Valeriana jatamansi Jones  has previously been shown to be effective for the treatment of anxiety disorder. In this study, the dose ratio of each component of the anxiolytic compounds group (ACG) in a 95% ethanol extract of ZZX was optimized by a uniform design experiment and mathematical modeling. The anxiolytic effect of ACG was verified by behavioral experiments and biochemical index measurement. Network pharmacology was used to determine potential action targets, as well as predict biological processes and signaling pathways, which were then verified by molecular docking analysis. Metabolomics was then used to screen and analyze metabolites in the rat hippocampus before and after the administration of ZZX-ACG. Finally, the results of metabolomics and network pharmacology were integrated to clarify the anti-anxiety mechanism of the ACG. The optimal dose ratio of ACG in 95% ethanol extract of ZZX was obtained, and our results suggest that ACG may regulate ALB, AKT1, PTGS2, CYP3A4, ESR1, CASP3, CYP2B6, EGFR, SRC, MMP9, IGF1, and MAPK8, as well as the prolactin signaling pathway, estrogen signaling pathway, and arachidonic acid metabolism pathway, thus affecting the brain neurotransmitters and HPA axis hormone levels to play an anxiolytic role, directly or indirectly.Anxiety disorder impacts the quality of life of the patients. The 95% ethanol extract of rhizomes and roots of  Anxiety disorder is a mental disease characterized by persistent, excessive worry, and anxiety symptoms ; with thValeriana jatamansi Jones. It is a traditional Chinese medicine (TCM) that has the effect of calming nerves, according to the Chinese Pharmacopoeia. ZZX has been used to treat mental diseases worldwide [Zhi zhu xiang (ZZX) is the rhizome and root of orldwide , and hasorldwide ,8, GABA orldwide , cell aporldwide , etc. Baorldwide , and theorldwide . The maiorldwide , which iorldwide , includiNetwork pharmacology is an effective method that integrates chemical informatics, bioinformatics, network biology, and traditional pharmacology to reveal the bioactive components and complex mechanisms of TCM. It allows us to study the role of multiple compounds in biological networks and to explore multi-target mechanisms of TCM ,14. MetaTherefore, in this study, the method of uniform design combined with mathematical modeling was used to optimize the proportion of ACG in a 95% ethanol extract of ZZX to achieve the best anxiolytic effect in vivo. The anxiolytic action pathway and core targets of the four compounds were predicted by network pharmacology, which were verified by molecular docking. Differential metabolites were screened in the rat hippocampus before and after the administration of ACG using metabolomics. Finally, integrated analysis of network pharmacology and metabolomics data was conducted to explore the mechanism of ACG in order to provide reliable ideas for the development of anxiolytic drugs.Valeriana jatamansi Jones by Prof. Jinli Shi of Beijing University of Traditional Chinese Medicine—the voucher specimen (S180930) was deposited in the Herbarium of Beijing University of Traditional Chinese Medicine. The 95% ethanol extract of ZZX was used for the verification test, and the preparation process was as follows: 150 g of ZZX coarse powder was soaked in 1500 mL of 95% ethanol for 30 min and extracted at 40 °C using an ultrasonic system . The extracted solution was dried using a rotatory evaporator below 35 °C, and freeze-dried to a constant weight (17.72 g). The extract was dissolved with 1% Tween-80 before use.ACG in a 95% ethanol extract of ZZX, including acevaltrate, valtrate, and baldrinal, was purchased from Sichuan Weikeqi Biological Technology Co., Ltd. . 11-ethoxyviburtinal was extracted in the laboratory . Compounds were dissolved with 1% Tween-80 before use. ZZX was purchased from Jiudingshan, Sichuan, China (batch No. 2018S10), and was identified as the dried rhizome and root of Specific-pathogen-free (SPF)-grade male Sprague Dawley (SD) rats, weighing 150–170 g , license No.: SCXK (Jing) 2016-0006), were individually housed in a standardized feeding environment with a 12 h light/dark cycle (light on 7:00–19:00) at 22 ± 2 °C and 60 ± 5% relative humidity. Before the experiment, the rats were provided standard feed and clean drinking water ad libitum. the experimental procedures were approved by the Animal Care and Use Committee of the Institute of Psychology of the Chinese Academy of Sciences (No. 20170327), and in compliance with the Provision and General Recommendation of Chinese Experimental Animals Administration Legislation.The empty bottle stress (EBS) experiment was conducted for 21 days to establish an anxiety model . EBS can5) and the corresponding use table  group, and UDG (UDG1–UDG8) groups. After 7 days of water drinking training and 7 days of empty bottle stress, the control group, model group, DZP group, and UDG groups were administered drugs by gavage for 7 days (1 mL/100 g/day) from the 15th day. The control group and model group were given 1% Tween-80 solution, rats in the DZP group were given diazepam (1 mg/kg/day), and the UDG groups were given a mixture of four ingredients according to The EPM has been widely used to evaluate potential therapeutic drugs for anxiolytic effects in rodents , and, inThe OFT has a solid theoretical rationale for detecting anxiety and the effectiveness of pharmacological agents . Before HPA axis abnormalities and neurotransmitter disorders are two important mechanisms of anxiety and can also be used as biochemical indicators to determine anxiety . Studies1–X4), and the comprehensive pharmacodynamics, including the index of EPM, OFT, and contents of CRH, ACTH, CORT, DA, 5-HT, NE, GABA, AEA, and BDNF, were calculated by entropy weighting as the dependent variable (Y) [The optimal proportion was determined as described previously . The conable (Y) . MinitabTo verify the anxiolytic effect of ZZX-OS and to compare the accuracy of the calculation, rats were randomly divided into a control group , model group , DZP group , ZXX-OS groups, and 95% ethanol extract of ZZX group . The EBS-induced rat anxiety model combined with EPM and OFT was used for investigating the pharmacodynamics. The anxiolytic effects of OS and 95% ethanol extract of ZZX were compared.(#)p < 0.05 and **(##)p < 0.01 were considered statistically significant.SPSS 17.0 software was used to analyze the data. The results are expressed as the means ± SEM. One-way ANOVA with Dunnett’s multiple comparisons test was used to determine differences between groups. *https://pubchem.ncbi.nlm.nih.gov/, 16 September 2019) and CASC Databases . The 2D and 3D structures were stored in the SDF format. We uploaded the SDF format files of acevaltrate, valtrate, baldrinal, and 11-ethoxyviburtinal to the Pharmmapper Database  to predict the candidate target of each compound. The target results were also predicted by importing the SMILE number into the Swiss Target Prediction database , followed by integrating the data to remove duplicates.The relevant information of four compounds, including the molecular weight, SMILE numbers, two-dimensional (2D) structure, and three-dimensional (3D) structure, was obtained using the PubChem , TTD , CTD , Genecards , and Drugbank . The keywords “anxiety” and “anxiety disorder” were used to search the reported genes, limiting the species to “Homo sapiens”, and integrated the target information related to anxiety. Duplicate genes and false-positive genes were removed.Anxiety disorder related-targets were collected from multiple databases, including PharmGKB , and Venny 2.1.0  was used to draw a Venn map of the target genes for the four compounds and anxiety-related target genes, resulting in common target genes. The protein–protein interaction (PPI) relationship between the common target genes was obtained using the STRING  and GPS-Prot Databases . The data were imported into Cytoscape 3.7.2 software to construct the PPI network of “anxiolytic ingredient groups–anxiety disease target gene”. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the DAVID  database, and the result was deemed significant at p < 0.05.The official names of target genes were obtained from the UniProt database . PyMOL 2.3.4 software was used to remove water and ligands, and MGLTools  was used to perform hydrogenation and charge calculations on the screened receptor protein. AutoDock Vina was used to dock the receptor protein and ligand small molecules, take the best-scored conformation, and draw the docking with PyMOL 2.3.4 software.To prove the accuracy of the network pharmacological prediction results, four compounds were used to dock with key target proteins. We used Open Babel 2.3.2 software to convert the SDF files into PDB files, and the receptor protein was retrieved from the Protein Data Bank database (g r/min) for 20 min. The supernatant was transferred into the autosampler vial. Then, 175 μL of pre-chilled methanol/chloroform (v/v = 3/1) solution was used for secondary extraction and centrifugation . Then, 200 μL of the supernatant was transferred to a vial. The remaining supernatant was collected for quality control samples. All samples were evaporated using a vacuum concentrator to remove chloroform and further lyophilized using a freeze-dryer. Fifty microliters of methoxyamine was added to the dried sample, derivatized at 30 °C for 2 h, and then 50 μL of MSTFA (1% TMCS) containing FAMEs was added as retention indices at 37.5 °C for another 1 h.The rats from the control group, EBS group, and ZZX-OS group were decapitated immediately after the behavioral test, and the cerebral cortex and hippocampus were quickly dissected on ice and stored at −80 °C. Before detection, the frozen samples were mixed with 25 mg of pre-chilled zirconium oxide beads and 10 μL of internal standard. An aliquot of 50% pre-chilled methanol (50 μL) was then added. Automatic homogenization was conducted, followed by centrifuging at 4 °C  with an Agilent 7890B gas chromatography Rxi-5sil MS capillary column  was used, with helium as carrier gas and a constant flow rate of 1.0 mL/min. The temperature of the sample injection and transfer interface was set to 270 °C, and the source temperature was 220 °C. Detection in the full scan mode (m/z 50–500) was conducted with electron impact ionization (70 eV).t-test (t-test), the Mann–Whitney–Wilcoxon test (U-test), ANOVA, and the Kruskal–Wallis test. p < 0.05 and p < 0.01 were considered statistically significant. We integrated the results of multivariate and univariate statistical analyses to identify differential metabolites, and then inputted the potential biomarkers into Metaboanalyst to construct the metabolic pathways.By comparing the retention index and mass spectrum data obtained by XploreMET software  with the data generated by the reference standard of the known structure in the JiaLib metabolite database, the metabolites in the rat hippocampus were identified. Two statistical analysis methods were used: (1) multivariate statistical analysis, including principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA), and (2) univariate statistical analysis, including Student’s The names of different metabolites in the rat hippocampus under the action of ACG in 95% ethanol extract of ZZX were imported into the KEGG database for ID conversion, and the ID information was input into Metscape (plug-in of Cytoscape software 3.7.2) to obtain the link proteins of differential metabolites, which were used as the action targets of differential metabolites. The target information obtained from metabolomics and network pharmacology was then imported into the STRING database and GPS Prot database for protein–protein interaction (PPI) analysis to visualize the PPI network, and the relevant targets were obtained to reveal the mechanism of ACG in the treatment of anxiety disorder.p < 0.01). The OE% and OT% in the DZP group increased significantly compared with those of the EBS rats (p < 0.01). Except for the UDG-3, UDG-7, and UDG-8 groups, the UDG treatments significantly increased the OE% and OT% of rats (p < 0.01 or p < 0.05). In UDG-3, UDG-7, and UDG-8, the OT% value increased higher than that of the EBS group and followed the same trend as that of the other UDG groups. The EPM test in rats showed that the ACG in 95% ethanol extract of ZZX had a certain anxiolytic effect.The results