The results showed that ZG significantly improved the bone mass and micro-trabecular architecture in OVX mice compared with untreated OVX mice. ZG could promote bone formation and inhibit bone resorption to ameliorate ovariectomy-induced osteoporosis as evidenced by increased number of osteoblast (N.Ob/Tb.Pm) and decreased number of osteoclast (N.Oc/Tb.Pm) in treated group compared with untreated OVX mice. After identifying potential drug–disease common targets by network pharmacology, GO enrichment analysis predicted that ZG might affect various biological processes including osteoblastic differentiation and osteoclast differentiation. The KEGG enrichment analysis suggested that PI3K/Akt and mTOR signaling pathways could be the possible pathways. Furthermore, the experiments in vitro validated our findings. ZG significantly down-regulated the expression of osteoclast differentiation markers, reduced osteoclastic resorption, and inhibited the phosphorylation of PI3K/Akt, while ZG obviously up-regulated the expression of osteogenic biomarkers, promoted the formation of calcium nodules, and hampered the phosphorylation of 70S6K1/mTOR, which can be reversed by the corresponding pathway activator. Thus, our study suggested that ZG could inhibit the PI3K/Akt signaling pathway to reduce osteoclastic bone resorption as well as hamper the mTORC1/S6K1 signaling pathway to promote osteoblastic bone formation.As the society is aging, the increasing prevalence of osteoporosis has generated huge social and economic impact, while the drug therapy for osteoporosis is limited due to multiple targets involved in this disease. Zhuangguguanjie formulation (ZG) is extensively used in the clinical treatment of bone and joint diseases, but the underlying mechanism has not been fully described. This study aimed to examine the therapeutic effect and potential mechanism of ZG on postmenopausal osteoporosis. The ovariectomized (OVX) mice were treated with normal saline or ZG for 4 weeks after ovariectomy following a series of analyses. The bone mass density (BMD) and trabecular parameters were examined by micro-CT. Bone remodeling was evaluated by the bone histomorphometry analysis and ELISA assay of bone turnover biomarkers in serum. The possible drug–disease common targets were analyzed by network pharmacology. To predict the potential biological processes and related pathways, GO/KEGG enrichment analysis was performed. The effects of ZG on the differentiation phenotype of osteoclasts and osteoblasts and the predicted pathway were verified  Zhuangg pathway . ZG has  pathway . Some co pathway  and inhi pathway . Psorale pathway . However pathway .in vitro/vivo were used to unravel the underlying mechanisms.Despite intensive clinical application and research, the function and mechanism of ZG on osteoporosis have not been fully investigated. As a systematic biological method, network pharmacology provides a network visualization of components and targets that are applied for the comprehensive study of TCM compounds . In thisTraditional Chinese medicine ZG was obtained from Sanjiu Medical & Pharmaceutical Co., Ltd. . It was dissolved in normal saline as a vehicle for animal experiment while using phosphate-buffered saline (PBS) as vehicle for cell experiment. Fetal bovine serum (FBS) was purchased from Gibco (United States). Alpha-minimal essential medium (a-MEM), PBS, trypsin−EDTA, penicillin, and streptomycin were purchased from Wisent (Canada). Cell Counting Kit-8 (CCK-8) and MHY1485 were from MCE (China). IGF-1, M-CSF, RANKL were from R&D systems (United States). Osteogenic Differentiation Medium and Alizarin Red staining kit were purchased from Cyagen (China).2O, mobile phase B consisted of acetonitrile, mobile phase C consisted of 0.1% formic acid in H2O, mobile phase D consisted of 0.1% formic acid in acetonitrile. The liquid chromatography was carried out using a gradient program at a flow rate of 0.4 ml/min; the injection volume was 10 μl; the column oven temperature was 40°C. The acquisition of high-resolution mass spectra was conducted in the positive and negative ion modes. Optimized ionization conditions were as follows: sheath gas flow, 8 L/min; capillary voltage, 2,500 V; gas temperature, 300°C; the analysis was carried out using a scan from 80 to 1,000 m/z. Identification of metabolites was conducted by searching the OCTML database according to exact mass matching (<10 ppm) and secondary spectrum matching. HPLC was performed to determine the content of psoralen, isopsoralen, icariin, and osthole. The ZG extract was dissolved to a final concentration of 20 mg/ml in methyl alcohol. The diluted solution was passed through a 0.45 µM filter membrane prior to HPLC analysis. The wavelength of UV detection was set at 254 nm for psoralen, isopsoralen, and icariin, while was set at 320 nm for osthole.LC-MS/MS analysis was performed using Thermo QE HF-X mass spectrometer and Thermo U3000 liquid chromatography. The analysis was conducted using Waters, ACQUITY UPLC HSS T3 1.7 µm, 2.1 mm× 150 mm column. The mobile phase A consisted of 5 mM ammonium formate in HEight-week-old male C57BL/6 mice (weighing 20 ± 2 g) were fed under pathogen-free conditions, supplied by the Model Animal Research Center of Nanjing University. The animals were maintained in a 12-h light/dark cycle. A total of 36 mice were equally divided into the following groups: sham with vehicle treatment group, ovariectomy with vehicle treatment group, and ovariectomy with ZG treatment group, 12 mice used per group. Subsequently, the OVX group and the OVX + ZG group underwent ovariectomy, anesthetized by isoflurane. A similar incision was made to the sham group, which was subsequently sutured, without any other procedures. All mice were monitored on a daily basis for postoperative complications within the first week after surgery. Both saline and ZG (468 mg/kg/day) administration started at 4 weeks after surgery. The mice were sacrificed by isoflurane overdose following 4 weeks of continuous treatment. After sacrifice, the bilateral femora were isolated and collected, as well as blood serum. The experimental protocols were reviewed and approved by the Animal Care Committee of the Affiliate Drum Tower Hospital, Medical school of Nanjing University in accordance with the Institutional Animal Care and Use Committee guidelines (2021AE01031).2O2 to remove endogenous peroxidase, incubated with anti-osteocalcin antibody  at 4°C overnight prior to addition of secondary antibody , incubated at 37°C for 120 min, then colored by diaminobenzidine (DAB) reaction staining. Finally, sections were evaluated by microscopy  in a blinded manner. Quantitative analyses were performed using ImageJ Software.Femurs and tibias were fixed in 4% paraformaldehyde for 24 h immediately after death. The fixed samples were imaged by micro-CT . To analyze the trabecular bone, a region of interest was selected 0.5 mm below the growth plate of the femur and tibia. Bone mineral density (BMD), bone volume to total volume (BV/TV), trabecular number (Tb. N), trabecular separation (Tb. Sp), and trabecular thickness (Tb. Th) were analyzed with the built-in software. After micro-CT analyses, femurs were decalcified with 5% EDTA and were embedded in paraffin blocks, and 5 mm sections were cut in serial incisions in the coronal plane. The sections were stained with hematoxylin and eosin (HE), and then TRAP staining was used to detect osteoclast activity. The expression of osteocalcin was examined by immunohistochemistry. Briefly, tissue sections were treated with 3% HMice were injected with calcein (10 mg/kg) at 10 days before euthanasia and xylenol orange (30 mg/kg) at 3 days before euthanasia. The femurs were collected and prepared for hard tissue sections with EXAKT 300CP. Fluorescence images were then obtained using confocal microscope .A total of 500 μl of blood was collected from the hearts of the mice following anesthesia. Serum samples were obtained by centrifugation of blood at 3,000 rpm for 15 min, and they were stored at −80°C prior to analysis. An ELISA assay was used to measure the plasma concentration of the biomarkers PINP, CTX-1, and TNF-α  according to the manufacturer’s instructions. The optical absorbance at 450 nm was determined using a microplate absorbance reader .All components in ZG were searched from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and Bioinformatics Analysis Tool for Molecular mechanism (BATMAN). Then related chemical compositions were supplemented according to literature. All results were saved in SDF format. In the Swiss Target Prediction database, species was restricted to “Homo sapiens”, and the candidate targets with prediction scores >0 were selected as the potential targets of ZG.Disease targets were retrieved using “osteoporosis” as the keywords based on the Disgenet database. All drug targets and disease targets were introduced into Venny2.1.0 to obtain common targets of ZG and osteoporosis. Cytoscape (version 3.2.1) was used to establish the drug-bioactive ingredient-target-disease network of ZG acting on osteoporosis. Finally, we used Network Analyzer function to analyze the main active components of traditional Chinese medicine compound prescription.The 212 common targets were uploaded to the STRING database to obtain the network relation of target interaction, with minimum required interaction score>0.4. All data were imported in Cytoscape for constructing the PPI network, in which the color and size of nodes represent the degree of the node.p < 0.05 and Q < 0.05. The results were output in the form of bar chart and bubble chart, respectively.To convert the drug–disease common target into entrez ID, we installed the Bioconductor package “org.Hs.eg.db” in R software and ran it. After that, the “cluster Profiler” package was installed in R software, and enrichment analysis of GO and KEGG was carried out according to the transformed entrez ID, with 4 cells/ml and cultured until they adhered completely. Then, cells were treated with ZG at different concentrations  for 24 h. After that, 10 µl of CCK-8 was added and incubated for 1 h. The absorbance was recorded at 450 nm, and experiments were performed in triplicate and repeated three times independently.The cytotoxicity of ZG was determined by CCK-8 assay. Briefly, the bone marrow monocytes (BMMs) and preosteoblasts were seeded onto 96-well plates independently at a concentration of 1 × 10BMMs were isolated from 4-week-old C57/BL6 male mice by separating the long bones and rinsing the medullary cavity. BMMs were cultured in a-MEM supplemented with 10% FBS, 1% penicillin-streptomycin solution, and 30 ng/ml M-CSF. After 48 h, the cell medium was removed, and the adherent BMMs were considered as osteoclast precursor cells that used for subsequent experiments. Then BMMs were seeded in 96-well plates and cultured to100% confluence. Osteoclast formation was induced