-192, ALA-306, ASN-307, PHE-190, GLU-259, LEU-304, and ILE-290; five hydrophobic bonding of LEU-304, ILE-290, LEU-294, and LYS-229; one electrostatic bond of GLU-259; and one pi-sulfur bond of MET-293 residues C.Following that, fisetin tetramethyl ether formed nine hydrogens and two electrostatic bonds with GLU-62, THR-61, THR-74, THR-75, ASN-60, ASP-72, ASP-78, LYS-59 and ASP-78 residues of 5HZ9, which all have a significant influence on the FABP3 association (PDB ID: 5HZ9) D. The esFisetin tetramethyl ether binds to the FABP4 (PDB ID: 3P6D) receptor, resulting in −3.816 kcal/mol across nine H-bonds  and three H-phobic bonds (VAL-73), which was considerably lower than co-crystallized ligand −3.74 Kcal/mol, rosiglitazone −3.196 Kcal/mol and the conventional medicine metformin −2.81 kcal/mol E.The active pocket of MMP1 (PDB ID: 1SU3) association with fisetin tetramethyl ether revealed eight H-bonds with HOH-951, GLN-50, SER-172, PRO-173, GLU-39, LYS-36, ASN-43 and six H-phobic bonds with LYS-36, LYS-40, and PRO-95 residues, ultimately exposing a docking score of −4.043 kcal/mol, which was of much lower energy than the competitive agonist rosiglitazone  and conventional medicine metformin , but nearly identical to the co-crystallized ligand  F. MeanwhNevertheless, compared to the corresponding agonist, the co-crystallized ligand and metformin, the vital essential component of MECSW, showed excellent binding affinity, implying that those binding complexes had greater binding stability than their synthetic counterparts. In contrast, this remarkable finding was not lined with the NR1H3 target in terms of metformin and rosiglitazone, which had a comparatively lower binding affinity. Density functional theory (DFT) was used to figure out molecular descriptors scoring functions, such as enthalpy, Gibbs free energy, HOMO, LUMO, hardness, and softness energy of key compound and standard medicine, to clearly define their reactive chemical nature, structural properties and regions of the molecules. Whilst the negative values of thermodynamic characteristics of fisetin tetramethyl ether are higher than metformin, the thermodynamic attributes of the key compounds are preferably excellent. The compound’s electron-donating and accepting aptitude are symbolized by the HOMO and LUMO frontiers molecular orbitals, respectively. Similarly, fisetin tetramethyl ether also has the highest negative energy of −0.21123 Kcal/mol, signifying that it is the ideal electron donor ingredient than metformin. Hardness and softness are defined as an energy gap, which are determinant hallmarks of compounds’ chemical reactivity . ApproprA synergistic attributable pattern of multi-pathways, multi-components and multi-targets has constantly been observed in medicinal plants. It is more complex to evaluate the herbal treatment efficiency in one drug-one target model due to the presence of multi-diverse chemical ingredients in plants . To metaPathway compound target (PCT) network revealed that 124 T2DM genes were linked to 26 chemicals  and 9 pathways in the diagnostic mechanism of T2DM. Similarly, the KEGG pathway also suggested that the nine pathways  influenced the progression and diagnostic process of T2DM, where the gene ontology (GO) resolution of MF, BP and CC was used to perform the functional process of the common targets between T2DM and compound-related genes. The biological process analysis found that common targets were mainly enriched in oxidation–reduction processes backed by the positive–negative regulation of transcription, transcription initiation, signal transduction, etc., which were possibly located in the integral component of the membrane followed by plasma membrane extracellular exosome, cytosol and others; at a molecular level, the genes participated in zinc ion binding, DNA binding, enzyme binding, and heme binding with notably ligand-activated sequence-specific binding potentiality. The relationship between the nine different types of T2DM related pathways is discussed below.Prostate cancer: insulin-deprived environment in long-term diabetes results in a lower amount of insulin-like growth factor-1 receptor (IGF-1R), and higher plasma IGF-1 is associated with a higher risk of prostate cancer ,67,68. TThe upregulation of PPARG in the PPAR signaling pathway regulates glucose homeostasis through modulating glucose transporter type 4 (GLUT4), beta-glucokinase, and c-Cbl–associated protein (CAP) in the adipose tissue . In addiIn the context of clockwise gene  activation, we conducted docking assays using the key compound “fisetin tetramethyl ether”, co-crystallized ligand, corresponding gene activation agonist (rosiglitazone and bezfibrate) and the standard medication metformin. As an equivalent to those gene agonists, the key ingredient had excellent activation energy over co-crystallized ligand affinity and metformin binding affinity. This indicates that fisetin tetramethyl ether has the potential to activate PPARs. Several pre-clinical studies also reported fisetin for improved antihyperlipidemic effect and antihyperglycemic effect in streptozotocin-induced diabetic rats ,98,99. HAdditionally, the quantum chemical assessment of the key compound and metformin at the DFT  level was used to verify their chemical reactivity with targets or other species. The HOMO and LUMO energy gaps are essential in influencing medicines’ kinetic stability and chemical reactivity . A largeThis study hypothesized the molecular insight of the bioactive presence in MECSW to treat T2DM employing network pharmacology. Importantly, this study’s findings show that MECSW might work through targeting the PPAR signaling pathway, where the key bioactive “fisetin tetramethyl ether” exhibited tremendous impact on this pathway in the regulation towards T2DM. These speculative mechanistic actions were further affirmed by molecular docking simulations, in which this key constituent showed excellent binding affinity with each protein agonist involved in the PPAR signaling cascade and conventional pharmaceuticals. In addition, this compound’s quantum chemistry was also characterized by its superior chemical reactivity over the usual medicine. Ultimately, this study provides empirical testimony to substantiate the treatment effectiveness of MECSW on T2DM and outlines profound insights into the bioactive, interacting potential target, and modes of action of MECSW against T2DM, thus adding to the existing knowledge.


Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes. Cancer is associated with abnormal alterations that lead to the dysregulation of the cellular system . Breast In literature, some existing pathway-based microarray analyses are restricted to binary class classification , includiSeveral studies in pathway-based microarray analysis did not select pathways ,13,14,28Here, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass pathway expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The ANOVA used in eDRW+ is intended to identify differentially expressed genes for multiple cancer subtypes. Hence, the weight of the genes is also essential to identify informative genes in the directed graph. Subsequently, the pathway selection used in eDRW+ is intended to select the top 100 ranking pathways in the pathway activities based on the highest reproducibility power, increasing the method’s classification performance. The proposed method is implemented in the R platform with version 3.3.3 in 64-bit using Windows 10. The input data used are gene expression data, pathway data, and a directed graph. The gene expression data were downloaded from NCBI’s Gene Expression Omnibus (GEO) repository (breast cancers: GSE1456 and GSE1561). This data was identified using microarray analysis ,33. GSE1DRWPClass. The directed graph covered 4113 genes and 40,875 directed edges (interaction between genes)  (accessed on 23 July 2021) and the Functional Annotation tool from the Database for Annotation, Visualization, and Integrated Discovery (DAVID) [https://david.ncifcrf.gov/summary.jsp] (accessed on 3 August 2021). PubMed text data mining is used to show the relationship between pathways, genes, and cancers [The informative pathways and genes identified by eDRW+ were then analyzed using PubMed text data mining [ cancers ,48,49. “ cancers ,51,52,53The results were collected from ten runs of an experiment in a stratified ten-fold cross-validation within and between datasets based on breast cancer (GSE1456 and GSE1561). Previous researchers have successfully analyzed the GSE1456 and GSE1561 datasets to link the identified pathways and genes with breast cancer, with high prediction accuracies . Besidesmctest function in the R mctest package has been applied to detect the multicollinearity diagnostic measures on the studied datasets. Multicollinearity analysis can also avoid multicollinearity problems [The problems . This prBecause the raw GSE1456 dataset was collected from all of the breast cancer patients, it has been provided with the clinical response data, like tumour subclasses and the detection of relapse in receiving surgery. However, GSE1561 did not have clinical response data. The variables of the GSE1456 dataset are Subtype , SURV_RELAPSE (time until relapse/no relapse), and SURV_DEATH (time until death/censoring). p-values and a 95% confidence interval. The eDRW+ demonstrated consistent and significant improvements over the other methods, with a minimum average difference of 0.65%.In previous research, some of the methods for pathway-based microarray analysis were restricted to use in binary class classification, and did not select pathways ,28,59. TMean-centering and magnitude-normalization, PCA and AHC were used to analyse the patient’s prognoses and predict their survival accuracy. However, it is insufficient to manipulate gene expression data only to study complex diseases ,8,59. OtA prognostic marker helps to identify a disease outcome, which can be beneficial in cancer treatment and drug discovery . In prevThe identification of prognostic markers for multiclass cancer expression data has been proposed using eDRW+. eDRW+ has used the ANOVA F-test and pathway selection to improve the weight of the genes, and to identify pathway markers and gene markers. The ANOVA F-test is commonly used to deal with multiclass classification, and pathway selection can select informative pathways. However, the use of the ANOVA F-test influences the calculation of the weight of the genes in the directed graph that is essential to infer a higher reproducibility power of pathway activity. A prognostic marker helps to identify a disease outcome for cancer treatment and drug discovery. Hence, the eDRW+ has successfully identified the cell cycle, p53 signalling pathway, and TP53 gene as cancer markers for breast cancer. Using PubMed text data mining and the Functional Annotation Tool for biological context verification and validation, the TP53 gene was significantly associated with the development and invasion of tumour cells.The weight of the genes is essential for pathway topology-based microarray analysis that shows the interaction between genes that have connected with each other, especially in the directed graph . Further