of the EPM test are shown in p < 0.05). Compared to the EBS group, DZP improved CE and CT (p < 0.05). The values of CT and CE for the UDG-1, UDG-4, UDG-7, and UDG-8 groups were significantly increased compared with that of the EBS group, (p < 0.01 or p < 0.05). The remaining UDG groups had higher scores. The results showed that the ACG in 95% ethanol extract of ZZX could alleviate anxiety-like behavior  and time spent in the central areas (CT) by the EBS group were reduced (behavior .p < 0.01), while the levels of GABA, AEA, and BDNF were significantly decreased (p < 0.01). The pharmacodynamic indexes of the UDG groups (UDG-1–8) were statistically different from those of the EBS group (p < 0.05 or p < 0.01); their trends were similar to those of the DZP group (p < 0.01), indicating that the ACG with different doses and proportions have different anxiolytic effects.The results in −8X42 + 20.786X1X2 + 5.398 × 10−7X3X4 − 7.146 × 10−8X32. , and the optimal solution (OS1) was: X1: 0.021, X2: 0.015, X3: 0.655, X4: 8.845, Unit: mg/kg.The stepwise regression equation of dose (X) and comprehensive efficacy (Y) was Y = 0.571 − 1.085 × 101 + 222.81X2 − 8.8X3 + 1.99X4 − 116.57X12 + 226.90X1X2 − 22.48X1X3 + 43.36X1X4 + 491.81X22 + 5X2X3 + 37.63X2X4 − 0.59X32 − 0.6X3X4 + 0.05X42 . The optimal solution (OS2) obtained by solving the equation was: X1: 0.489, X2: 0.500, X3: 0.655, X4: 8.845, Unit: mg/kg.The partial-least-squares regression equation was Y = 1422.91 − 36.03X1 and OS2 administration groups than that in the EBS group (p < 0.05). The values of OT% in the ZZX extract and OS2 groups were also increased (p < 0.05). There was no significant difference between the two OS groups, DZP group, and ZZX group. In the open field test, the indexes in the ZZX, OS1, and OS2 groups were significantly higher than those in the EBS group (p < 0.01). Although there was no significant difference between the three groups, the CT and CE of rats in OS1 and OS2 were somewhat higher than those of the ZZX group, and the OS2 group performed the best (1 and OS2) had anxiolytic effects, which may have had the same anxiolytic effect as the extract, or even better than that of the extract.In the EPM test, the values of OE% were higher in the OSthe best . The resBased on network pharmacology analysis, 2434 anxiety targets and 206 ACG targets were obtained, and the number of common targets of compounds and diseases was 152. A Venn diagram of ACG target genes and anxiety-related target genes was drawn A. The PPp-value, and retained 20 signaling pathways related to anxiety disorders  of the estrogen and prolactin signaling pathways were the highest, showing that these two pathways play a crucial role in the anti-anxiety process of ACG. The enrichment results of KEGG pathway analysis are extensive, involving a variety of biological pathways  and biological molecules , which may be related to anxiety disorder. A Target–Pathway network was constructed, as shown in The GO function of 152 target genes mainly focused on the following aspects A: (1) reisorders B, in whiThe core target proteins AKT1, EGFR, ESR1, MAPK8, MMP9, and SRC that belong to the prolactin and estrogen signaling pathways from the PPI analysis were selected for molecular docking with four compounds in ACG, and their docking score was between −6.1 kcal/mol and −8.3 kcal/mol , indicatt-test or Mann–Whitney U test) were conducted to screen potential biomarkers.The metabolites were identified using the XploreMET software, combined with previous literature and the JiaLib database. A total of 73 compounds were identified, and the metabolite names and category are shown in PCA analysis was used to obtain the principal component score diagram . 2Y = 0.973, Q2 = 0.665, indicating that this model has a reliable predictive ability and can protect against over-fitting. Then, the marker metabolites were screened using a volcano plot based on the results of OPLS-DA. When the variable influence on projection (VIP) was >1.0, it was considered to be significantly different. The values of VIP and the correlation coefficients are shown in the volcano plot  based on the screening conditions (p < 0.05). The results are shown in the volcano plot . The abundances of seven differential metabolites in the administration group were relatively similar to those in the control group, indicating that the ACG had a callback effect on the metabolic disorder of anxiety rats caused by EBS. These seven metabolites were included in the 15 differential metabolites screened above. As shown in To further elucidate the mechanism of ACG of 95% ethanol extract in ZZX, the metabolic profiles of the hippocampus in the control group, model group, and ZZX-OSMetabolic pathway analysis of differential metabolites can reflect the biochemical disturbance of anxiety in rats induced by EBS, and may provide information for elucidating the action mechanisms. The information of the above seven metabolites was analyzed and four metabolic pathways were enriched, including arachidonic acid (ARA) metabolism, unsaturated fatty acid biosynthesis, glycolysis or gluconeogenesis, and pyruvate metabolism. The ARA metabolic pathway was the most significant one . Because n-methylalanine, pentadecanoic acid, and cellobiose in differential metabolism could not be extracted in Metscape analysis, four metabolites were introduced into the Metscape plug-in to obtain 73 related protein targets .To comprehensively explore the potential mechanism of the anxiolytic ingredient group in 95% ethanol extract of ZZX in vivo, we integrated 73 differential metabolites obtained from metabolomics and 152 action targets obtained from network pharmacology. These data were imported into STRING and GPS-Prot databases to obtain PPI analysis data, and then imported into the Cytoscape software to visualize the PPI network . It was Compared with the medicinal materials and extracts, the effective compounds group (ECG) of traditional Chinese medicine has the advantages of clear components, controllable quality, and stable curative effect. To reasonably utilize effective compounds, it is necessary to optimize the dose of ECG administration. The key to optimization lies in the selection of the optimization method. In terms of experimental design, the uniform design not only fully considers the uniformity of experimental points, but also makes each experimental point more representative, can be used to minimize the occurrence of experimental errors, and improve the experiment efficiency on the premise of reducing the number of experiments . In termIn this study, a uniform design combined with PLSR and stepwise regression was used to optimize the best proportion of four anxiolytic compounds in 95% ethanol extract of ZZX: 11-ethoxyviburtinal (0.489 mg/kg), baldrinal (0.500 mg/kg), acevaltrate (0.655 mg/kg), and valtrate (8.845 mg/kg). In recent years, the experimental design combined with mathematical modeling has been used to optimize the proportion of multi-drug combinations, and is an effective, multi-objective optimization method ,26. Our Based on the significant anxiolytic effect of ACG in 95% ethanol extract of ZZX, the mechanisms underlying its effect were further explored. Network pharmacology could predict the possible pathways involved in the drug treatment effects and reveal the relationships between compound targets and disease-associated proteins , especiaThe prolactin and estrogen signaling pathways have been extensively studied in the field of anxiety disorders. It has been reported that a combination of prolactin and its receptor can activate the MAPK pathway to mediate the phosphorylation of ERK, affect the expression of CRH, and regulate the response of the HPA axis . ProlactAlthough network pharmacology has been considered as an effective method to complement the established pharmacological approaches , the posTo systematically and comprehensively elucidate the anxiolytic mechanism of the ACG in 95% ethanol extract of ZZX, the targets of network pharmacology and differential metabolites were integrated to construct a PPI network of common targets . These tThe proportion of the anxiolytic compounds group in 95% ethanol extract of ZZX were determined to be 11-ethoxyviburtinal (0.489 mg/kg), baldrinal (0.500 mg/kg), acevaltrate (0.655 mg/kg), and valtrate (8.845 mg/kg). Our results showed that ACG had a significant anti-anxiety effect, which was superior to that of the extract of the raw materials. Through ALB, AKT1, PTGS2, CYP3A4, ESR1, CASP3, CYP2B6, EGFR, SRC, MMP9, IGF1, MAPK8, and other targets, ACG in the 95% ethanol extract of ZZX could regulate the prolactin signaling pathway, estrogen signaling pathway, and ARA metabolism pathway, thereby directly or indirectly affecting the brain neurotransmitter levels and HPA axis hormone levels to exert an anti-anxiety mechanism. This study provides a scientific basis for the development of ZZX as a safe and reliable anxiolytic drug.


Pathway annotation tools are indispensable for the interpretation of a wide range of experiments in life sciences. Network-based algorithms have recently been developed which are more sensitive than traditional overlap-based algorithms, but there is still a lack of good online tools for network-based pathway analysis.We present PathwAX II—a pathway analysis web tool based on network crosstalk analysis using the BinoX algorithm. It offers several new features compared with the first version, including interactive graphical network visualization of the crosstalk between a query gene set and an enriched pathway, and the addition of Reactome pathways.http://pathwax.sbc.su.se.PathwAX II is available at Bioinformatics online. Advances in modern molecular biology have led to a multitude of high throughput techniques to discover altered expression levels between different conditions, for example, healthy and diseased cells. The resulting list of significantly altered genes usually gives few biological insights on its own, and therefore researchers often move on to determine which pathways are activated (In contrast to overlap-based methods, state-of-the-art network-based methods have the ability to find significant associations between gene sets and pathways even in the absence of shared genes. This is achieved by evaluating the enrichment of the interactions, crosstalk, between a pathway and a gene set within a biological network like FunCoup (To counter this issue, we developed PathwAX (Here, we present PathwAX version II. In addition to the version I functionality, it is equipped with a new interactive graphical network viewer for visualizing details of the connections between the query and pathway genes, which allows the user to inspect which genes and connections that underlie the observed crosstalk. This can for instance be used to see if the crosstalk is spread out between many genes or dominated by a small number of hubs, or to identify which pathway genes are connected to the largest number of query genes. It also features updated networks and KEGG pathways, and the addition of Reactome pathways (See -8, which has been shown to impact mesothelioma survival (We illustrate the usage of PathwAX II by annotating the gene list LOPEZ_MESOTHELIOMA_SURVIVAL_WORST_VS_BEST_UP . Compare-7. The glucose transporter GLUT4 has been shown to be transcriptionally repressed by the p53 tumor suppressor, which is an important protein in cell cycle control and apoptosis and has been linked to PI3K-Akt signaling pathway in cancer (-6. Platelets are important for angiogenesis, inflammation, wound healing, hemostasis and cancer formation. Cancer cells with access to the bloodstream trigger platelet-mediated recognition, which leads to a higher survival and spread of cancer cells (Using the Reactome database which was added in PathwAX II, it identifies 35 enriched and six depleted pathways at a significance threshold of FWER < 0.05. None of these pathways would have been detected using conventional overlap-based methods such as DAVID (-3, is linked to regulation of cell proliferation, differentiation, migration and apoptosis (A key pathway in cancer, MAP2K and MAPK activation, found by PathwAX II at FWER = 1.57×10poptosis  or grid poptosis  that botThe data underlying this article are available in the FunCoup, KEGG, and Reactome databases.btac153_Supplementary_DataClick here for additional data file.