through medium replacement with a-MEM containing 30 ng/ml M-CSF and 50 ng/ml RANKL, with different concentrations of ZG  and IGF-1  depending on experimental design. After 10 days, the cells were fixed in 4% PFA and stained for TRAP using an acid phosphatase leucocyte staining kit (Sigma-Aldrich), according to the instructions. To study the actin ring formation, the cells were permeabilized with 0.3% Triton-X 100 and cultured with phalloidin to visualize F-actin after fixed in 4% PFA. Both TRAP staining and phalloidin staining were photoed by a fluorescence microscope .BMMs were cultured as previously described. The adherent BMMs were planted onto bovine bone slices  in a-MEM containing 30 ng/ml M-CSF and 50 ng/ml RANKL. They were also treated with different concentrations of ZG  and IGF-1 (100 ng/ml) depending on experimental design. After 10 days, the bovine bone slices were ultrasonically cleaned and cleared with PBS followed by air-drying for 2–3 h. Finally, the samples were subjected to vacuum drying, sprayed with gold, and imaged by SEM (FEI Quanta 200). Quantitative analyses were performed using ImageJ Software.5 cells/well in a-MEM supplemented with 10% FBS and 1% penicillin-streptomycin solution. After being cultured to 100% confluence, osteoblast formation was induced through medium replacement with osteogenic differentiation medium . They were also treated with different concentrations of ZG  and MHY1485 (10 μM) depending on experimental design. ALP staining (Sigma-Aldrich) was performed on Day 7, and Alizarin red staining was conducted on Day 21, according to the instructions. Both ALP staining and Alizarin red staining were photoed by microscopy  in a blinded manner.Preosteoblast was isolated from calvariae of newborn mice by serial digestion in 1 mg/ml of collagenase type 2. After centrifugation and filtration, preosteoblast was seeded onto six-well plates at a density of 1×10The cells were homogenized and lysed in RIPA buffer. Total proteins were extracted by centrifugation at 4°C for 15 min at 13,000 g. Subsequently, total proteins were subjected to 7.5/10/15% SDS-PAGE and transferred onto PVDF membranes . The membranes were blocked in 4% nonfat milk, and primary antibodies of TRAP , CTSK , TRAF6 , p-Akt , Akt , p-PI3K , PI3K , mTOR , p-mTOR , p-70S6K1 , Collagen 1 , RUNX2 , and β-actin  were added and incubated at 4°C overnight. HRP-goat anti-rabbit or mouse antibodies  were added and incubated at room temperature for 40 min. Finally, the membranes were scanned with chemiluminescence system .Total RNA was extracted from the cultured cells using TRIzol . cDNA was subsequently reverse transcribed from total RNA using cDNA synthesis kit , according to the manufacturer’s instructions. Real-time PCR was carried out by incubation of cDNA, ddH2O, primers, ROX, and SYBR Premix Ex Taq . The reagents comprised a total volume of 10 μl per reaction. The samples were subjected to 40 cycles of amplification according to the manufacturer’s instructions. The PCR reactions were carried out in Viia 7 Real-Time PCR System . All data were normalized to β-actin by the 2^(−ΔΔCt) method, and the tests were carried out in triplicate. The sequences of the used primers are indicated in 5 cells/well in a-MEM containing 30 ng/ml M-CSF. Cells were then transfected with pNFκB-TA-luc and pRL-TK responsive luciferase plasmid (Beyotime). After 48 h, the BMMs were treated with or without ZG  and RANKL (50 ng/ml) for 2 h. At the end of the time points, cells were lysed, and luciferase activity was measured using the luciferase reporter assay kit (Beyotime) and a luminescence reader .The BMMs were seeded in 48-well plates at a density of 1.5 × 10p-values < 0.05 were considered to be statistically significant .In this study, original data obtained from three or more independent experiments were analyzed by one-way ANOVA with Tukey’s HSD post hoc test and presented as mean ± standard deviation (SD). The results of statistical analysis with ZG is a TCM formulation recorded in the Chinese Pharmacopoeia and approved by the CFDA . Each batch of ZG is manufactured in strict accordance with national execution standard (WS3-709 (Z-141)-2010Z), and the conformity of main components was identified and compared. We used LC-MS/MS to determine the major components of ZG for every batch experiment. The total positive and negative ion chromatograms of ZG are shown in To investigate the therapeutic effects of ZG on osteoporosis, we orally administrated ovariectomized (OVX) mice with ZG at a dosage of 468 mg/kg every day. We observed better organized trabecular micro-architecture with a higher bone mass in OVX + ZG group than that in OVX group after treatment both in HE staining  and 3D rThe active components of ZG formula comprising multiple herbs, including Radix Angelicae Biseratae, Drynariae Rhizoma, Spatholobus Suberectus Dunn, Myrrha, Aucklandiae Radix, Olibanun, Herba Taxilli, Rehmanniae Radix Praeparata, Dipsaci Radix, and Epimrdii Herba were identified based on TCMSP with the criteria of OB ≥ 30% and DL ≥ 0.18, besides that the effective components of Psoralea corylifolia and Cibotium barometz were screened under setting score ≥20 in the Batman database. The extra verified active elements were added in combination based on the literature data mining, such as naringin, hederagenin, Asperosaponin VI, and so on. At last, the reported active ingredients of ZG were confirmed, and the potential targets were identified based on the Swiss Target Prediction database .A total of 1,098 disease targets were obtained from the Disgenet database with “Osteoporosis” as the keyword. Then we generated 212 common targets by taking the intersection of 1,018 drug targets and 1,098 disease targets. The 196 potential active components in ZG and 212 drug–disease common targets were inputted into the Cytoscape software, and the network diagram of “drug-component-target-disease” interaction was drawn . Among tThe 212 common targets were submitted to the STRING database, which provided the information of predicted interaction, and they were imported in Cytoscape to analyze and construct the PPI network , in whicThe biological process, cellular composition, and molecular function were selected by GO analysis of 212 common targets. The results of GO showed that the set of overlapping genes was enriched into 197 molecular functions, which were mainly involved in protein serine/threonine kinase activity, protein tyrosine kinase activity, hormone receptor binding, etc . The setTo further explore the potential pathways of ZG on osteoporosis, a total of 162 KEGG pathways were selected by KEGG analysis of 212 common targets. As a result, a bubble diagram with functional enrichment of KEGG pathways was formed . The resWe first examined the effect of ZG on viability of BMMs. BMMs were incubating with ZG at concentrations of 0, 30, 60, 90, 180,360, and 720 µg/ml for 24 h. The results showed that cell viability of BMMs was significantly inhibited after ZG treatment at the concentration of 360 µg/ml for 24 h while lower concentrations had no significant effect . The samin vitro at the concentration of 90 or 180 μg/ml, which was determined by CCK-8 as the optimal low and high concentration of ZG for in vitro administration. After treated with ZG for 24 h, the number of multinuclear TRAP-positive cells was significantly lesser than that in control group , collagen 1, RUNX2, and osteopontin (OPN), in osteoblasts treated with ZG were all obviously higher than those in osteoblast-treated vehicle, respectively  and ZG at 90 or 180 μg/ml for 24 h, respectively. The optimal low and high concentrations of ZG were determined by CCK-8. We observed that the expression levels of osteogenic differentiation marker genes, including ectively . Followiectively . The proectively . To valiic genes , the ALPic genes , and theic genes  in osteoic genes . Thus, oin vivo and in vitro.During the development of osteoporosis, the balance between osteoclast-mediated bone resorption and osteoblast-mediated bone formation was disrupted . MultiplAs the ovariectomized mouse model has widely been used to mimic clinical features of postmenopausal osteoporosis, we orally administrated OVX mice with ZG at dosage of 468 mg/kg every day according to dosage conversion formula based on body surface area . Both H&in vivo, we further explored the potential action mechanism using network pharmacology, which could facilitate exploring the potential interaction mechanism between drug components, diseases, and molecular targets based on network visualization. In the present study, we identified the active components in ZG and drawn the network diagram of “drug–component–target–disease” interaction. A total of 212 drug–disease common targets were predicted. The mutual regulation of various targets creates the occurrence and development of biological processes. PPI network analysis demonstrated the priority of interactions between targets. Targets such as GAPDH, Akt, JUN, and mTOR showed a more important position in the process of affecting other targets. However, a single target with higher influence in PPI network does not necessarily mean that it will participate in the best pathway which common targets enriched. After GO enrichment analysis, we found that these overlapping genes were enriched into 2,748 biological processes including response to peptide hormone, ossification, cellular response to oxidative stress, osteoblast differentiation, osteoclast differentiation, and so on. Further KEGG analysis showed that common targets mainly enriched in the PI3K/Akt signaling pathway, MAPK signaling pathway, mTOR signaling pathway, and PPAR signaling pathway. Combined with the results of KEGG and PPI network, PI3K/Akt and mTOR signaling pathways were predicted to be the possible targets for ZG to exert its anti-osteoporosis effect. It was reported that the PI3K/Akt signaling pathway was responsible for activation of osteoclast differentiation  was an evolutionarily conserved protein kinase including mTORC1 and mTORC2 protein complexes with different structures and functions. S6 kinase 1 (S6K1) was a downstream target and phosphorylated substrate of mTORC1 . The mTOvia inhibiting the NF-κB pathway. There were still many potential pathways that may be involved in the treatment of osteoporosis by ZG, which need more attention in future studies.There were also many other pathways that have been screened by network pharmacology as potential targets of ZG for osteoporosis, such as the NF-κB signaling pathway. NF-κB signaling was considered to be the first event in early osteoclast development from precursors. As the key factor of osteoclast differentiation, RANKL could activate the NF-κB pathway and thus release the inhibition of NFATc1 to positively regulate osteoclast formation and functions . By consWe studied ZG as a whole herb formulation consisting of many components by network pharmacology and experimental verification. It would be needed to further integrate active components of metabolomics determination with the bioavailability and medicinal properties of these components for better analysis of network pharmacology. This will help us to improve the beneficial anti-osteoporosis effects and avoid side effects of ZG herb formulation for further clinical application. In addition, our study mainly used OVX mice to simulate postmenopausal osteoporosis. In future, other types of osteoporotic animal models would be carried out to determine whether ZG still has significant efficacy for other clinical types of osteoporosis, such as senile osteoporosis and secondary osteoporosis.in vivo and in vitro as well as network pharmacological analysis, ZG could inhibit the PI3K/Akt signaling pathway to reduce osteoclastic bone resorption as well as hamper the mTORC1/S6K1 signaling pathway to promote osteoblastic bone formation. Thus, ZG can be used as an appropriate clinical strategy for the treatment of osteoporosis.Overall, ZG excreted beneficial effects on preventing bone loss in osteoporosis. Based on current mechanism, exploration data from experiments