Ocimum basilicum L. (OBL) is mainly used to treat neurological diseases in China. The preliminary work of this group showed that OBL improves cognitive impairment in Alzheimer's disease (AD). However, the underlying pharmacological mechanism remains unclear. in vivo experiment was conducted to verify the findings of network pharmacology.  The components of OBL were compiled by literature search, and their active ingredients were screened by online database. The drug targets of OBL in the treatment of AD were predicted and analyzed using information derived from sources such as the SwissTargetPrediction tool. And through the network visual analysis function of Cytoscape software and protein-protein interaction analysis (PPI), the core targets of OBL treatment of AD are predicted. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were employed to analyze the related signaling pathways affected by OBL. Moreover, AutoDock software was used to assess the potential binding affinity between the core targets and the active compounds. Subsequently, β25-35-induced injury HT22 cell cultures, and it can be speculated that DBP has the ability to protect the stability of injured neuronal cells and improve the permeability of cell membranes, thus stabilizing the intracellular environment. Mechanistically, DBP may increase the mRNA levels of AKT, GSK-3β, etc. in AD cell models and regulate the phosphorylation of AKT/GSK-3β pathway-related.  A total of 35 active compounds and 188 targets of OBL were screened, of which 43 common targets were related to AD. The active compounds of 35 OBLs induced 118 GO and 78 KEGG. The results of PPI and network topology parameter analysis show that targets such as MAPK1, GSK3B, NR3C2, ESR1, and EGFR are known as the core targets for the treatment of AD by OBL and are docked with the active ingredients of OBL. Molecular docking results suggest that diterbutyl phthalate (DBP) may be the main active component of OBL for the treatment of AD. Flow cytometry analysis results showed that apoptosis decreased with increasing DBP dose. In addition, DBP significantly decreased the levels of lactate dehydrogenase (LDH) and reactive oxygen species (ROS) in the supernatant of A Conclusively, our study suggests that DBP, the main active component of OBL, has potential in the prevention or treatment of AD. Alzheimer's disease (AD) is a neurodegenerative disease characterized by amyloid protein (A) deposition and neurogenic fiber tangles . Data shOcimum basilicum L. (OBL), a genus of basil in the family Labiatae, has pharmacological effects such as free radical scavenging, anti-inflammatory, antitumor, antibacterial, hypolipidemic, and antiatherosclerotic [clerotic . OBL hasclerotic . Studiesclerotic . OBL ameclerotic . The comIn vitro experiments showed the effect of DBP on HT22 cells with Aβ25-35-induced damage. The material basis of the action of OBL and its mechanism of action was investigated to provide some theoretical basis for the treatment of clinical AD at a later stage, see In this study, we investigated the active components, potential targets, and signaling pathways of OBL for the treatment of AD through network pharmacology, which is consistent with the therapeutic principles of Chinese medicine and ethnic medicine for the treatment of complex diseases and then searched for the best match between OBL small molecule compounds and target proteins by complementing the receptor active sites of AD disease targets with spatial structure and minimizing the binding energy. The monomeric component diterbutyl phthalate (DBP) was screened from OBL. https://www.chemicalbook.com/) to retrieve normalized 3D molecular structure descriptors for subsequent data analysis.The OBL chemical components were collected according to the following criteria: (1) regional OBL component that has been publicly reported in the literature within the last 5 years and (2) chemical components that have been quantified in OBL. The collected components were preprocessed and standardized to remove outlier samples and redundant molecular descriptions, and finally, the component names were entered into the PubChem database (http://www.swissadme.ch/index.php) to derive pharmacokinetic parameters related to the chemical composition, and the biologically active components were selected for further study based on ADME parameters. In SwissADME, the high gastrointestinal absorption is indicated by the OB. The high OB value indicates the important index of pharmacodynamic molecules and drug-like properties. In this paper, high gastrointestinal absorption and bioavailability scores 0.55 were used as criteria for screening. The higher OB value is a key index indicating the potent molecules and drug-like properties. Pharmacodynamic effects can influence the ADME process and therefore lead to changes in drug bioavailability [The OBL-normalized 3D molecular structure descriptors were compiled according to the above criteria and entered into the SIB database (lability .http://www.tcmip.cn/TCMIP/index.php) of herbal ingredients for searching to obtain the level of drug-likeness weight [http://www.swisstargetprediction.ch/) database for their targets of action.To estimate the drug similarity of each ingredient, pharmacokinetic parameters were calculated based on the model in the Pipeline Pilot ADMET collection. The obtained ingredients' English names were imported into the TCMIP V2.0 database (s weight . A quanthttp://www.disgenet.org/), CTD (http://ctdbase.org/), TTD (https://db.idrblab.org/ttd/), and DrugBank (https://www.drugbank.ca), and eliminated repeated targets to obtain known targets for the pathogenesis of AD.Using “Alzheimer disease” as a keyword, we searched and screened known disease targets in four major disease-related databases, including DisGeNET  association network was constructed, and isolated nodes were deleted to obtain the initial network. The CytoNCA (2.1.6) plug-in was then used to determine the betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), degree centrality (DC), local average connectivity-based method (LAC), network centrality (NC), and other topological attributes as criteria for 2-step screening to simplify the network, screening different target clusters, and the top-ranked target clusters as key target clusters for OBL treatment of AD.https://cytoscape.org).In order to systematically elucidate the role of OBL in the treatment of AD, the intersecting targets of OBL for AD were subjected to GO (Gene Ontology) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway enrichment analysis. The active component-target-pathway network map was constructed by Cytoscape 3.7.1 software , the structure was converted to PDB format. The structure is filled with AutoDock Tools 1.5.6, atomic charges are added, atomic types are assigned, and all flexible bonds are rotatable by default and saved in pdbqt format as docking ligands.The structures of the active ingredients obtained The crystal structure proteins of the core targets of OBL therapeutic AD collected http://vina.scripps.edu/index.html) for small molecules. Finally, the dominant conformation was taken for analysis and plotted using Maestro (Schrodinger) (https://www.schrodinger.com) software.The above pretreated ligands and receptors were docked to the proteins using AutoDock Vina ; benchtop low-speed centrifuge ; PCR instrument ; and enzyme marker .DBP (Ref. D806672: Maclean's); Reagents include fetal bovine serum (Ref. FND500: Excell Bio) and DMEM high sugar medium (Ref. C11965500BT: GIBCO). Instruments include the following: biological safety cabinet ; COβ25-35-inhibited HT22 cell survived in a concentration-dependent manner, with an IC50 of 173.568 μm [4 cells/mL single cell suspension with complete medium, inoculated into 96-well plates , incubated for 24 h at 37°C with 5% CO2 for wall attachment, the medium was discarded, and 100 μL of Aβ25-35 at a final concentration of 173.568 μmol/L (IC50) was added separately. L (IC50) of Aβ25-35 and different concentrations of DBP  were added, while a blank control group was set up. After 48 h of intervention, the supernatant was collected for LDH assay, while 100 μL of the configured 10% CCK-8 solution was added to each well, and the incubation was continued in the incubator for 1 h. After 1 h, the OD value at 450 nm was measured by enzyme marker. OD value at 450 nm was measured by ELISA after 1 h. The results of CCK-8 and LDH assay were combined to screen the low, medium, and high intervention concentrations of DBP for subsequent experiments.A previous study by our group showed that A3.568 μm . HT22 ce4 cells/mL single cell suspension with complete medium, inoculated into 96-well plates  and 6-well plates , incubated at 37°C and 5% CO2 for 24 h for wall attachment, and discarded medium, and the interventions were performed according to experimental groups, with 3 replicates per group. After the intervention was completed, the medium was discarded and the cells were collected for LDH and ROS detection .HT22 cells in good growth state were taken, cells were digested with trypsin, prepared into 5 × 104 cells/mL single cell suspension with complete medium, inoculated into 25 cm2 culture flasks, and incubated in a 37°C, saturated humidity, 5% CO2 cell incubator for 24 h. After the intervention according μL of precooled PBS, add the cell suspension to 3.5 mL of precooled 80% ethanol, and fix overnight at 4°C. Centrifuge at 2000 rpm for 5 min to precipitate the cells. Carefully aspirate the supernatant to avoid aspirating the cells. Wash 2 times with precooled PBS and discard the clean supernatant. Add 500 μL PI/RNase Staining Buffer to resuspend the cells and pass through a 200 mesh nylon sieve to make a single cell suspension, incubate for 30 min at 4°C, protected from light, detect red fluorescence at an excitation wavelength of 488 nm with a flow cytometer, and detect light scattering. Analysis software was used for cellular DNA content analysis and light scattering.HT22 cells in good growth condition were digested with trypsin, prepared into 5 × 104 cells/mL single cell suspension with complete medium, inoculated into 25 cm2 culture flask, and incubated in 37°C, saturated humidity, 5% CO2 cell culture chamber for 24 h. After the intervention according to the experimental grouping μL of 1 × Binding Buffer to resuspend the cells and pass through a 200 mesh sieve to make a single cell suspension. Add 5 μL Annexin V-PE and 10 μL 7-AAD to each tube, mix gently, and leave for 5 min at 4°C protected from light. Flow cytometry assay was performed within 30 minutes.HT22 cells in good growth condition were digested with trypsin, prepared into 5 × 10μmol/L of Aβ25-35 intervened in HT22 cells for 48 h); positive control group (173.568 μmol/L of Aβ25-35 and 0.5 μmol/L of SB216763 together intervened in HT22 cells for 48 h); low-dose group of DBP (173.568 μmol/L of Aβ25-35 and 50 μmol/L DBP cointervened in HT22 cells for 48 h); DBP medium-dose group (173.568 μmol/L of Aβ25-35 and 100 μmol/L DBP cointervened in HT22 cells for 48 h); and DBP high-dose group (173.568 μmol/L of Aβ25-35 and 150 μmol/L DBP cointervened HT22 cells for 48 h).The experimental groupings are as follows: model control group  and the targets (blue circles) represent interactions.“minimum required interaction score” ≥ 0.7. The 43 drug-disease targets were filtered by STRING data with the criteria of “Homo sapiens” and “minimum required interaction score” ≥ 0.7 to obtain 37 candidate targets, and the interaction relationship between the targets is shown in The TTD, CTD, DisGeNET, and DrugBank databases were searched for targets related to AD pathogenesis to obtain 155, 298, 252, and 45 disease targets, respectively, and intersected with 188 OBL targets to obtain 43 shared targets, see The results of GO gene function analysis of 43 common targets of OBL for AD treatment by the DAVID database showed that 289 entries were obtained, and the top 45 results were ranked in accordance with the corrected FDR pairs.G-protein-coupled acetylcholine receptor activity, enzyme binding, G-protein-coupled serotonin receptor activity, RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding, neurotransmitter receptor activity, identical protein binding, alpha1-adrenergic receptor activity, steroid binding, sequence-specific DNA binding, protein serine/threonine/tyrosine kinase activity, zinc ion binding, oxidoreductase activity, acting on single donors with incorporation of molecular oxygen, incorporation of two atoms of oxygen, virus receptor activity, protein kinase binding, and beta-amyloid binding are the first 15 biological functions induced by the 35 OBL components, see Cytosol, nucleoplasm, nuclear chromatin, receptor complex, perinuclear region of cytoplasm, cytoplasm, cyclin-dependent protein kinase holoenzyme complex, transcription factor complex, plasma membrane, caveola, plasma membrane, integral component of plasma membrane, integral component of presynaptic membrane, dendrite, synapse, axon, postsynaptic membrane, macromolecular complex, receptor complex, glutamatergic synapse, integral component of postsynaptic membrane, membrane, nucleus, and mitochondrion were ranked among the top 15 cell components, see The first 15 biological processes are adenylate cyclase-inhibiting G-protein-coupled acetylcholine receptor signaling pathway, G-protein-coupled serotonin receptor signaling pathway, signal transduction, positive regulation of vasoconstriction, G-protein-coupled acetylcholine receptor signaling pathway, peptidyl-tyrosine autophosphorylation, aging, G-protein-coupled receptor signaling pathway, coupled to cyclic nucleotide second messenger, response to xenobiotic stimulus, cellular response to reactive oxygen species, memory, intracellular steroid hormone receptor signaling pathway, cellular response to estradiol stimulus, adenylate cyclase-activating adrenergic receptor signaling pathway, and phospholipase C-activating G-protein-coupled receptor signaling pathway, see Forty-three targets were imported into the DAVID database to analyze KEGG signaling pathways, and the pathways were further screened according to FDR size, with the FDR value indicating that the smaller the value in the enrichment analysis, the higher the enrichment significance. A total of 83 signaling pathways were collected and sorted according to FDR ≤ 0.05, and the top 15 results are shown in Fifteen signaling pathway connecting targets and their acting OBL candidate components were used to construct a component-target-pathway (C-T-P) network map by Cytoscape software, which includes 75 nodes  and 240 edges. The edges between components (red octagons), targets (blue circles), and signaling pathways (green triangles) represent interactions, see To further determine the accuracy of the prediction results, molecular docking of the core targets to the candidate components was performed. The core targets EGFR (PDBID=5GTY), ESR1 (PDBID=4XI3), MAPK1 (PDBID=1TVO), GSK3B (PDBID=3mvh), and NR3C2 (PDBID=3VHU) and 35 candidate components were molecularly docked.The magnitude of the binding energy (BE) is used to determine how well the components of OBL match the core target. When the conformation of ligand and receptor is stable, the lower the energy, the higher the possibility of action. Generally, BE ≤ −4.25 Kcal/mol indicates that the active ingredient has a certain binding energy to the target, BE ≤ −5.00 Kcal/mol indicates that the active ingredient has good binding energy to the target, and BE ≤ −7.00 Kcal/mol indicates that the active ingredient has strong binding energy. In this paper, using BE ≤ −4.25 Kcal/mol as the criterion, 15 of the 35 candidate components had some binding activity with 5 core targets, as shown in π-interactions with LYS745 by patterning hydrophobic interactions with adjacent residues LEU428, TRP383, LEU384, LEU387, MET388, PHE404, LEU391, ARG394, GLU353, ALA350, LEU349, THR347, LEU346, MET343, MET528, LEU525, and VAL534 interactions, forming π-interactions with PHE404 and forming hydrogen bonds with ARG394, thus binding to ESR1.Quercetin can bind to GSK3B by patterning hydrophobic interactions with adjacent residues ASP292, THR291, THR211, LYS179, ALA177, MET227, GLU228, TYR229, ALA230, PHE438, MET281, LEU156, GLY157, GLY159, VAL164, GLU234, and ARG4, form hydrophobic interactions, and form hydrogen bonds with ALA230, thus binding to GSK3B. Quercetin binds to MAPK1 . Quercetin can be activated at the active sites of the adjacent residues LEU844, THR854, ASP855, MET766, LEU777, LEU788, and ILE789. Quercetin can bind to EGFR by patterning hydrophobic interactions with adjacent residues LEU844, THR854, ASP855, MET766, LEU777, LEU788, ILE789, THR790, LEU792, MET793, GLY796, CYS797, LEU718, LEU1001, PHE997, VAL726, LYS745, ILE744, and ALA743 and Kaempferol can bind to EGFR by patterning hydrophobic interactions with adjacent residues ARG841, LEU844, THR790, ASP855, THR854, LYS745, ALA743, CYS797, GLY796, LEU718, GLY719, SER720, GLY721, GLY724, THR725, and VAL726. Kaempferol binds to ESR1 . Kaempferol can be used as the active site of the residues MET281, THR291, ASP292, VAL164, LYS163, GLY162, GLY159, LYS158, GLY157, LEU156, ARG4, PHE442, PHE438, GLU234, LYS179, ILE180, and LEU181 forming hydrophobic interactions and thus binding to GSK3B. Kaempferol can bind to GSK3B by patterning hydrophobic interactions with adjacent residues LEU814, SER811, LEU810, MET852, LEU938, MET807, PHE941, CYS942, PHE956, THR945, LEU766, LEU769, MET845, ASN770, PHE829, LEU772, ALA773, and GLN776 forming hydrophobic interactions and thus binding to NR3C2. Kaempferol can bind to NR3C2 by forming hydrophobic interactions with adjacent residues MET108, ASP111, LEU156, ASN154, LYS114, SER153, ILE31, LYS151, GLU33, GLY34, CYS166, ASP167, YS54, GLY37, and VAL39 and thus binds to MAPK1 by patterning hydrophobic interactions with adjacent residues MET108, ASP154, LYS114, SER153, ILE31, LYS151, GLU33, GLY34, CYS166, ASP167, YS54, GLY37, and VAL39.DBP binds to GSK3B  active sites, and hydrogen bonds are formed with THR291, further improving the interaction between the ligand and GSK3B protein.β aggregation [Among the above active ingredients, Quercetin and Kaempferol have been reported in the treatment of AD. For example, Quercetin can improve cholinergic function and play a neuroprotective role in AD. The neuroprotective effects of Quercetin have multiple mechanisms, including inhibition of Aregation , inhibitregation , and aceregation  to reducregation . It playregation .β (GSK-3B) is a key factor of the signal transduction pathway during oxidative stress in AD neurons [β in AD patients has neuronal toxicity and induces oxidative stress in neurons [β is able to decrease AKT activity, increase GSK-3β activity, and inhibit AKT/GSK-3-related signaling pathways [β can be effectively inhibited, it can help alleviate the symptoms of AD patients.It was reported  that DBP neurons . Aβ in A neurons . The dowpathways . It can β include Thiazolidinones (TZD), Bis-indole [β, the next step is to conduct in vitro experiments to verify it, see Researchers have shown that inhibitors of GSK-3s-indole , Anilines-indole , Maleimis-indole , and Inds-indole , while ls-indole . DBP hass-indole . Whetherβ25-35-induced injury in the HT22 AD cell model of hippocampal neuronal cells in intervening mice. Compared with the model group, the OD values of DBP 50, 100, and 150 μmol/L increased significantly with the concentration gradient, and 150 μmol/L increased more significantly with statistically significant differences (P < 0.01). Therefore, 50, 100, and 150 μmol/L were chosen for the next experiment. As shown in β25-35-induced injury HT22 cells, the morphological observation showed that DBP could reduce the damage of Aβ25-35-induced injury HT22 cells.The OD values increased after different concentrations of DBP intervention in Aβ25-35-induced injury, the LDH content in the cell supernatant was measured. The results showed a statistically significant decrease in LDL content with concentration gradient in the DBP 50, 100, and 150 μmol/L groups compared with the model group (P < 0.01). In addition, the cell survival rate was significantly reduced after Aβ25-35 intervention in mouse hippocampal neuronal cells HT22. Based on the experimental results, 50, 100 μmol/L, and 150 μmol/L were selected as the low, medium, and high concentrations of DBP for subsequent experiments, see To further determine the effect of DBP on the activity of HT22 cells with AP < 0.01). Compared with the model group, the LDH content in the cell cultures of the DBP low-, medium-, and high-dose groups was significantly lower than that of the model group, with a statistically significant difference compared with the model group (P < 0.01).As shown in P < 0.01). Compared with the model group, the fluorescence intensity of ROS in the cell culture fluid of DBP low-, medium-, and high-dose groups was significantly reduced, and the difference was more significantly reduced in the high-dose group, and the difference was statistically significant (P < 0.01). This indicates that DBP can reduce oxidative stress and improve the viability of HT22 cells.As shown in β25-35-induced injury to HT22 cells showed that, compared with the model group, the Aβ25-35-induced injury to HT22AD cell model group showed a gradual decrease in the effect on the G0/G1 phase of the cell cycle with increasing DBP dose, a gradual increase in the effect on the S phase, and almost no effect on the G2/M phase.The results are shown in β25-30 after Aβ25-35 induced HT22 cell injury . After DBP intervention in the low-, medium-, and high-dose groups compared with the model group, Aβ25-35 produced damage to HT22 cells, and the apoptosis rate of damage gradually decreased with the increase of concentration. Among them, DBP was more obvious at medium and high doses, and the difference was statistically significant (P < 0.01) (Flow cytometry detected the apoptosis rate of A < 0.01) .