Variovorax sp. PAMC28711. Trehalose, a disaccharide, is used by the microorganism as an alternative carbon source.Metabolism including anabolism and catabolism is a prerequisite phenomenon for all living organisms. Anabolism refers to the synthesis of the entire compound needed by a species. Catabolism refers to the breakdown of molecules to obtain energy. Many metabolic pathways are undisclosed and many organism-specific enzymes involved in metabolism are misplaced. When predicting a specific metabolic pathway of a microorganism, the first and foremost steps is to explore available online databases. Among many online databases, KEGG and MetaCyc pathway databases were used to deduce trehalose metabolic network for bacteria Variovorax sp. PAMC28711, we found that all enzymes that were missing in KEGG and MetaCyc databases were involved in the trehalose metabolic pathway.While using KEGG and MetaCyc databases, we found that the KEGG pathway database had one missing enzyme . The MetaCyc pathway database also had some enzymes. However, when we used RAST to annotate the entire genome of Findings of this study shed light on bioinformatics tools and raise awareness among researchers about the importance of conducting detailed investigation before proceeding with any further work. While such comparison for databases such as KEGG and MetaCyc has been done before, it has never been done with a specific microbial pathway. Such studies are useful for future improvement of bioinformatics tools to reduce limitations. Variovorax sp. PAMC28711 selected in this study to explore trehalose metabolism is one of cold adapted lichen-associated bacteria isolated from Antarctica. Analysis of enzymes from cold-adapted microorganisms has become common in recent years because cold-adapted enzymes from organisms living in Polar regions, deep oceans, and high altitudes have various benefits [Variovorax is a cold adapted, Gram-negative, motile bacterium that comes in a variety of shapes, including flat, slightly curved, and rod shapes. Because of the presence of carotenoid pigments, Variovorax colonies are yellow, slimy, and shiny [Variovorax sp. PAMC28711. One of them is trehalose metabolic pathway. Trehalose is a naturally occurring alpha-linked disaccharide formed by two molecules of glucose. It was first isolated by French chemist Marchellin Berthelot in the mid-nineteenth century from Trehala manna, a sweet substance obtained from nests and cocoons of the Syrian coleopterous insects (Larinus maculatus and Larinus nidificans) known to feed on the foliage of a variety of thistles. Trehalose is used for biopharmaceutical preservation of labile protein drugs and cryopreservation of human cells. It is also widely used in the food industry [Metabolism refers to all biochemical processes that occur during the growth of a cell or an organism. Microbial metabolism involves a group of complex chemical compounds. It includes anabolism and catabolism for microorganisms to obtain energy and nutrients for survival and reproduction. A microbe’s metabolic properties are the foremost important factors in determining its condition. They may be accustomed to monitor biogeochemical cycles and industrial processes . Therefobenefits . Genus Vnd shiny . There aindustry . Trehaloindustry . MetaCycindustry , 11. Rapindustry . RAST anindustry –16.Variovorax sp. PAMC28711 and predict enzymes involved in this pathway, these two databases were chosen in study. This is the first study to compare cold-adapted bacteria to well-known databases and predict missing enzymes using RAST annotation software for further analysis of results obtained from KEGG and MetaCyc databases. Furthermore, this paper provides insight into how to validate computational data’s outcomes and proceed further.KEGG and MetaCyc are two well-known and popular databases for metabolic pathway prediction. To study trehalose metabolic pathway in Variovorax sp. PAMC28711 was obtained from the National Center for Biotechnology Information (NCBI) genome database (https://www.ncbi.nlm.nih.gov/) for this metabolic pathway study. The GenBank accession number of Variovorax sp. PAMC28711 is NZ_CP014517.1.A complete genome information of http://www.kegg.jp/ or http://www.genome.jp/kegg) and MetaCyc database (MetaCyc.org) were used to predict trehalose metabolic pathway in the complete genome of Variovorax sp. PAMC28711. During prediction of pathway via the annotated file, bioinformatics tools such as RAST annotation server (https://rast.nmpdr.org/rast.cgi) were used to find the missing enzyme.The KEGG pathway database  and trehalose-6-phosphate phosphatase (OtsB). The TreS reversible pathway has one enzyme, trehalose synthase.The comparison of three programs  for the prediction of enzymes involved in trehalose metabolism in As shown in Table Variovorax sp. PAMC28711 obtained from the KEGG pathway database [Variovorax sp. PAMC28711 lacks enzyme maltooligosyl-trehalose synthase (TreY: EC 5.4.99.15), which makes the TreY/TreZ pathway incomplete.Figure database –20. TrehVariovorax sp. PAMC28711 obtained from the MetaCyc database. Figure Variovorax sp. PAMC28711: trehalose biosynthesis I (OtsA: EC 2.4.1.15 and OtsB: EC 3.1.3.12), trehalose biosynthesis IV (TS: EC 5.499.16), and trehalose biosynthesis V . According to MetaCyc, trehalose biosynthesis V has three enzymes . However, Variovorax sp. PAMC28711 lacks enzyme TreY: EC 5.4.99.15, which prevents the trehalose biosynthesis V pathway from being complete. Therefore, it is assumed that the trehalose biosynthesis V pathway is absent in Variovorax sp. PAMC28711 as results suggest that only two trehalose biosynthesis pathways are involved in this strain.Figure Variovorax sp. PAMC28711 has three pathways for trehalose biosynthesis OtsA/OtsB, TS, and TreY/TreZ. Enzymes involved in these three pathways are trehalose 6-phosphate synthase (OtsA: EC 2.4.1.15), trehalose 6-phosphate phosphatase (OtsB: EC 3.1.3.12), trehalose synthase (TS: EC 5.499.16), maltooligosyl-trehalose synthase (TreY: EC 5.4.99.15), and maltooligosyl-trehalose trehalohydrolase (TreZ: EC 5.3.2.1.141). The trehalose degradation pathway (TreH) in Variovorax sp. PAMC28711 possesses one enzyme, trehalase. Figure Variovorax sp. PAMC28711. The missing enzyme (TreY: EC 5.4.99.15) was found from results of RAST annotation through SEED Viewer which started and stopped at 335612 to 3352054 coding sequence (CDS) , in KEGG results. RAST annotation is an excellent starting point for a more systematic annotation initiative since it can differentiate between two types of annotation and use reasonably accurate subsystem-based statements as the basis for a through metabolic reconstruction [Variovorax sp. PAMC28711 used all three trehalose biosynthesis pathways. In addition, we examined MetaCyc pathway database to compare our results and found that the enzyme maltooligosyl-trehalose synthase (TreY: EC 5.4.99.15) was also missing in this database (Table FIGfams) [Variovorax sp. PAMC28711. MetaCyc is widely used in a variety of fields, including genome annotation, biochemistry, enzymology, metabolomics, genome and metagenome analysis, and metabolic engineering, duet to its exclusively experimentally determined results, intensive curation, comprehensive referencing, and user-friendly and highly integrated design. Although these two databases (KEGG and MetaCyc) have distinct features, both bioinformatics tools have certain drawbacks that should be considered when conducting research validation. It is important to note that different pathway databases have different pathway boundaries. The KEGG database favors complex metabolic maps that include all known reactions related to a general topic, regardless of whether they occur within the same species or even the same kingdom. UniPathway [Trehalose metabolism is one of metabolism pathways for carbohydrates. Five distinct pathways for trehalose synthesis have been described. However, there is only one pathway for trehalose synthesis in fungi, plants, and animals . These ftruction . As a reFIGfams) . The KEGFIGfams) . A five-FIGfams) . Other mFIGfams) . It contiPathway , on the iPathway .Variovorax sp. PAMC28711 possessed the enzyme maltooligosyl-trehalose synthase (TreY: EC 5.4.99.15) in the TreY/TreZ pathway for trehalose biosynthesis. Therefore, researchers should be aware of this when conducting preliminary screening employing bioinformatics tools. Many researchers are employing bioinformatics tools to predict their hypothesis before conducting any experiments. Our exploration of the trehalose metabolic pathway using two commonly used pathway databases demonstrated that bioinformatics tools might not provide accurate results. Thus, we need to evaluate databases before drawing definite conclusions.Before performing any kind of wet laboratory work, bioinformatics methods play a crucial role in predicting pathways. Online software has been proven to be useful in predicting research projects. Although commonly used online programs have good features, they have some limitations. In this study, we compared results of predicting trehalose metabolism pathways using two common databases. We found that both databases had some limitations as both databases showed enzymes missing for specific pathways. However, RAST annotation revealed that