β mRNA expression after different intervention groups are shown in β mRNA expression was increased in a concentration-dependent manner. Among them, compared with the model group, the AKT and GSK-3β mRNA expression levels of DBP low-, medium-, and high-dose groups increased with the gradient of DBP concentration, and the difference was not statistically significant.The results of reverse transcription quantitative PCR of AKT and GSK-3β, and p-GSK-3β and internal reference protein β-actin are identified separately in β protein expression was significantly increased in a concentration-dependent manner in the DBP low-, medium-, and high-dose groups compared with the model group, and the expression levels were more pronounced in the high dose, with statistically significant differences (P < 0.05).The WB results for detection of target protein AKT, p-AKT, GSK-3Natural products have complex biological activities; their components are complex and diverse, and the composition of the formula is even more complex. In the field of TCM, natural products are commonly used in disease treatment, and their pathways and modes of action vary after entering the human body. They can act directly on specific targets, produce new products after metabolism, or act indirectly through the regulation of endogenous substances, exerting multicomponent, multitarget regulation. It is urgent to establish new research strategies and methods that can reflect the overall characteristics of TCM-ethnic medicine. In recent years, the rapid development of network pharmacology in TCM-ethnic medicine research has attracted attention, which integrates three aspects of TCM-ethnic medicine, including components, targets, and related diseases, and constructs a multidimensional network of “components-targets-pathways-diseases.” The active ingredients and mechanisms of action of TCM-ethnic medicine for diseases are then visualized and analyzed.Visual analysis of the PPI network and its CytoNCA network revealed multiple associations between targets, with higher connectivity values associated with greater potential therapeutic effects. The top-ranked target clusters of MAPK1, EGFR, NR3C2, ESR1, and GSK3B may be key targets. In previous studies, MAPK1 has been associated in previous studies with neurodegeneration, synaptic plasticity, cell survival, and a role in autophagic vesicle formation in AD , 38. Theβ toxicity, and neuroinflammation [EGFR is a transmembrane receptor with tyrosine kinase activity and is an important member of the ErbB family that is involved in regulating brain development, neuronal survival, and functional regulation, among other activities. Many neurodegenerative diseases include AD . High leammation .NR3C2 is an important gene involved in the stress response, and its gene product, salt corticosteroid receptor, is mainly distributed in the hippocampus and amygdala regions involved in the regulation of tension and anxiety and is closely related to tension and anxiety generation and regulation and cognitive function . miR-135Estrogen can cross the blood-brain barrier to act in the brain , and theβ signaling pathway through ER and has a protective effect against Aβ25–35-induced damage in PC12 cells [β signaling pathways [KEGG enrichment pathway analysis revealed that neuroactive ligand-receptor interaction , PI3K-AK12 cells . Quercetpathways . Kaempfepathways .β25–35-induced damage, and the results indicated that low doses of DBP had a protective effect on HT22 cells with Aβ25–35-induced damage. The present study showed that DBP significantly reduced the LDH and ROS content in the supernatant of Aβ25-35-induced injury HT22 cell cultures, and DBP was also able to reduce the apoptosis rate of Aβ25-35-induced injury HT22 cells. Thus, it can be speculated from the results of this study that DBP has the ability to protect the stability of injured neuronal cells and improve the permeability of the cell membrane, thus stabilizing the intracellular environment. This effect may be related to the fact that DBP increases the mRNA levels of AKT, GSK-3β, etc. in AD cell models and regulates the phosphorylation of the AKT/GSK-3β pathway.The results of the present study showed that DBP was able to reduce the rate of inhibition of HT22 cells with Ain vitro experimental methods. This paper provides a new idea for the treatment of AD with complex pathogenesis and also lays the foundation for the in-depth study of the synergistic mechanism of OBL.In summary, OBL has been used to explain the relationship between OBL active ingredients, potential targets, signaling pathways, and the pathogenesis of AD disease at a holistic level through network pharmacology technology and to verify the pharmacodynamic and regulatory mechanisms of OBL main active ingredients through


Chaihu-Shugan-San (CHSGS), a noted traditional Chinese medicine formula, has been used as a complementary and alternative therapy for liver fibrosis. However, the antifibrotic mechanisms of CHSGS still remain unclear. Thus, we used network pharmacology approach in combination with single cell and bulk transcriptomics to elucidate the antifibrotic mechanisms of CHSGS. We first screened out 134 bioactive ingredients of CHSGS through the defined criteria. Then, 1150 genes were predicted to be targets for CHSGS, while 625 liver fibrosis-associated genes were identified by single cell transcriptomics analysis. Next, 71 intersecting genes of CHSGS and liver fibrosis were defined as the therapeutic targets in CHSGS against liver fibrosis. Further, 21 core targets and 12 core ingredients of CHSGS against liver fibrosis were also identified. Meanwhile, enrichment analyses of core targets highlighted that the key mechanisms of CHSGS against liver fibrosis include modulation of inflammation responses, inhibition of angiogenesis, and regulation of ECM remodeling, of which the most important mechanism was the regulation of ECM remodeling. The molecular docking simulation validated strong binding affinity between the core targets and core ingredients. Furthermore, 62-gene signature may be used for determining the prognosis in cirrhotic patients based on the results of ssGSEA-Cox analysis. In conclusion, the present study revealed the multiple pharmacological targets and therapeutic mechanisms of CHSGS against liver fibrosis, which may thus serve as an effective antifibrotic therapy. Meanwhile, CHSGS may improve survival of patients with liver cirrhosis by the interaction of 62-gene signature. Liver fibrosis is a fibrous scar formed by the excessive deposition of extracellular matrix (ECM) proteins, which mainly includes collagens type I and type III ,2. It caBupleurum chinense DC. , Citrus aurantium L. , Ligusticum striatum DC. , Citrus reticulata Blanco , Cyperus rotundus L. , Paeonia lactiflora Pall. , and Glycyrrhiza uralensis Fisch. . It has been widely used as a common TCM formula for the treatment of liver diseases in China. CHSGS can alleviate chronic inflammation in nonalcoholic fatty liver rats through the NLRP3 inflammasome pathway , which includes five cirrhotic and five healthy human livers , and theElbowPlot. The first 12 principal components were opted as input for shared nearest neighbour graph-based clustering; the clustering resolution was set to 0.4 based on the function clustree. With the above parameters, 76,467 cells were classified into 17 initial clusters using the Seurat function FindClusters. Further, the cells clustering results visualization were constructed using uniform manifold approximation and projection with the same 12 principal components as the related clustering. In order to identify the genes associated with liver fibrosis, DEG analysis was performed in the Seurat function FindMarkers using Wilcoxon rank sum test. Only genes expressed in at least 25% of the compared cells and with a log fold-change of at least 0.7 were retained.Next, we conducted principal component analysis using the integrated variable genes for dimension reduction and estimated significant principal components based on the Seurat function P method (iLOGP) ≤5  with 216 liver cirrhosis patients . The surhttps://www.rcsb.org/) database and PubChem (https://pubchem.ncbi.nlm.nih.gov/) database, respectively. The initial preprocessing of removing water and original ligands of the core target proteins were performed by PyMol (Version 2.2.0) software. We then used MGLTools (Version 1.5.6) of Autodock software to add hydrogen and get PDBQT format files of the core macromolecules and ligands for further docking. Meanwhile, the docking active center parameters, wrapped the macromolecules, were set by the grid box function in the MGLTools. Finally, virtual docking was performed with the above parameters by using Autodock Vina (Version1.1.2) software [Molecular docking was applied for analyzing the binding and interaction situation of core ingredients and core target proteins in CHSGS against liver fibrosis. The three-dimensional (3D) structure of the core target proteins and molecular structure of the core ingredients were obtained from the PDB . Among the 134 bioactive ingredients, five ingredients were shared by two or more Chinese herbs, including kaempferol (MOL000422), quercetin (MOL000098), isorhamnetin (MOL000354), naringenin (MOL004328), and nobiletin (MOL005828). In addition, CH, ZQ, CX, CP, XF, BS, and GC Chinese herbs had 12, 3, 4, 3, 9, 16, and 82 specific bioactive ingredients, respectively . It indiWith the target predicting methods, a total of 1150 genes were predicted to be targets for CHSGS. Meanwhile, the ingredient-target network was established by Cytoscape software with 134 bioactive ingredients and 1150 target genes. Almost all bioactive ingredients had multiple targets that lead to a complex network with 10,854 interactions between ingredients and targets (Supplementary Table S4). In the ingredient-target network, there were 40 target genes per ingredients on average. Quercetin (MOL000098) had the majority of targets, with a frequency of 231. Further, the 71 intersection genes in CHSGS and liver fibrosis were used to construct the herb-ingredient-target-disease network, which was composed of 213 nodes and 848 edges . With thIn our research, 71 overlapping genes of CHSGS against liver fibrosis were identified by the intersection of 1150 CHSGS-pharmacological targets with 625 liver fibrosis-associated genes, which were queried in STRING online tool to construct the PPI network . After hP<0.05), including TIMP1, IGFBP5, ALB, HSP90AA1, CXCL2, TTR, MYC, NTRK2, CXCR4, PABPC1, and SLC29A1 (P<0.001)  . SimilarP<0.001) C. MoreovP=0.037; D.In order to analyze the potential binding of core ingredients and core targets in CHSGS against liver fibrosis, molecular docking analysis was performed with MGLTools of Autodock Vina. Except SPP1, IRF1 and CAV1 without the 3D protein structure of Homo sapiens, the rest 18 core target protein structures were obtained from the PDB database and the PDB identifiers were shown in Supplementary Table S7. The molecule structures of 12 core ingredients were then collected from PubChem database. Further, the lowest binding energy of these small molecules and core proteins were calculated by Autodock Vina software. All the absolute value of the lowest binding energy of small molecule-protein were lower than −5 Kcal/mol, indicating that the core target and core pharmacological molecules have strong binding affinity  can inhibit vast majority of matrix metalloproteinases, which is the only enzyme that can break down the fibrous collagen . COL1A1 Taken together, the multi-omics and network pharmacology findings highlight that the key mechanisms of CHSGS against liver fibrosis include modulation of inflammation responses, inhibition of angiogenesis, and regulation of ECM remodeling, of which the most important mechanism was the regulation of ECM remodeling. In addition, 62-gene signature may be used for determining the prognosis in cirrhotic patients based on the results of ssGSEA-Cox analysis. Moreover, the potential pharmacological ingredients and targets of CHSGS against liver fibrosis were identified, indicating that CHSGS may serve as an effective antifibrotic therapy.Click here for additional data file.


Background and objective: Lonicera japonica Flos (LJF) is a well-known traditional herbal medicine that has been used as an anti-inflammatory, antibacterial, antiviral, and antipyretic agent. The potent anti-inflammatory and other ethnopharmacological uses of LJF make it a potential medicine for the treatment of nonalcoholic fatty liver disease (NAFLD). This research is to explore the mechanisms involved in the activity of LJF against NAFLD using network integration and experimental pharmacology. Materials and methods: The possible targets of LJF involved in its activity against NAFLD were predicted by matching the targets of the active components in LJF with those targets involved in NAFLD. The analysis of the enrichment of GO functional annotations and KEGG pathways using Metascape, followed by constructing the network of active components–targets–pathways using Cytoscape, were carried out to predict the targets. Molecular docking studies were performed to further support the involvement of these targets in the activity of LJF against NAFLD. The shortlisted targets were confirmed via in vitro studies in an NAFLD cell model. Results: A total of 17 active components in LJF and 29 targets related to NAFLD were predicted by network pharmacology. Molecular docking studies of the main components and the key targets showed that isochlorogenic acid B can stably bind to TNF-α and CASP3. In vitro studies have shown that LJF down-regulated the TNF-α and CASP3 expression in an NAFLD cell model. Conclusions: These results provide scientific evidence for further investigations into the role of LJF in the treatment of NAFLD. Nonalcoholic fatty liver disease (NAFLD) is defined as a condition in which fat accumulates in the liver instead of being caused by excessive alcohol consumption . AccordiLonicera japonica Flos (LJF), a member of the Caprifoliaceae family, has a long history of use by humans as a functional food and a TCM [nd a TCM . It is mnd a TCM . The potnd a TCM . Anothernd a TCM ,12. BesiIn recent years, new technologies including network integration pharmacology have been developed to determine the activity and mechanisms of TCM. With this multidisciplinary research, it is possible to predict the active components of TCM and their potential biological targets. Additionally, the mechanisms of action of TCM and the interaction between the active compounds and biological targets can be explored by network pharmacology, which will be of great help in elucidating the pharmacological characteristics of TCM ,14.In this study, we have used the multiprong approach, which combines network integration, molecular docking studies, and in vitro studies to identify the bioactive compounds in LJF and their potential biological targets, signaling pathways, and molecular mechanisms in treating NAFLD.Li et al. has performed metabolomic studies on LJF in healthy rats and identified 25 compounds, out of which 15 are LJF compounds and the remaining 10 are their metabolites ,17.https://pubchem.ncbi.nlm.nih.gov/, accessed on 12 March 2020) database to convert all compounds into the standard Canonical SMILES format and obtained the 2D structure of compounds. Then, we imported the SMILES format file into Swiss Target Prediction  platform to get potential targets in humans (Homo sapiens) [We used the PubChem (sapiens) .http://www.omim.org/, accessed on 13 March 2020), GeneCards  and NCBI gene  [The genes involved in NAFLD were collected from three disease target databases; OMIM ch 2020) ,20. The 20 ,20. Thttps://string-db.org/, accessed on 14 March 2020) and selected the race as “Home sapiens” to obtain PPI network.We uploaded the potential targets of LJF against NAFLD to the large-scale STRING protein database  is a gene annotation analysis database for enrichment analysis of GO annotation and KEGG pathways of input genes. The analysis is also useful to make new predictions based on the information in the existing databases [p-value < 0.01. Then GO and KEGG pathway enrichment analysis of potential targets were performed. The top ranked GO annotation and KEGG pathways were screened according to the number of targets contained in each entry, and the results were visualized using GraphPad Prism 7.0 software .The Metascape platform  was used to obtain the 3D structures of the target receptor. The molecular docking process, including ligand and receptor preparation, was done using Surflex-Dock module of the Sybyl-X 2.1.1 program  and ranked based on Surflex-Dock score  [To validate the above results, molecular docking studies were carried out to calculate the binding affinity of the active ingredients toward the key hubs in the network. The active components with most targets and the proteins with high-degree nodes in the gene network were chosen to perform molecular docking studies. The 3-dimensional (3D) structure of ligand was downloaded from the PubChem database. A database (l score) . The grav/v) ethanol (1: 10 ratio) under reflux for 1 h and filtered. The filtered residue was again extracted under reflux with 65% v/v ethanol for 0.5 h and filtered. The filtrates were combined, concentrated under vacuum to distill off ethanol, and then lyophilized. The main 12 components of LJF extract were determined using high-performance liquid chromatography (HPLC) (LJF (20210710) was purchased from Pingyi county of Shandong province and was authenticated by Yong-Qing Zhang of Shandong University of Traditional Chinese Medicine. The material was powdered, passed through a 60-mesh sieve, and extracted with 65% (y (HPLC) . The com2) with DMEM complete medium containing 10% fetal bovine serum, 100 μg·mL−1 penicillin, and 100 μg·mL−1 streptomycin. HepG2 cells in the logarithmic growth phase were chosen for the following experiments. HepG2 cells were seeded in a 96-well plate at a cell density of 2 × 104 cells/mL and cultured in an incubator for 12 h. HepG2 cells were divided into three groups: (1) control group in which cells were treated with DMEM complete medium, (2) NAFLD model group in which cells were treated with a complete medium containing 200 μmol·L−1 sodium oleate, and (3) LJF group in which cells were treated with a complete medium containing 200 μmol·L−1 sodium oleate and 25 mg/L LJF extract. The three cell groups  were stained with oil red O stain to investigate the accumulation of lipids.HepG2 cells were cultured in the incubator  were cultured for 24 h, the cells were collected and homogenized. The alanine transaminase (ALT) and aspartate aminotransferase (AST) content in HepG2 cells was determined using Alanine Transaminase Activity Assay Kit and Aspartate Aminotransferase Activity Assay Kit , respectively.6 cells/mL. After 24 h, the RNA was extracted with a total RNA extraction kit. The purity of mRNA was determined using electrophoresis using agarose gel and TAE buffer. The mRNA was reverse-transcribed to cDNA using the Hiscript II 1st Strand cDNA Synthesis Kit. Finally, QuantiNovaTM SYBR Green PCR Kit was used for quantitative estimation of the genes. The primer sequences of TNF-α and CASP3 used in this study are shown in The qRT-PCR assay was used to detect tumor necrosis factor (TNF-α) and caspase 3 (CASP3) mRNA levels in three cell groups . The HepG2 cells from three groups were seeded in a 60 mm diameter Petri dish at a cell density of 2 × 104 cells/mL and cultured for 24 h. Then, the culture medium was discarded, and 3.7% formaldehyde was added to fix the cells for 20 min. Then, the formaldehyde was replaced with 0.1% Triton, and the plate was shaken slowly for 5 min to permeabilize the cells. The above step was repeated five times. Skimmed milk (5%) was added to the wells and incubated for 1.5 h, followed by incubation with the primary antibody diluted with 5% skimmed milk  at 4 °C for 16 h. Then, the cells were incubated with 5% fluorescent secondary antibody  diluted with skim milk at 37 °C for 1 h in the dark. Imaging and analysis were performed under the ODYSSEY CLx dual infrared laser imaging system.Western blot (WB) assay was used to detect TNF-α and CASP3 protein levels in all three cell groups . Cells in three groups were seeded in a 96-well plate at a cell density of 2 × 10p < 0.05 is considered statistically significant, and p < 0.01 is considered very significant.SPSS 26.0 software was used for statistical analysis of the data, and the data were expressed as mean ± SD. One-way analysis of variance was used to compare the statistical differences between the groups. We have selected 17 compounds with Oral Bioavailability (OB) ≥30% and Drug Likeness (DL) ≥0.18, via the study of Li et al. . The namThe Swiss Target Prediction platform was used to predict the potential targets of the active compounds, and a total of 142 potential target genes were identified after removal of the duplicates. By comparing these target genes with NAFLD-related genes from the OMIM, Gene Cards, and NCBI gene databases, via the Metascape platform, the intersection of the genes were shortlisted in To explore the possible mechanism of action of LJF in treating NAFLD, the 29 potential target proteins were input into the STRING database to construct a PPI network of target protein interactions . The netp- value < 0.01 and screened the top GO annotation and KEGG pathways. GraphPad Prism 7.0 was used to visualize the results   B. Dockinil 2020) C. The toil 2020) D. −1) to develop a lipid accumulation model, and oil red O staining was used to detect the lipid content. p < 0.01). Compared with the model group, the ALT and AST levels in the LJF group were significantly decreased (p < 0.05) (HepG2 cells were stimulated with sodium oleate (200 μmol·L < 0.05) B. p < 0.01) (p < 0.01), as shown in To further explore the mechanism of LJF in treating NAFLD, the real-time qPCR and Western blot methods were used to quantify the mRNA and protein expression of TNF-α and CAPS3. Compared with the control group, sodium oleate has significantly up-regulated the mRNA and protein expression of TNF-α and CAPS3 ( < 0.01) C–E. LJF TCM plays a unique and essential role in alternative medicine, the pharmaceutical industry, and in many other fields ,24. NoneThere are significant obstacles facing traditional research methods for TCM, including long cycles, low data flux, low precision, and high costs. Moreover, it is difficult to elucidate the pharmacological actions of TCM and their mechanisms by conventional research methods. The network pharmacology, target predictions, and molecular docking studies have provided new avenues of research for exploring the pharmacological actions and mechanisms of TCM . TCM hasIn this study, we have conducted an integrated approach, which combines network pharmacology and molecular docking approaches to predict 17 active ingredients in LJF that exhibit activity on 29 potential targets relevant to NAFLD. This approach can be considered as multi-component–multi-target–multi-pathway process to unveil the involvement of multiple proteins, multiple pathways, and multiple biological processes. In vitro studies revealed that LJF extract can downregulate TNF-α and CASP3 expressions in an NAFL cell model, which further confirmed the mechanism of LJF against NAFLD. A study has demonstrated that TNF-α plays an essential part in the development of NAFLD by upregulating the mediators of fibrosis and lipid metabolism and upregulating the inflammatory cytokines in the liver . CaspaseIGFBP-3 is reported to exhibit anti-inflammatory activity in hepatocytes by downregulating the production of proinflammatory cytokines through the NF-κB and JNK pathways. The secretion of IGFBP-3 is inhibited under lipotoxic conditions, boosting palmitate-induced IL-8 synthesis and secretion . Amyloid precursor protein/presenilin-1 (APP/PS1) transgenic mice fed with a cholate, cholesterol, and high-fat diet is reported to induce NAFLD without hyperglycemia and obesity. NAFLD leads to cognitive decline and cerebral hypoperfusion in APP/PS1 mice . A recenRecent study has shown that apoptosis is the principal mechanism contributing to hepatocellular death in NAFLD . AnotherIn this study, the network pharmacology was integrated with molecular docking studies to predict the underlying mechanism of LJF in the treatment of NAFLD. We have found that LJF can regulate the hepatocellular death and hepatocyte autophagy to protect the normal function of the liver in NAFLD. A total of 17 active components in LJF, and 29 targets related to NAFLD were predicted by network pharmacology. In total, 23 terms of biological process (BP), 20 entries of cellular component (CC), and 21 terms of molecular function (MF) were obtained by GO analysis, while 19 pathways associated with LJF against NAFLD were revealed by KEGG analysis. The molecular docking study of the main components and the key targets showed that isochlorogenic acid B can bind stably to TNF-α and CASP3. The in vitro studies have revealed that LJF extracts down-regulated TNF-α and CASP3 expression in an NAFLD cell model. Animal and clinical studies should be carried out to further confirm the mechanism of LJF.