Gaining insight into the mechanisms of signal transduction networks (STNs) by using critical features from patient-specific mathematical models can improve patient stratification and help to identify potential drug targets. To achieve this, these models should focus on the critical STNs for each cancer, include prognostic genes and proteins, and correctly predict patient-specific differences in STN activity. Focussing on colorectal cancer and the WNT STN, we used mechanism-based machine learning models to identify genes and proteins with significant associations to event-free patient survival and predictive power for explaining patient-specific differences of STN activity. First, we identified the WNT pathway as the most significant pathway associated with event-free survival. Second, we built linear-regression models that incorporated both genes and proteins from established mechanistic models in the literature and novel genes with significant associations to event-free patient survival. Data from The Cancer Genome Atlas and Clinical Proteomic Tumour Analysis Consortium were used, and patient-specific STN activity scores were computed using PROGENy. Three linear regression models were built, based on; (1) the gene-set of a state-of-the-art mechanistic model in the literature, (2) novel genes identified, and (3) novel proteins identified. The novel genes and proteins were genes and proteins of the extant WNT pathway whose expression was significantly associated with event-free survival. The results show that the predictive power of a model that incorporated novel event-free associated genes is better compared to a model focussing on the genes of a current state-of-the-art mechanistic model. Several significant genes that should be integrated into future mechanistic models of the WNT pathway are DVL3, FZD5, RAC1, ROCK2, GSK3B, CTB2, CBT1, and PRKCA. Thus, the study demonstrates that using mechanistic information in combination with machine learning can identify novel features (genes and proteins) that are important for explaining the STN heterogeneity between patients and their association to clinical outcomes. Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide ,2,3. FroSTNs are extremely adjustable and dynamic . To bettSTNs can be mapped to their most frequent mutations. Interestingly, APC (adenomatous polyposis coli) negatively regulates WNT activity since it is an integral part of the destruction complex, which targets ß-catenin for degradation. Mutations in APC thus lead to hyperactive WNT signalling . Across In the present study, we developed adjustable linear regression machine learning models of the activation of the canonical WNT signalling pathway and incorporated genes and proteins that were significantly associated with event-free patient survival in CRC. The term “adjustable” in such models represents the capability to vary (include/remove) features with ease to determine which features are most important. The results revealed that the machine learning models can not only help to understand the behaviour of the initial steps of this complex network by identifying novel features not yet included in mechanistic models but also interpret its behaviour by relating pathway activity to clinical outcomes on the level of individual patients.p-values of each significant feature are denoted below in In line with the literature, five signalling pathways critical in CRC were selected to analyse, from several network databases, including WikiPathways, Gene Set Enrichment Analysis (GSEA), and KEGG ,22,37,38p-value cut-off of 0.5 and 0.05, respectively.The total number of genes whose RNA expression correlated with event-free patient survival in this cohort was 389 of 872 genes across all CRC signalling pathways considered. For most pathways, including the WNT pathway, approximately half of the genes were significant using a fold change and p-values of the significant genes in the y-axis against their x-axis. Genes above the dashed threshold line, to the right, DVL3 (dishevelled segment polarity protein 3), VANGL2 (vang-like protein 2), CER1 (cerberus 1), and TCF7L1 (transcription factor 7-like 1), etc., are found to be significant, with the lowest p-values and greatest positive hazard-ratio fold-changes. These genes were associated with increased risk. Conversely, genes to the left above the threshold line, including MAPK8, have a negative hazard-ratio fold change and were thus associated with decreased risk. Overall, 15 genes were found to be significant negative markers, and the remaining 39 genes were significant positive markers determined for the WNT signalling pathway.Next, we wanted to obtain a more comprehensive picture of the significant genes within a pathway. Using the WNT pathway as an exemplar, the volcano plot in n = 26) and low (n = 303) groups by scanning the group size from 10–90 to 90–10 percent splits, where 10–90 means that 10% of the patients were in the low group and 90% of the patients in the high group and calculating the p-value for the overall event-free survival difference between the groups using a log-rank test with Yates’ correction. A high expression of DVL3 resulted in poor patient survival, where a 2-year event-free survival equals 38% in the high group (n = 26). The Kaplan–Meier curves for the most significant gene found for each CRC STN is shown in Plotting the event-free survival Kaplan–Meier curve for DVL3, the most significant gene in the WNT signalling pathway, resulted in a and 2.29 . Kaplan–p-value of Across all of the CRC signalling pathways considered, 75 of 284 proteins exhibited expressions that correlated with event-free patient survival. For each pathway, about 4–10% of the pathway proteins exhibited an association with event-free survival. The top two significant proteins across each CRC signalling pathway are shown in Six significant proteins within the WNT signalling pathway were found from the proteomics dataset, including PRKCA, ROCK2, CSNK2A1 , LRP1 (low-density lipoprotein receptor-related protein 1), CSNK1A1 , and GPC4 (glypican 4). This is represented by the volcano plot in p-value of n = 55). The Kaplan–Meier curves for the most significant proteins found for each CRC STN are shown in Plotting the event-free survival Kaplan–Meier curve for PRKCA, the most significant protein in the WNT signalling pathway, resulted in a Across five critical CRC signalling pathways, the results from the WNT signalling pathway introduced several significant genes and proteins that have not yet been accounted for in current WNT mechanistic models. Therefore, the results of this section reinforce the need to propose a strategy to incorporate them into current WNT mechanistic models. To summarise, Having identified the significant genes and proteins within the pathways, we now sought to obtain activity scores for each pathway. The purpose was two-fold. Firstly, to identify pathway activities that correlate with event-free patient survival; and secondly, to relate the genes and proteins expression of a pathway to its activity score. The RStudio package function PROGENy was used to obtain pathway scores from the RNA sequencing data of the TCGA legacy dataset. Albeit PROGENy’s composition of 14-cancer relevant pathways, specifically for this study, only the CRC pathways were analysed in depth. The only available CRC pathways in PROGENy were PI3K, MAPK, TGF-Beta, WNT, and p53. The PROGENy activity scores for each CRC signalling pathway are shown in p-value of 1.9731 and 0.0013, respectively).To investigate the correlation between the PROGENy pathway activity scores and patient event-free survival, a Kaplan–Meier analysis and log-rank test were used. The activities of the WNT, PI3K, TGF-Beta, and MAPK pathways were all significantly correlated with event-free survival . In partp-value of The event-free survival Kaplan–Meier curves for the CRC STNs MAPK, TGF-Beta, PI3K, p53, and WNT, associated with patient survival through PROGENy activity scores, are shown in Having ascertained the significant genes, proteins, and pathways, we next sought to determine how well the expression of genes and proteins of a pathway can predict its activity scores. The results should give valuable insight into which candidate genes and proteins should be prioritised in follow-up studies focusing on constructing patient-specific mechanistic models with significant value. The WNT STN was the most significant pathway ; thus, wp-value. A p-value less than 0.05 was taken as significant for this field of study. The RMSE is an appropriate measure of predictive power for this study because model complexity was not an issue, and RMSE measures the differences between predicted and actual PROGENy values. There is no absolute threshold; however, lower values indicate a better overall fit of how accurately the model predicts the response. An acceptable range for the RSME values is difficult to define, but Model 1, consisting of the genes of the current mechanistic model, can act as a baseline benchmark. Notably, the predicted values of Model 2 show a much better correlation with the true values and a lower RMSE value compared to Model 1. : DKK3, FZD5, NKD1, NOTUM , WNT11, PRKCA, and ROCK2.Model 3: Features are the protein expression of seven proteins; five identified using LASSO and the two most significant proteins : CTBP1 , CTBP2 , GPC4, PLCB4 (phospholipase c beta 4), and RAC1 (ras-related C3 botulinum toxin substrate 1).In total, three machine learning models were developed to predict the PROGENY pathway scores using different features as inputs. The features were the genes and proteins of the WNT pathway and their associated gene or protein expression values. To minimise the risk of overfitting, 10-fold cross-validation was used to build each model. The predictive power of each model was judged based on the root mean squared error (RMSE), the correlation coefficient (R), and the Model 1. B; thus, Model 1 was developed based on the mechanistic WNT signalling pathway. The selected features were the eleven genes of the mechanistic models in the literature ,30,31,32p-value of p-value of p-value, correlation coefficient, and lower RMSE. Similarly to Model 2, Model 3 used LASSO to identify proteins from the canonical WNT pathway. In total, data for only 27 proteins were available due to missing data and imputation . Model 3 is the least predictive model. The associated Spearman correlation coefficient, RMSE, and p-value were 0.4095, 1.0509, and How well can the mechanism-based, mRNA-based, and protein-based models predict the pathway scores? Model 1, based on the genes of the mechanistic models, serves as a benchmark and achieved a reasonable predictive power with a Spearman correlation coefficient of 0.5241, a ficients D–F.By incorporating the results from the event-free survival analysis, the PROGENy analysis, and the development of the linear regression machine learning models together, it is apparent that each finding paves the way to a valuable tool for predicting the heterogeneity of the WNT STN activity on an individual patient level. Concretely, each finding from each analysis builds sequentially upon another. The event-free survival analysis brings forth critical and novel features for the WNT pathway that have not yet been accounted for mechanistically. Interesting, from the