This study aimed to explore the efficacy and safety of combining epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) with ZiLongJin Tablet (ZLJT) in delaying acquired resistance in advanced EGFR-mutant lung adenocarcinoma (LUAD) patients. Furthermore, we employed network pharmacology and molecular docking techniques to investigate the underlying mechanisms.n = 26) and resistant (n = 26) PC9 cell lines. The "limma" package in R software was employed to detect DEGs. Based on this, we constructed a protein‒protein interaction network, performed gene ontology and KEGG enrichment analyses, and conducted pathway network analysis to elucidate the correlation between the active ingredients in ZLJT and signaling pathways. Finally, molecular docking was performed using AutoDockVina, PYMOL 2.2.0, and Discovery Studio Client v19.1.0 software to simulate spatial and energy matching during the recognition process between predicted targets and their corresponding compounds.A retrospective comparative study was conducted on stage IIIc/IV LUAD patients treated with EGFR-TKIs alone or in combination with ZLJT at the Second Affiliated Hospital of the Air Force Medical University between January 1, 2017, and May 1, 2023. The study evaluated the onset of TKI resistance, adverse reaction rates, safety indicators , and inflammatory markers (neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio) to investigate the impact of EGFR-TKI combined with ZLJT on acquired resistance and prognostic indicators. Additionally, we utilized the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine, PubChem, UniProt, and Swiss Target Prediction databases to identify the active ingredients and targets of ZLJT. We obtained differentially expressed genes related to EGFR-TKI sensitivity and resistance from the Gene Expression Omnibus database using the GSE34228 dataset, which included sensitive (P < 0.01). Compared to the monotherapy group, the combination group showed a prolongation of 3.27 months in delayed acquired resistance. There was also a statistically significant difference in the onset of resistance to first-generation TKIs between the two groups (P < 0.05). (2) In terms of safety analysis, the incidence of adverse reactions related to EGFR-TKIs was 12.5% in the combination group and 14.3% in the monotherapy group, but this difference was not statistically significant (P > 0.05). There were no statistically significant differences in serum AST, ALT, CREA, TBIL, ALB and BUN levels between the two groups after medication (P > 0.05). (3) Regarding inflammatory markers, there were no statistically significant differences in the changes in neutrophil-to-lymphocyte Ratio(NLR) and Platelet-to-lymphocyte Ratio(PLR) values before and after treatment between the two groups (P > 0.05). (4) Network pharmacology analysis identified 112 active ingredients and 290 target genes for ZLJT. From the GEO database, 2035 differentially expressed genes related to resistant LUAD were selected, and 39 target genes were obtained by taking the intersection. A "ZLJT-compound-target-disease" network was successfully constructed using Cytoscape 3.7.0. GO enrichment analysis revealed that ZLJT mainly affected biological processes such as adenylate cyclase-modulating G protein-coupled receptor. In terms of cellular components, ZLJT was associated with the cell projection membrane. The molecular function primarily focused on protein heterodimerization activity. KEGG enrichment analysis indicated that ZLJT exerted its antitumor and anti-drug resistance effects through pathways such as the PI3K-Akt pathway. Molecular docking showed that luteolin had good binding activity with FOS (-9.8 kJ/mol), as did tanshinone IIA with FOS (-9.8 kJ/mol) and quercetin with FOS (-8.7 kJ/mol).(1) A total of 89 patients were included, with 40 patients in the EGFR-TKI combined with ZLJT group (combination group) and 49 patients in the EGFR-TKI alone group (monotherapy group). The baseline characteristics of the two groups were comparable. There was a significant difference in the onset of resistance between the combination group and the monotherapy group  and the FOS protein, indicating their potential role in delaying acquired resistance.5. Combining ZLJT with EGFR-TKIs may alter signal transduction pathways, block the tumor cell cycle, inhibit tumor activity, enhance cellular vitality, and improve treatment effectiveness.6. The combination of Western and Chinese medicine using EGFR-TKI and ZLJT represents a promising tumor treatment strategy. Lung cancer is the most common malignant tumor in 36 countries, including China. It ranks first in terms of cancer incidence among males and third among females in China . Non-smaAccording to the 2021 NCCN guidelines for non-small cell lung cancer (NSCLC), osimertinib has become the recommended adjuvant therapy for stage Ib-IIIa NSCLC patients with EGFR mutations after surgery . This maAstragalus membranaceus), Danggui (Angelica sinensis), Baiying (Solanum lyratum Thunb.), Longkui (Solanum nigrum L.), Danshen , Shemei (Duchesnea indica), Banzhilian (Scutellaria barbata), and Yujin (Curcuma wenyujin/Curcuma Longa/Curcuma kwangsiensis/Curcuma phaeocaulis). Studies have shown that ZLJT has inhibitory effects on tumor occurrence and development, improves hematopoietic function, and enhances the body's immune system. It is widely used in patients with advanced-stage non-small cell lung cancer (NSCLC) [In recent years, traditional Chinese medicine (TCM) has shown significant efficacy in the treatment of lung cancer progression and the inhibition of drug resistance , 12. Amo (NSCLC) . Huangqi (NSCLC) . Huangqi (NSCLC) . Danggui (NSCLC) . Baiying (NSCLC) . Longkui (NSCLC) . Compoun (NSCLC) . Shemei  (NSCLC) . Banzhil (NSCLC) . Yujin h (NSCLC) .This study aims to evaluate the efficacy and safety of combining EGFR-TKIs with ZLJT in delaying acquired resistance. Furthermore, it aims to explore the underlying mechanisms using network pharmacology and molecular docking techniques.All patients included in this study were recruited from the Second Affiliated Hospital of Air Force Medical University. They were diagnosed with LUAD and received EGFR-TKIs with or without ZLJT from January 1, 2017, to May 1, 2023. Drug resistance occurred during this period.Inclusion Criteria were as follows: diagnosed with stage IIIc or stage IV LUAD based on pathological or cytological examination; pathological tissue specimens confirmed through real-time quantitative PCR to have an EGFR gene exon 19 deletion mutation (19Del) or exon 21 L858R mutation (L858R); receiving EGFR-TKI treatment for the first time, and the combination group should start taking ZLJT (7.8 g/day) concurrently with the first dose of EGFR-TKI and continue for at least 8 weeks; and aged 18 years or older without a history of significant heart, lung, liver, kidney, blood system, or other severe diseases. To exclude interference from primary resistance, patients will be re-evaluated within 8 weeks of TKI initiation to confirm no disease progression.Exclusion Criteria were as follows: failure to meet any of the inclusion criteria; presence of severe cardiovascular, cerebrovascular, or mental disorders; and insufficient medical records or data for analysis.First-generation TKI drugs: a. gefitinib tablets: 250 mg once daily, orally, on an empty stomach or with food until disease progression, intolerable toxic side effects, or patient request to discontinue; b. erlotinib tablets: 150 mg once daily, orally, on an empty stomach or with food until disease progression, intolerable toxic side effects, or patient request to discontinue; and c. icotinib tablets: 125 mg three times daily, orally, on an empty stomach or with food until disease progression, intolerable toxic side effects, or patient request to discontinue.Second-generation TKI drugs: a. afatinib tablets: 40 mg once daily, orally, on an empty stomach or with food until disease progression, intolerable toxic side effects, or patient request to discontinue; and b. dacomitinib tablets: 45 mg once daily, orally, on an empty stomach or with food until disease progression, intolerable toxic side effects, or patient request to discontinue.Third-generation TKI drugs: a. osimertinib tablets: 80 mg once daily, orally, on an empty stomach or with food until disease progression, intolerable toxic side effects, or patient request to discontinue; b. furmonertinib tablets: 80 mg once daily, orally, on an empty stomach or with food until disease progression, intolerable toxic side effects, or patient request to discontinue; and c. almonertinib tablets: 110 mg once daily, orally, on an empty stomach or with food until disease progression, intolerable toxic side effects, or patient request to discontinue.ZLJT treatment regimen: 2.6 g per dose (0.65 g per tablet), three times daily, orally, with a treatment course of every 56 days.Primary Outcome Measures: The main outcome measure is the time from the initiation of EGFR-TKIs combined with ZLJT or EGFR-TKIs alone to the occurrence of disease progression. The difference in time represents the development of acquired resistance.Secondary Outcome Measures: The incidence of EGFR-TKI-related adverse events will be recorded. If a patient experiences any adverse reaction related to TKI medication during the treatment period, it will be counted as 1; otherwise, it will be recorded as 0. Changes in liver and kidney function before and after treatment will be monitored in both groups. AST, ALT, CREA, TBIL, ALB and BUN will be used as safety indicators. A comparison of changes in immune and inflammatory markers before and after TKI treatment will be conducted between the two groups.A retrospective case data research form was prepared and used to collect information on all enrolled cases. The form includes the following details: basic information of the enrolled cases, such as name, sex, and age; disease-related information, such as tumor location, pathological type, and clinical staging; and treatment history prior to enrollment, including whether surgery was performed or not. It is important to ensure objective and accurate completion of the form. In case of any errors, corrections were made by crossing out the incorrect information with a horizontal line and providing the correct information next to it. The corrections were dated and signed by the person making the modifications.https://tcmsp-e.com/) and BATMAN-TCM (http://bionet.ncpsb.org.cn/batman-tcm/) to search for the active ingredients of "Huangqi", "Yujin", "Danggui", "Shemei", "Baiying", "Banzhilian", "Longkui", and "Danshen" in ZLJT. In TCMSP, we used the conditions of oral bioavailability (OB) ≥ 30% and drug likeness (DL) ≥ 0.18 for screening. Since the TCMSP database did not include "Shemei," we used BATMAN-TCM. We set the cutoff value of the input parameter score as ≥ 20 and adjusted P < 0.05 to obtain the active ingredients and targets of "Shemei." The compounds were standardized into simplified molecular inputs and converted into SMILES format, which were then submitted to the Swiss Target Prediction Database (http://www.swisstargetprediction.ch/). By selecting "Homo sapiens" as the filtering condition, the potential therapeutic targets of ZLJT active ingredients were predicted. The compound-target interactions were standardized using the UniProt database (https://www.uniprot.org).We used TCMSP (https://www.ncbi.nlm.nih.gov/geo/). The GSE34228 dataset includes 26 PC9 cell lines classified as either sensitive or resistant to gefitinib. The Limma package in R version 4.3.0 was utilized for integrated analysis of multiple microarrays, and proper data batch processing was performed (https://www.bioconductor.org/). DEGs were selected based on an adjusted P < 0.05 and log2-fold change > 1 or log2-fold change < -1 [DEGs of EGFR-TKI-sensitive and -resistant cells were obtained from the GEO database  to construct a PPI network. The species was limited to Homo sapiens (human), with a medium confidence greater than 0.9, and discrete targets were excluded. The PPI network graph was generated in TSV format for download. The file was imported into Cytoscape software to build the PPI network, and the degree algorithm was used to calculate the top 20 core targets and