PROGENy analysis, we found that the WNT signalling pathway was the most significant pathway associated with event-free patient survival. This finding solidifies the idea that there is a relationship between this pathway and patient survival that should be continued to be researched mechanistically. Finally, the motivation for using linear regression machine learning was particularly because it is an appropriate tool to predict WNT pathway activity from specified features  on an induvial patient level. Precisely, the machine learning models additionally identified several features that were not only significantly associated with event-free patient survival, but also important for predicting the patient-specific WNT pathway activity scores. Thus, by incorporating such features into an updated mechanistic model of the canonical WNT STN activation, one would expect to better understand the patient-specific differences in the control of pathway activity, but also to relate pathway activity to clinical outcomes on the level of individual patients.Our experimental workflow employed numerous Kaplan–Meier survival scans, a PROGENy analysis, and developed three machine learning models based on mechanistic WNT pathway models and LASSO identified genes and proteins of the extant WNT network. The models can serve as a platform for improving current mechanistic models and WNT pathway activity-based patient stratification. Conversely, all models of the WNT signalling pathway to date have focussed on a few genes and proteins of the core pathway ,30,31,32This study determined novel genes and proteins significantly associated with event-free survival in CRC across the major five CRC signalling pathways, WNT, PI3K-Akt, TP53, MAPK, and TGF-Beta. In total, 389 genes and 75 proteins were found to be significant across all signalling pathways from a sum of 872 genes and 284 proteins. A total of 59 of these features were significant in the WNT signalling pathway, and interestingly, only seven of these significant features, DVL3, DKK1, SFPR2, WNT3A, WNT3, SFPR1, and GSK3B, are found within previous mechanistic WNT models ,30,31,32p-value of p-value of 2.7692 and For the WNT signalling pathway, the number of significant genes determined from RNA sequencing was 53. The top two significant genes found were DVL3 and VANGL2. The most significant gene was DVL3, with a corresponding hazard ratio of 2.8158 and a -catenin . Previou-catenin . Additio-catenin . Our stueceptors . Furthereceptors . TogetheFinally, the results for the WNT pathway analysis identified several significant genes and proteins that were associated with event-free survival but have not yet been accounted for in current WNT mechanistic models. Most, if not all, models to date are based on a few intracellular and extracellular components of the core WNT pathway ,30,31,32The second part of this study consisted of a PROGENy pathway activity score analysis. There were several reasons why PROGENy and not classical GSEA was performed. One major reason PROGENy does not require predefined groups for comparisons, unlike GSEA, which determines whether a defined set of genes shows statistically significant, concurring differences between two groups, for example, normal and tumour tissue ,48. PROGz-scores quantifying standardised  relative differences, yet all patients might have high WNT activity in absolute terms.An advantage of the PROGENy analysis is that it reveals patient-specific differences, showing that different pathways are active in different patients . InteresThe results correlating the PROGENy pathway activity scores to patient event-free survival show that the WNT, PI3K, TGF-Beta, and MAPK pathways, when activated, were significantly associated with event-free patient survival. It is very interesting to note that the WNT signalling pathway was the most significant pathway associated with event-free patient survival. These results are in line with the literature, where high WNT pathway activation was associated with shorter survival ,30,31,32The final part of this study developed three linear regression machine learning models with the aim to predict the PROGENy WNT pathway activity scores. Current mechanistic models describe the pathway components and provide deterministic insights into how the WNT pathway is activated but neglect patient-specific differences ,30,31,32Overall, Model 2 was found as the most predictive model. In comparison, the RMSE of Model 2 was 0.3751 times smaller than Model 1. Five features were identified that were both prognostic of event-free patient survival and predictive of WNT pathway activity, including DVL3, PRKCA, VANGL2, GPC4, and ROCK2. Of these, DVL3 was the only feature found in the mechanistic model, Model 1. The finding also highlights a lack of features in current mechanistic models that can explain patient-specific differences. Concretely, solidifying the need to propose such a strategy to incorporate them. In particular, our work identified the four overlapping features above  that should be the focus of mechanistic modelling in the future. Machine learning, Model 2, accounting for such features, outperformed the current mechanistic model and can accurately predict the WNT pathway activity scores.Interestingly, the protein PRKCA was found to be both significantly associated with event-free survival and a significant predictive feature of the WNT pathway activity score . The resIn conclusion, we developed machine learning models of the activity of the canonical WNT signalling pathway and incorporated significant genes and proteins that are associated with event-free patient survival. This model is a valuable tool for predicting the heterogeneity of WNT pathway activity on an individual patient level. In this study, we identified several features  that were not only significantly associated with event-free patient survival but also important for predicting the patient-specific WNT pathway activity scores. We expect that integrating these features into mechanistic models of WNT pathway activation in the future will not only help to better understand the patient-specific differences in the control of pathway activity but also to relate pathway activity to clinical outcomes on the level of individual patients.All data used throughout this study was open-source data. The first datasets were from the 2016 TCGA Colorectal Adenocarcinoma, GDAC Firehose Legacy study, previously known as TCGA provisional. The datasets used for this study included Genomic from RNA sequencing , proteomic from mass spectrometry (5562 proteins), and the associated clinical dataset ,40. The The five critical CRC STNs used in this study stemmed from the literature ,11,23. Tp-value for the overall event-free survival difference between the groups using a log-rank test with Yates’ correction. The cut-offs were based on the TCGA legacy datasets: RNA sequencing (329 patients) and proteomics (73 patients), and the CPTAC RNA dataset (79 patients). The inputs for the first two simulations were based on the gene or protein sets for each CRC STN identified above. The output was a Kaplan–Meier curve for the gene or protein expressed, indicating the number of patients in the high expression or low expression group with the corresponding statistical values apparent. The inputs for the final simulation were the PROGENy CRC pathway activity scores, and the output was 5 Kaplan–Meier curves for each CRC STN. The number of patients in the high activity or low activity group was identified with the corresponding statistical values. All statistical computations and Kaplan–Meier analyses were performed in MATLAB (version R2020b Update 5 (9.9.0. 1592791), The MathWorks, Inc., Natick, MA, USA) using the statistics toolbox and the log-rank ) and kmplot ) functions from the MATLAB (version R2020b Update 5 (9.9.0. 1592791), The MathWorks, Inc., Natick, MA, USA) file exchange [The Kaplan–Meier survival analysis was simulated three times, the first to find the prognostic genes from RNA sequencing, the second to find the prognostic proteins from proteomics, both using the TCGA legacy datasets, and finally to determine the associations between PROGENy pathway activity scores and event-free survival using the RNA CPTAC dataset. The optimum cut-off for stratifying the patient populations into low and high groups was identified by scanning the group sizes from 10–90 to 90–10 percent splits, where 10–90 means that 10% of the patients were in the low group and 90% of the patients were in the high group and calculating the exchange .http://www.rstudio.com/) [PROGENy is a machine learning based tool installed from Bioconductor as an RStudio package (RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA URL io.com/) ,49. A PRio.com/) ,49. The io.com/) ,49.p-value) of 0.05 was used to determine if a feature was significant. For the proteomics dataset, this resulted in 5/27 features (proteins) found to be significant, including CTBP1, CTBP2, PLCB4, PRKCA, and RAC1. Similarly, for the RNA sequencing dataset, 5/89 features (genes) were significant, DKK3, FZD5, NKD1, NOTUM, and WNT11. All associated datasets are found within the The three linear regression machine learning models developed in the study were performed in MATLAB using the regression learner app . The thr


Coptis chinensis (C. chinensis) is used to clear heat and dampness, purify fire, and detoxify in traditional Chinese medicine (TCM). Although C. chinensis has demonstrated efficacy against PCOS in clinical practice, there are no available data regarding the bioactive components of C. chinensis, their targets, and molecular mechanisms underlying their effects.  Polycystic ovary syndrome (PCOS) causes low fertility in females. C. chinensis, their targets, and signaling pathways underlying their effects. The TCM systems pharmacology database and analysis platform (TCMSP) was used to screen 14 effective active ingredients and 218 targets of C. chinensis. The GeneCards, OMIM, and PharmGkb databases were used to screen 3517 disease targets for PCOS, and 102 common targets of drugs and diseases were screened using R Cytoscape that was utilized to build a drug-active ingredient-disease target interaction network, and the STRING platform was utilized to construct a common target protein-protein interaction network, including 102 nodes and 221 edges. Key targets of C. chinensis for the treatment of PCOS included JUN, MAPK, IL6, CXCL8, FOS, and IL1B. A total of 123 gene ontology (GO) terms and 129 pathways were acquired by GO and KEGG enrichment analyses. The AGEs/RAGE, TNF, IL-17, MAPK, and HIF-1 signaling pathways were closely related to PCOS and may be the core pathways involved in PCOS. Schrodinger software was used to evaluate the interaction between active components and their targets and explore binding modes. Furthermore, based on the prediction of network pharmacology study, a mouse model of PCOS was established to evaluate the curative role and underlying mechanisms of C. chinensis. The results showed that C. chinensis treatment reversed histopathological damage of the ovary and also ameliorated the mRNA and protein expression levels of the predicted hub targets . These results indicated that WZYZP has a protective effect on spermatogenesis disorder, suggesting that it could be an alternative choice for male infertility therapy.  Network pharmacology was used to analyze the bioactive components of C. chinensis, a TCM, in the treatment of PCOS. These results indicate that the therapeutic effects of C. chinensis on PCOS may be achieved by regulating the expression of inflammatory factors. This study provides new insights for the systematic exploration of the mechanism of traditional Chinese medicine. This preliminary study verified the basic pharmacological effects and mechanisms of  Polycystic ovarian syndrome (PCOS) is an endocrine syndrome characterized by persistent anovulation, hyperandrogenemia, or insulin resistance and is the most common cause of infertility in women of childbearing age . In receCoptis chinensis (C. chinensis) is a commonly used botanical medicine with a long history and is widely used to treat various diseases, including gynecological diseases and diabetes. In the theory of Chinese medicine, C. chinensis can clear away heat and dampness, purify fire, and detoxify the body. Pharmacological studies have confirmed that C. chinensis has anti-inflammatory, antiallergenic, hypoglycemic, lipid-lowering, and antioxidant properties [C. chinensis in the treatment of PCOS still has shortcomings such as low sensitivity, single reliable evaluation index, and inability to systematically evaluate it. It is difficult to make a scientific, effective, and comprehensive explanation of its mechanism of action. Therefore, it is important to systematically and comprehensively study the material basis and mechanisms of action of C. chinensis to improve and treat PCOS.In China, operties , 6. Of noperties , 8. HoweNetwork pharmacology is based on the theory of systems biology. A model is established for predicting the relationship between drugs and targets, diseases and therapeutic targets, integrating the network of interactions between them, and analyzing the interaction of active ingredients and targets in the network module. It is a systematic and holistic approach to the study of the relationships between active pharmaceutical ingredients and potential targets , 10. It C. chinensis) and explore its possible targets and mechanisms of action in treating PCOS. Further, an experiment model was established to evaluate the effect of C. chinensis on PCOS. The flow chart of the current study is shown in In this study, a network pharmacology method was used to analyze the active ingredients of a TCM (https://lsp.nwu.edu.cn/tcmsp.php); Uniprot database (https://www.uniprot.org); GeneCards database (https://www.genecards.org/), OMIM database (https://www.omim.org/), PharmGkb database (https://www.pharmgkb.org/); functional protein contact network (STRING) database (https://string-db.org/); Cytoscape 3.7.1 (https://cytoscape.org/); R language software Rx64 3.6.2 (https://www.r-project.org/); Bioconductor (https://www.bioconductor.org/), an R plug in source; and Microsoft Windows 10 Home operating environment.The following databases and resources were used: Chinese Herbal Medicine System Pharmacology Platform (TCMSP) database  ≥ 30% and drug-likeness (DL) ≥ 0.18 as the limiting conditions to obtain the biologically active components of C. chinensis.TCMSP, a traditional Chinese medicine (TCM) ingredients database, is commonly used for researching Chinese medicine network pharmacology. It contains more than 500 herbal medicines and more than 3,000 kinds of Chinese herbal medicines. It has collected more than 3300 targets of ingredients from databases such as DrugBank and HIT. TCMSP was used to search all the active ingredients in the traditional Chinese medicine, C. chinensis were collected from the TCMSP database. For the convenience and standardization of data processing, the Uniprot database was used to uniformly convert the “protein name” of the target into the “gene name.”Targets of effective active ingredients of Using the search term “Polycystic Ovary Syndrome,” the databases GeneCards, OMIM, and PharmGkb were interrogated. These platforms are comprehensive disease-related data platforms, and there are a wide range of data related to complex diseases, including data literature, and experimental verifications. Search results from the three platforms were refined to obtain the disease targets of PCOS.R software was used to obtain the common target of drugs and diseases, and a Venn diagram was drawn. The STRING platform was used to construct the common target protein-protein interaction (PPI). The minimum interaction threshold was set to “highest confidence” (>0.9). “Node” was used to represent different targets, and “edge” represented the relationship between different targets. R software was used to count the frequency of occurrence and to produce a histogram. All R packages were downloaded via the R and Bioconductor websites.https://cytoscape.org/) software was used to build a drug-active ingredient-disease-target interaction network. In the resulting network diagram, the software defines nodes and edges. “Node” represents drugs, active ingredients, diseases, and targets, and “edge” represents the relationship between the above nodes.Cytoscape 3.7.1 (htps://www.omicshare.com/tools), and the results were presented as bubble charts. P was defined as significant at a value < 0.05. The degree of enrichment of core pathways was analyzed based on the value of the enrichment factor to explore the possible mechanism of C. chinensis in treating PCOS. In addition, the ClueGo plugin in Cytoscape 3.7.1 software was used to perform GO analysis on common targets of drugs and diseases, and the results were presented as an interaction network and a pie chart.GO and KEGG data were analyzed using OmicShare tools (https://pubchem.ncbi.nlm.nih.gov/) was used to collect the 3D structure data of quercetin (CID 5280343), berberrubine (CID 72703), berberine (CID 2353), and canadine (CID 34458). PDB data were used for the structures of MAPK1 (PDBID: 6G54), CXCL8 (PDBID: 5D14), IL-6 (PDBID: 4O9H), and IL-1β (PDBID: 4G6M). Schrodinger software  was used to pretreat compound structures and generate multiple conformations. Protein structures were processed by removing hydrone and adding hydrogen atoms, and the Sitemap module was used to explore and define binding sites. The ligand-dock module was used to simulate the molecular docking of compounds and proteins, and the docking score was evaluated using the Docking score function. Simultaneously, the visual analysis module was used to observe the binding postures of molecules and their targets.The interaction between active components and their targets was evaluated to explore the binding modes; four components and four targets were chosen. PubChem (Coptis chinensis (10g). And the dry whole body of Coptis chinensis granules was provided by the department of TCM granules in Zhejiang Integrated Traditional and Western Medicine Hospital according to the Chinese Pharmacopoeia (2015) and prepared in the Huisong Pharmaceuticals Co., Ltd . The final prepared WZYZP was sealed and stored in a dry and cool location.The CCG was composed of C. chinensis composition. Chromatographic analysis was performed using an ACQUITY BEH C18 column  on a Waters ACQUITY UHPLC system with a binary solvent manager at a flow rate of 0.3 mL/min, sample injection volume of 1.0 μL, and column temperature of 30°C. The MS analysis was conducted on a Waters G2 QTOF TM system  operated using software.Ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS/MS) was used to verify the Healthy female C57BL/6N mice , weighing 16–20 g, were obtained from the Animal Center of Zhejiang Chinese Medical University , certificate No.: SYXK (Zhejiang province) 2021–0012. The experimental procedures were performed based on the Zhejiang Chinese Medical University Committee on Laboratory Animals (protocol number 202103-0079 and 20210308-05). All mice were maintained under the conditions  and were provided water and chow ad libitum in the laboratory environment.Coptis chinensis 2.7 group (C. chinensis 2.7 group) and PCOS + Coptis chinensis 5.4 group (C. chinensis 5.4 group), 6 mice each group. The PCOS and PCOS + Coptis chinensis (2.7 and 5.4 g/kg groups) were established by the oral administration of letrozole solution at the dose of 1.0 mg/kg for 25 days. In addition, the normal control group was orally given normal saline. At the beginning of the experiment, the mice were weighed every other day, and the changes in body weight were observed for 21 days. After the establishment of the PCOS model, mice in the PCOS + Coptis chinensis (2.7 and 5.4 g/kg groups) were treated with Coptis chinensis 2.7 g/Kg/d and 5.4 g/Kg/d for 25 days.A total of 24 female C57BL/6N mice were randomly labeled into 4 groups as follows: normal control group, PCOS group, PCOS + μm sections. Next, the slides were stained by hematoxylin and eosin. Subsequently, the slices were dehydrated in 95, 90, and 70% ethanol, cleared in xylene. Finally, the slides were observed under the microscope .Using hematoxylin and eosin (H&E) staining, ovarian morphology was observed. Ovarian tissues were fixed in 4% paraformalin overnight at room temperature, embedded in paraffin, and cut into several 4 β. The forward and reverse sequences are described in Supplementary Use the reverse transcription method to prepare cDNA (needed for experiment) according to the manufacturer's instructions. The expected detection indicators in this experiment are MAPK1, CXCL8, IL-6, and IL-1μg) using SDS-PAGE and electrotransfer to a polyvinylidene fluoride membrane. After blocking with 5% skim milk, the membrane was incubated overnight at 4°C with the following antibodies: anti-MAPK1 , anti-CXCL8 , anti-IL-6 , anti-IL-1β , and anti-GADPH . The membrane was further incubated with the secondary antibody; ECL-Plus chemiluminescence was used to detect immunoreactive bands.100 mg of ovarian tissue from each of the four groups was homogenized with RIPA lysis buffer for 30 minutes on ice, and the total protein concentration was measured using the bicinchoninic acid assay. Separate the protein sample . Statistical analysis was performed with SPSS 18.0 software. Statistical significance between groups was calculated by one-way analysis of variance (ANOVA) followed with post hoc LSD test. C. chinensis. According to the screening conditions, there were 26 components with OB ≥ 30%, 31 kinds with DL ≥ 0.18, and 14 kinds of effective serum active ingredients with OB ≥ 30% and DL ≥ 0.18. These included berberine, obacunone, berberrubine, epiberberine, canadine, berlambine, corchoroside