export them.The obtained intersecting targets were imported into the STRING database (http://www.empowerstats.com) was used for statistical analysis. For normally distributed continuous data with homogeneous variances, t tests were used for group comparisons. For continuous data that did not follow a normal distribution, the median (interquartile range) was used, and nonparametric tests, such as the Mann‒Whitney U test, were performed. For categorical data, percentages are used, and within-group comparisons are conducted using the chi-square test or, if necessary, Fisher's exact test. P < 0.05 was considered to indicate statistically significant.The obtained clinical data were input into a computer, and a relevant database was established. EmpowerStats software  enrichment analysis for molecular functions (MFs), biological processes (BPs), and cellular components (CCs), and KEGG pathway enrichment analysis was performed to elucidate the pathways involved in the inhibition of EGFR-TKIs resistance in LUAD by ZLJT. The results of GO enrichment and KEGG pathway enrichment were visualized and presented using bubble plots .https://www.rcsb.org/), and the chemical structures of compounds were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The small molecule structures (in PDB format) were drawn using ChemDraw 8.0.3 for docking. Autodock 4.2.6 was used for virtual docking between the compounds and protein targets, with only the ligand conformation changed while keeping the protein conformation unchanged. All other parameters were set to default values. The docking results were visualized and analyzed using PyMOL 2.2.0 and Discovery Studio Client v19.1.0 software. The research process is illustrated in Fig. Molecular docking was performed with the main active compounds in ZLJT and the core target proteins. Protein crystal structures were obtained from the PDB database (P > 0.05) . There was also a significant difference in the onset of resistance to first-generation TKIs between the two groups (P < 0.05) , while in the monotherapy group, it was 328.00 days (10.93 months). Compared to the monotherapy group, the combination group showed a significantly longer time to acquired resistance, with an extension of 3.27 months (P > 0.05)  and platelet-to-lymphocyte ratio (PLR) between the two groups before and after medication showed es Table .Table 5By searching the TCMSP and BATMAN databases, a total of 144 active ingredients that met the criteria were obtained in ZLJT Table . Among tP value filtering, a total of 976 upregulated genes and 1059 downregulated genes were obtained , including 1,178 BP. The top three BP were adenylate cyclase-modulating G protein-coupled receptor signaling pathway, epithelial cell proliferation, and neuron death signaling pathway. In terms of CC, the top ones were cell projection membrane, postsynaptic membrane, and leading edge membrane. Additionally, there were 124 MF identified, such as protein heterodimerization activity, amide binding, and DNA-binding transcription. Bubble plots were created using the top 10 results for BP, CC, and MF , the top 30 enriched pathways were plotted in a bubble map  of patients with advanced NSCLC has significantly improved . StudiesThe mechanisms underlying the development of acquired resistance are not fully understood, but one of the most common mechanisms involves the secondary T790M mutation in the affected cancer gene, which leads to structural changes in the ATP-binding pocket and reduced affinity for TKI binding . AdditioP < 0.01). This indirectly contributed to an improvement in patient progression-free survival (PFS). Among the data from the two groups, there was a statistically significant difference in the onset of resistance with the use of first-generation TKIs (P < 0.05), but no statistically significant difference was observed between the second- and third-generation TKIs. In the analysis of immune-inflammatory markers, the P values were all greater than 0.05, suggesting the possibility of insufficient sample size as a potential confounding factor. Regarding safety analysis, there was no statistically significant difference between the two groups, indicating the feasibility of combining ZLJT with TKIs in terms of safety.In the clinical study involving 89 patients, it was observed that EGFR-mutant LUAD patients who received combination therapy of TKIs and ZLJT exhibited a significant extension in the time to acquired resistance compared to the TKI-only group, with a prolongation of 3.27 months (To further explore the mechanism by which ZLJT delays LUAD EGFR-TKIs resistance, we conducted network pharmacology analysis and molecular docking techniques. In this study, we first collected compounds of ZLJT from databases, such as TCMSP and BATMAN-TCM, through network pharmacology analysis. Subsequently, we constructed a "ZLJT-compound-target-disease" regulatory network and identified the main active ingredients of ZLJT that have anti-LUAD cell resistance and inhibit tumor cell growth and migration, including quercetin, luteolin, kaempferol, and tanshinone IIA. Quercetin, a natural flavonoid, is found in many fruits and vegetables . RelatedIt has been reported that kaempferol enhances anticancer potency when used in combination with other anticancer drugs. When combined with quercetin, kaempferol significantly increases the anticancer effect of quercetin by blocking its efflux. Moreover, kaempferol intake significantly enhances the cytotoxicity of cisplatin and increases the bioavailability of various anticancer drugs . TanshinPPI network analysis revealed that IL-6, FOS, STAT3, ErbB2, EGF, and other targets had high degree values and were the core targets of ZLJT in delaying LUAD resistance. IL-6 is involved in multiple physiological and pathological processes, such as cell proliferation, inflammation, and stress. Cancer-related inflammation significantly affects cancer metastasis, clinical manifestations of cancer, and patients' tolerance to anticancer treatment . EffectiGO functional enrichment analysis revealed that the biological process of ZLJT to delay LUAD resistance mainly involves the adenylate cyclase-modulating G protein-coupled receptor signaling pathway, which plays a crucial role in various cancers, including lung adenocarcinoma. This pathway can contribute to cancer cell proliferation, invasion, and metastasis, making it a potential target for lung adenocarcinoma treatment. Additionally, the process involves epithelial cell proliferation since lung adenocarcinomas typically originate from epithelial tissues of the lungs. Understanding the mechanism of epithelial cell proliferation can provide valuable insights into the biology of lung adenocarcinoma. Moreover, neuron death may be associated with the effects of neurons in lung adenocarcinoma. Abnormalities in the neuron death signaling pathway could be linked to the tumor microenvironment and treatment response, thus influencing the development of cancer treatment strategies and pathological analysis. In terms of CC, the cell projection membrane may play a role in the invasion and migration process of lung adenocarcinoma cells. Investigating this process can enhance our understanding of the metastatic mechanisms of cancer cells and potentially improve therapeutic strategies. If lung adenocarcinoma occurs near the nervous system, the pathway associated with the postsynaptic membrane may be affected, which could have implications for studying specific subtypes of lung adenocarcinomas and making therapeutic choices. Furthermore, leading edge membranes are often involved in the process of cell migration and infiltration, which is crucial for characterizing tumor aggressiveness and metastasis in lung adenocarcinomas. Lastly, the molecular functions mainly include protein heterodimerization activity, an important component in signaling pathways. Understanding these activities can provide insights into protein interactions associated with lung adenocarcinoma. While amide binding may not be directly critical in the specific study of lung adenocarcinoma, it still holds biological significance. Additionally, DNA-binding transcription plays a crucial role in regulating gene expression, which is closely linked to the development and treatment response of lung adenocarcinoma.Through KEGG enrichment analysis, the PI3K-Akt signaling pathway was identified as the top core node targeted by ZLJT in combating LUAD cell resistance. As an important signaling pathway, the PI3K-Akt signaling cascade is involved in the release of various proinflammatory cytokines, such as NF-κB, IL-6, and IL-17 . InflammIn addition, based on the results of KEGG pathway enrichment analysis, it is evident that ZLJT may play a role in delaying drug resistance in LUAD through several key pathways. Among these pathways, the lipid and atherosclerosis pathway is closely associated with tumor drug resistance. Tumors in harsh environments like hypoxia may increase the expression of growth factors and cytokines to bypass the drug target and generate potential drug resistance . AdditioThe enrichment results suggest that ZLJT may affect EGFR tyrosine kinase inhibitor resistance and signaling pathways in non-small cell lung cancer. These findings provide strong support for the role of ZLJT in inhibiting drug resistance and delaying tumor progression in LUAD.Molecular docking validation showed that luteolin, tanshinone IIA, and quercetin exhibited the strongest binding affinity and docking scores with the FOS target protein. Their binding affinities were -9.8, -9.8, and -8.7 kcal/mol, respectively. This indicates that active ingredients, such as luteolin, tanshinone IIA, and quercetin, in ZLJT play a critical role in inhibiting late-stage LUAD resistance.However, further studies and experimental validation are needed to gain a deeper understanding of its mechanism and potential for clinical application.This study has certain limitations. Firstly, it only used the TCMSP and BATMAN-TCM tools for screening active ingredients of the drug, which may introduce bias in exploring the drug's active ingredients. To improve the level of evidence for drug ingredients, mass spectrometry or experimental studies can be conducted. Secondly, since the purpose of this article is to clinically retrospectively evaluate the effect of ZLJT combined with EGFR-TKI in delaying drug resistance in advanced lung adenocarcinoma, there is a lack of experimental verification of pathway research. Future studies should include relevant cell experiments or animal experiments to complete the evidence chain. Lastly, there were still limitations such as a single center and a small number of samples after screening, leading to a certain degree of bias in the results. Additionally, it is worth further considering and exploring how to better reduce interference factors in clinical experiments to ensure the scientific nature of the experiments.In summary, ZLJT has potential antitumor progression effects. In EGFR gene mutation-positive LUAD patients, the combination of EGFR-TKIs and ZLJT may have a role in delaying acquired resistance. Active ingredients, such as quercetin, luteolin, kaempferol, and tanshinone IIA can modulate signaling pathways related to NSCLC-EGFR cell resistance, lipids and atherosclerosis, chemical carcinogenesis receptor activation, and neuroactive ligand‒receptor interaction. They act on targets such as IL-6, FOS, STAT3, ERBB2, and EGF, exerting effects on delaying tumor cell resistance and inhibiting tumor cell invasion and metastasis. ZLJT may inhibit LUAD cell EGFR-TKIs resistance by altering signal transduction pathways, blocking the tumor cell cycle, suppressing tumor activity, enhancing cell vitality, and improving the bioavailability of coadministered drugs. Molecular docking simulations further validate the spatial and energy matching between the targets and active ingredients, providing theoretical evidence for the ability of ZLJT core components to delay LUAD resistance. This study provides insights for further mechanistic research and the clinical application of combating LUAD resistance. Therefore, more cell and animal experimental verification is still needed in the future to address the inherent limitations of this study.