A, magnograndiolide, palmidin A, palmatine, quercetin, coptisine, worenine, and moupinamide  can be used as potential targets for C. chinensis in treating PCOS.There were 102 nodes and 221 edges in the interaction network . The freC. chinensis, the target on which the component act, and the target of the disease were input into Cytoscape 3.7.1 to build a drug-active, component-disease-target, interaction network . A bubble chart was drawn, using the OmicShare tools (http://www.omicshare.com/tools)  . The GO platform . Excludihttps://cytoscape.org/) software, the ClueGo plugin was used to perform GO term analysis on the common targets, and an interactive network and pie chart were obtained. The degree of correlation between the enriched biological processes and the number of genes included was described previously  were studied through molecular docking. A score of <5 points indicates that the compound has excellent docking activity with the target. Docking scores of quercetin with MAPK1 (−6.894), CXCL8 (−5.185), IL-6 (−5.864), and IL-1β (−6.048) were the highest among the selected components. Quercetin forms covalent bonds with the ALA35, ASP111, ASP167, MET108, and LYS114 amino acid residues of MAPK1  and cellular inflammatory pathway related factors . However, there was no significant difference in body weight between the model group and the treatment group  increased , and the mRNA expression of IL-1β was also significantly higher than that in the NC group (P < 0.05). After drug intervention, the mRNA expression of MAPK1 and CXCL8 in the PCOS + C. chinensis 2.7 g group was lower than that in the PCOS group (P < 0.05); the mRNA expression of MAPK1, CXCL8, and IL-6 in the PCOS + C. chinensis 5.4 g group was significantly lower than that in the PCOS group (P < 0.01), and the mRNA expression of IL-1β was also lower than that of the PCOS group (P < 0.05). In addition, we used Western Blot to detect MAPK1, CXCL8, IL-6, and IL-1β in mouse ovarian tissue Protein expression level . After treatment, compared with the PCOS model group, the relative protein expression levels of CXCL8 in the PCOS + C. chinensis 2.7 g group were significantly reduced (P < 0.05), and the relative protein expression levels of MAPK1, CXCL8, IL-6, and IL-1β in the PCOS + C. chinensis 5.4 g group were significantly reduced (P < 0.05).Combining the PPI network interaction targets and molecular docking results of the common drug-disease target, we selected inflammation-related genes as verification targets and used qRT-PCR to detect the expression levels of MAPK1, CXCL8, IL-6, and IL-1β mRNA . We obse Figures . We obseCoptis chinensis is a representative medicine for clearing away heat and dampness in TCM. At present, most clinical studies focus on the effect of the berberine extract component (“berberine”), but pharmacological studies have found that a Huang Lian (Chinese Herb) decoction is better than berberine monomers in improving insulin resistance, lowering blood sugar, and improving glucose and lipid metabolism disorders [C. chinensis, many ingredients often participate in the process of exerting its effects, and the mechanisms of action are intertwined with each other. Therefore, it is necessary to conduct a comprehensive analysis on the relevant network pharmacology software platforms to interpret the mechanisms of action.Polycystic ovary syndrome is an endocrine syndrome associated with ovulatory disorders and metabolic abnormalities. Since the main external characteristics of the disease are infrequent menstruation and prolonged infertility, in TCM, PCOS is mostly classified as “late menstruation,” “less menstruation,” “amenorrhea,” “collapse,” and “infertility.” The etiology and pathogenesis of the disease (in TCM) involve kidney-Tiangui-Chongren-cycle axis dysfunction, phlegm dampness, and obstruction in the middle focus, with heat over time, resulting in kidney, liver, and spleen dysfunction. isorders , 13. DueC. chinensis were found to include various organic components such as berberine, berberine, quercetin, canadine, and berberrubine. The core nodes in the “target” network suggest that these several active ingredients play an extremely important role in the treatment of PCOS. Other related studies have also verified that several components in C. chinensis improve follicular developmental disorders, ovulatory disorders, and lower fasting blood glucose and fasting insulin levels to reduce insulin resistance in women with PCOS [In this study, the main active ingredients of ith PCOS , 15, conith PCOS . In addiThrough analysis of drug and disease targets, it was found that quercetin has 139 targets, which are active ingredients with many targets. Quercetin is a natural flavonoid, which can inhibit the activity of aldose reductase and has the effect of reducing the advanced glycation end products (AGEs) and insulin resistance. It has attracted the attention of researchers and clinicians . The useC. chinensis in treating PCOS. A mitogen-activated protein kinase pathway (MAPK) is involved in the regulation of oocyte maturation, and this effect is essential for normal follicular development and proper ovulation [FOS gene in granular cells may increase the androgen secretion of ovarian granular cells by reducing the inhibitory effect on the CYP17 gene [β, suggesting that the compound may play a therapeutic role by combining with these proteins.Analysis of the PPI network indicated that MAPK, JUN, IL-6, CXCL8, and FOS are at the core of the network and can be regarded as potential key targets for vulation . Once thvulation , 20. Expvulation . The abnP17 gene . The resC. chinensis can alleviate ovulation disorders in mice, improve the quality of follicles, and reverse ovarian histopathological damage. C. chinensis also significantly reduces the expression of inflammatory factor proteins and mRNA and reduces the level of cellular inflammation in ovarian tissue. These results indicate that C. chinensis has a therapeutic effect on polycystic ovary syndrome by reducing ovarian inflammation. And we speculate that the biologically active compounds screened from C. chinensis may significantly promote this effect, which requires further verification by subsequent experiments.Further experimental studies on C57BL/6N model mice show that C. chinensis, which confirmed that C. chinensis has certain effects on biological processes such as apoptosis, cytokine activation, oxidant antioxidant, and nitric oxide metabolism. The process is reflected in the targets and related pathways involved in the treatment of PCOS by C. chinensis. In the KEGG enrichment analysis, C. chinensis was shown to regulate various PCOS pathogenesis-related signaling pathways including AGEs/RAGE, MAPK, PI3k/Akt, and TNF. Studies have shown that the high expression of AGEs plays a key role in the formation of PCOS. AGEs mainly bind to specific receptors (RAGE) and mediate a series of pathological responses through cellular signal transduction mechanisms have found that, after binding to AGEs, RAGE can inhibit the activation of phosphatidylinositol 3-kinase (PI3K/Akt) and the expression of related genes in the course granule cells, so that the glucose transporter (GLUT4) can be transferred to the plasma membrane. Obstacles, inhibition of glycogen synthesis, inhibition of glycolysis, promotion of gluconeogenesis, inhibition of protein synthesis, and changes in gene expression promote elevated blood glucose, severe hyperinsulinemia, and IR. In the process of follicular development, oocytes find it difficult to synthesize glucose, and most of their energy comes from granulocyte glycolysis [Coptis chinensis in treating PCOS.ClueGO biological process enrichment analysis was performed on the key targets of the active components of ycolysis . Therefoycolysis . In addiycolysis , 19. TheC. chinensis in the treatment of PCOS. Through bioinformatics analysis, a total of 102 common targets related to C. chinensis and PCOS were identified. Network analysis identified MAPK1, JUN, IL-6, CXCL8/IL-8, FOS, IL1B, ESR1, and EGF as the central targets and revealed that the cellular inflammation pathway is a key pathway, which has multiple signaling pathways and multiple targets. The molecular docking results showed that the biologically active components in C. chinensis inhibit cell inflammation by regulating the expression of MAPK1, IL-6, CXCL8, and IL1B, thereby exerting a therapeutic effect on PCOS. In addition, in vivo experiments have shown that C. chinensis treatment can improve polycystic ovary syndrome by improving the quality of follicles and reversing ovarian histopathological damage, reducing the expression of cellular inflammation-related proteins  in ovarian tissue. Therapeutic effect: based on network pharmacological analysis and experimental verification, this study shows that C. chinensis is effective in reversing polycystic ovary syndrome, suggesting that it may be an alternative to the treatment of PCOS. However, further in vitro and in vivo studies are still needed to clarify the role and complex mechanisms of C. chinensis in the treatment of polycystic ovary syndrome.Our research uses a combination of methods including network pharmacology, molecular docking, and in vivo experiments to systematically explore the mechanism of action of


The Chinese patent drug Yinzhihuang granule (YZHG) is used to treat hepatitis B. This research is aimed at exploring the multicomponent synergistic mechanism of YZHG in the treatment of inflammation-cancer transformation of hepar and at providing new evidence and insights for its clinical application.  To retrieve the components and targets of Yinzhihuang granules. The differentially expressed genes (DEGs) of hepar inflammation-cancer transformation were obtained from TTD, PharmGKB, and GEO databases. Construct the compound-prediction target network and the key module network using Cytoscape 3.7.1.  The results show that hepatitis B and hepatitis C shared a common target, MMP2. CDK1 and TOP2A may play an important role in the treatment with YZHG in hepatitis B inflammatory cancer transformation. KEGG pathway enrichment showed that key genes of modules 1, 2, and 4 were mainly enriched in the progesterone-mediated oocyte maturation signaling pathway and oocyte meiosis signaling pathway.  The multicomponent, multitarget, and multichannel pharmacological benefits of YZHG in the therapy of inflammation-cancer transition of hepar are directly demonstrated by network pharmacology, providing a scientific basis for its mechanism. Chronic viral hepatitis affects half of the people in the world, which may lead to cirrhosis and liver cancer . Liver cArtemisia scoparza Waldst. et Kit. or Artemisia capillaris Thunb. , Gardenia jasminoides Ellis , Scutellaria baicalensis Georgi , and Lonicera japonica Thunb. . It has the effects of clearing heat and detoxifying, normalizing the gallbladder to cure jaundice. It is widely used in neonatal jaundice, viral hepatitis, drug-induced hepatitis, intrahepatic cholestasis of pregnancy, alcoholic liver disease, and ABO maternal-fetal blood group incompatibility. Currently, YZHG is widely used in the treatment of viral hepatitis in clinic [YZHG is a preparation of traditional Chinese medicine made from the extracts of n clinic , 10. Basn clinic –14. The http://www.ncbi.nlm.nih.gov/GEO/) is an international public database that can freely store and obtain high-throughput gene expression and other functional genomic datasets. The included datasets meet the following conditions: (1) human liver tissue samples, including human normal liver tissue samples and hepatitis samples infected with HBV; chronic HBV-related HCC adjacent normal tissue and chronic HBV-related HCC liver tissue; and liver cirrhosis group and liver cancer tissue samples. (2) Each dataset contains at least 3 samples. The “limma” package of R 4.1 software was used to identify DEGs when ∣log2FC | ≥1 and adj.P < 0.05 were considered to be DEGs [The Gene Expression Omnibus database   is a database containing known targets, explored therapeutic proteins, disease targets, nucleic acid targets, pathway information, and corresponding drugs. The keyword “hepatitis C” was used to search for hepatitis C-related targets in TTDThe Therapeutic Target Database   is applied to collect, guide, and disseminate knowledge on clinical operable gene-drug associations and genotype-phenotype relationships. The keyword “hepatitis C” was screened in PharmGKB to obtain known hepatitis C-related targetsPharmacogenomics Knowledgebase  . ChemDraw was used to depict the structures of compounds without the standard information from SMILES [A literature search in CNKI and PubMed was performed to find the chemical ingredients of YZHG in this investigation. To collect their simplified molecular input line entry specification (SMILES) information, the obtained compounds were entered into the PubChem database –23 . It can anticipate the ATC coding or target of small molecules and acquire compound information, which is useful for drug development [https://www.swisstargetprediction.ch/) is a site that uses the reverse screening similarity principle to suggest the most likely protein targets for small compounds [All compounds' SMILES information was entered into the SuperPred and SwissTargetPrediction databases search tools. The server for chemical composition, ATC code, and target prediction is the SuperPred database (elopment . SwissTaompounds . Only huompounds  was usedompounds –30.Protein-protein interaction (PPI) analysis was performed on the hepatitis C data. The 4 groups of disease data  and the common targets in the compound-putative target of the YZHG network were determined to be the potential targets in the treatment of inflammation-cancer transformation of the hepar process .https://string-db.org/) database, and the relevant information of PPI was retrieved, respectively, with the species limited to “Homo sapiens” and confidence scores higher than 0.7 . The “CytoHubba” plug-in sorts the nodes in the network according to the network characteristics and provides 11 topological analysis methods to discover the most important targets and subnetworks of complex networks [In this study, the potential targets of the above 4 groups of YZHG in the treatment of inflammation-cancer transformation of the hepar process were entered into the STRING  (https:/networks . In thishttps://biit.cs.ut.ee/gprofiler/gost) database [To elucidate the role of potential targets in gene function and signal transduction pathways, GO enrichment analysis and KEGG pathway enrichment analysis of targets in the inflammation-cancer transformation of the hepar target network were performed using the g:Profile . They were processed using AutoDock Tool 1.5.6 software, including the addition of polar hydrogen atoms and Gasteiger charges, and then, the corresponding pdbqt format was saved. The crystal structure of the protein receptor was obtained from the RCSB PDB (http://www.rcsb.org/) protein database. Water molecules and original ligands in protein receptors were removed and saved in pdb format. A script file was created containing the three-dimensional coordinates of the active sites and the number of independent docking calculations; then, polar hydrogen atoms and Gasteiger charge were added to the macromolecular receptor using AutoDock Tool 1.5.6 software, and the corresponding pdbqt format was saved [http://www.PyMOL.org) was used to view and analyze the results.The small-molecule ligands (mol2 format) of YZHG were downloaded from the PubChem database  identified 263 DEGs, hepatitis B-hepatitis B-related hepatocellular carcinoma (GSE121248) contained 798 DEGs, 72 hepatitis C protein-protein interaction data and hepatitis C-related cirrhosis-hepatitis C-related hepatocellular carcinoma (GSE17548) contained 706 DEGs. The above DEG data , molecular functions (MF), and cellular components (CC). Gene function is comprehensively defined from three different perspectives. Among them, the biological process shows the involvement of genes in different biological processes, the molecular function shows the function of the gene, and the cellular component shows the distribution of the gene in the cell. In this study, GO and KEGG enrichment analysis of the key genes obtained by 4 groups of module analysis was performed to systematically elucidate the multiple mechanisms of YZHG in the treatment of inflammation-cancer transformation of hepar . The resThe mechanism of YZHG in the treatment of inflammation-cancer transformation of hepar is reflected in the interaction between compounds and targets. Through the construction of the PPI network and module analysis, the directly related targets in the key modules, namely, CDK1, TOP2A, EGFR, and CCNB2, were selected to investigate the interaction of other corresponding compounds. The three-dimensional crystal structures of 4 targets were derived from the PDB database as their PDB codes. For CCNB2, the corresponding PDB code was not found. AutoDock Vina was applied to dock the above 3 targets and their corresponding small-molecule drug structural ligands. As shown in Supplementary Table YZHG is a commonly used drug in the clinic for the treatment of liver diseases –41. HoweAccording to the YZHG-disease association network, the hepatitis B and hepatitis C PPI data in the normal group had a common target, MMP2, with the predicted target of YZHG. The data of normal group-hepatitis B, hepatitis B-hepatitis B-related hepatocellular carcinoma, and hepatitis C-related cirrhosis-hepatitis C-related hepatocellular carcinoma all had two common targets CDK1 and TOP2A with YZHG putative targets.Matrix metalloproteinase-2 (MMP2), a member of the matrix metalloproteinase (MMP) gene family, is a zinc-dependent enzyme that can cleave extracellular matrix components and participate in signal transduction. The protein encoded by the gene is gelatinase A, type IV collagenase, and its catalytic site contains 3 fibronectin type II repeats that bind denatured type IV collagen and type V collagen to elastin. Unlike most members of the MMP family, activation of this protein can occur at the cell membrane. It can inhibit cell migration and adhesion, activate mitochondrial-nuclear stress signaling, induce the activation signal of nuclear transcription factor B, and activate T nuclear factor interferon regulatory factor, promoting vascular remodeling and angiogenesis to form tumor invasion tissues . Yang etin vitro and in vivo. HBx can inhibit the growth of hepatocellular carcinoma (HCC) cells and induce G2/M phase arrest in vitro. HBx continuously activates cyclin B1-CDK1 kinase. In vivo, HBx inhibits tumor cell growth and induces apoptosis and inhibits the vascular endothelial cell growth [CDK (cyclin-dependent kinases) is a Ser/Thr kinase system that corresponds to cell cycle progression. Activation of CDK1 can phosphorylate the target protein and cause corresponding physiological effects, such as nuclear fiber layer protein phosphorylation leading to nuclear fiber layer disintegration, nuclear membrane disappearance, and H1 phosphorylation leading to chromosome condensation. The end results of these effects make the cell cycle continuous. Hepatitis B virus X protein (HBx) is a multifunctional regulatory protein known to be involved in viral proliferation, transcriptional activation, and cell growth control . Cheng el growth .In summary, HBx induces G2/M arrest and apoptosis through continuous activation of cyclin B1-CDK1 kinase and negatively regulates cell growth in vitro and in vivo. HCV core proteins disrupt the G1/S and G2/M phases by regulating the expression and activity of several cell cycle regulators . Viral pP < 0.001), serum HBsAg (P = 0.004), and Ki-67 (P = 0.038). Ignat et al. used quantitative RT-PCR to verify the coexpression of linking genes in a group of normal liver tissues (n = 8), chronic liver diseases (n = 7), and hepatocellular carcinoma (n = 7) induced by hepatitis C virus (n = 9). TOP2A was significantly upregulated in dedifferentiated hepatocellular carcinoma and hepatocellular carcinoma with loss of chromosome 13q [TOP2A encodes a DNA topoisomerase that controls and alters the topological state of DNA during transcription. The ribozyme is involved in chromosome condensation, chromatid separation, and the reduction of torsional stress during DNA transcription and replication . It catasome 13q , 49.Molecular docking results showed that the binding ability of CDK1, TOP2A, and EGFR and their corresponding compounds was greater than that of the positive control group. Epidermal growth factor receptor (EGFR) is the receptor for cell proliferation and signal transduction of epithelial growth factor (EGF). Studies have shown that EGFR is highly or abnormally expressed in many solid tumors. EGFR is associated with tumor cell proliferation, angiogenesis, tumor invasion, metastasis, and inhibition of apoptosis. Wen et al. and Lo et al. found that EGFR has the most significant antitumor effect in treating liver cancer , 51.YZHG is used in clinical application for acute and chronic hepatitis and severe hepatitis caused by the damp-heat toxin and can also be used for comprehensive treatment of other types of severe hepatitis –54. In tin vitro and in vivo data are needed to validate these findings and optimize the method.In conclusion, the present study explored and predicted the key targets and potential mechanisms of YZHG in inhibiting the inflammation-cancer transformation of hepar by network pharmacology and bioinformatics. It is hoped that this study will lay a good foundation for further experimental research and contribute to the application of network pharmacology in exploring the potential mechanism of complex diseases. Since this study is based on data analysis, further