Chinese medicine usually acts as "multi-ingredients, multi-targets and multi-pathways" on complex diseases, and these action modes reflect the coordination and integrity of the treatment process with traditional Chinese medicine (TCM). System pharmacology is developed based on the cross-disciplines of directional pharmacology, system biology, and mathematics, has the characteristics of integrity and synergy in the treatment process of TCM. Therefore, it is suitable for analyzing the key ingredients and mechanisms of TCM in treating complex diseases. Intracerebral Hemorrhage (ICH) is one of the leading causes of death in China, with the characteristics of high mortality and disability rate. Bring a significant burden on people and society. An increasing number of studies have shown that Chinese medicine prescriptions have good advantages in the treatment of ICH, and Ditan Decoction (DTT) is one of the commonly used prescriptions in the treatment of ICH. Modern pharmacological studies have shown that DTT may play a therapeutic role in treating ICH by inhibiting brain inflammation, abnormal oxidative stress reaction and reducing neurological damage, but the specific key ingredients and mechanism are still unclear.To solve this problem, we established PPI network based on the latest pathogenic gene data of ICH, and CT network based on ingredient and target data of DTT. Subsequently, we established optimization space based on PPI network and CT network, and constructed a new model for node importance calculation, and proposed a calculation method for PES score, thus calculating the functional core ingredients group (FCIG). These core functional groups may represent DTT therapy for ICH.Based on the strategy, 44 ingredients were predicted as FCIG, results showed that 80.44% of the FCIG targets enriched pathways were coincided with the enriched pathways of pathogenic genes. Both the literature and molecular docking results confirm the therapeutic effect of FCIG on ICH via targeting MAPK signaling pathway and PI3K-Akt signaling pathway.The FCIG obtained by our network pharmacology method can represent the effect of DTT in treating ICH. These results confirmed that our strategy of active ingredient group optimization and the mechanism inference could provide methodological reference for optimization and secondary development of TCM.The online version contains supplementary material available at 10.1186/s12906-022-03831-7. Hypertensive intracerebral hemorrhage (ICH) is one of the main causes of death and disability among Chinese residents. According to the statistics, the mortality rate is as high as 35%-52%, and about 80% of the surviving patients still have disabilities within half a year , which bIn recent years, many experimental studies have been carried out on ICH. Statins can improve neurological outcome and promote neurovascular recovery after ICH . MesenchGlycyrrhiza uralensis Fisch. ex DC.(1.5 g), Zingiber officinale Roscoe (1.5 g), Citrus reticulata Blanco (2.1 g), Arisaema heterophyllum Blume (2.5 g). Panax ginseng C.A.Mey.(3 g), Pinellia ternata (Thunb.) Makino(3 g), Poria cocos (Schw.) Wolf.(3 g), Citrus acida Pers (2.5 g), Bambusa tuldoides Munro (2.1 g).The DTT prescription contains 9 herbs: The prescriptions of TCM have multiple ingredients, multiple targets and multiple pathways, which are necessary factors for treating many complex diseases. As a new subject, network pharmacology is beneficial to identify the effective ingredients and explore the mechanisms of TCM in modern pharmacology research. It is helpful to explain the "combination-effect relationship" and compatibility rule of TCM at a system level. The effect of TCM on cells and organisms is a complex biological network, which is inline with the "integrity" characteristics of systems pharmacology. Thus, system pharmacology is suitable for studying the underlying rules of TCM prescription.At present, several formula optimization methods have been proposed. Most of these methods take the structural similarity of ingredients as the most important feature but ignore the linkage of pathogenic genes and drug targets. In this study, systems pharmacology was used to detect FCIG and clarify the mechanisms of DTT in treating ICH. We present a reverse optimization model based on the association between disease genes and ingredient targets is proposed, which provides space for optimization based on effective proteins. This method can well determine the optimization space of the target. Second, reverse searching related ingredients based on the optimized space provided by effective protein. The results showed that the enrichment functional pathway of effective proteins could cover 96% of the enrichment functional pathway of disease genes.PPI data derived from Dip , HPRD 1, BioGRIDIngredients of DTT were extracted from three herbal medicine databases: TCMID and TCMSP. For all ingredients, the OpenBabel toolkit (version 2.4.1) was employed to convert the format of structure to canonical SMILES. Subsequently, the chemical properties, such as molecular weight (MW), DL (drug-likeness), Caco-2 permeability (Caco-2) and oral bioavailability (OB) were retrieved from TCMSP.Three published ADME-related modules, OB, Caco-2 and DL, were used to select the bio-active ingredients. OB (%F) refers to the relative amount and rate of absorption of drugs into circulation after oral administration. High oral bio-availability is one of the key indicators to determine the therapeutic properties of drugs. Selecting suitable ingredients with OB ≥ 30% as potential active ingredients for next step analysis. Caco-2 cell model is a human clonal colon adenocarcinoma cell, which has similar structure and function as intestinal epithelial cells, and it contains an enzyme system related to the brush border epithelium of the small intestine, which can be used to simulate the intestinal transport of drugs in vivo. The transport rate (nm/s) of ingredients in Caco-2 cells represents the permeability of intestinal epithelium. Ingredients with Caco-2 value less than -0.4 have poor permeability in intestinal epithelial cells, so we choose ingredients with Caco-2 > -0.4 as candidate ingredients. DL is used to evaluate the drug-like properties of expected compounds, which is helpful to optimize pharmacokinetics and drug characteristics, such as solubility and chemical stability. In this study, the drug-like scores of the ingredients were more than 0.18 as the selection criteria .After screening by ADME, some ingredients that do not meet the three screening criteria are also selected, because experiments have proved that they have high content and high biological activity. These high-content and high-activity ingredients were merged with ADMET screened ingredients for subsequent analysis.To obtain the targets of active ingredients in DTT, the widely used prediction tools, HitPick, Swiss Target Prediction and Similarity Ensemble Approach (SEA)  and Swism represent the number of nodes in the network; h, i and j represent nodes in the network, V represent the unit of all nodes in the network, h and i; j.h. N stands for natural number. The method can be described as follows:For calculating the importance of each node in the network, we constructed a Node importance calculation method (BCD), in which, There are m nodes in the network. Combining degree with the center degree of betweenness, BD can effectively reflect the direct relationship between nodes and neighboring nodes, as well as the control function of neighboring nodes in the whole network –19.After being quantized, BD was sorted from small to large, and was represented by a new variable Y.The new variable R represented the nodes in the network. Each R responds to its unique Y.BCD represented the important nodes selected form all nodes in the network. N represented natural number.We collected all ingredients of DTT from databases and literature. All ingredients of DTT were used to screen out potential active ingredients by using previous reported ADME models. Three public online tools were employed to identify the targets of the active ingredients. The active ingredients and their targets were used to construct CT network. The pathogenic genes were mapped to a comprehensive protein–protein interaction network to obtain a pathogenic gene regulation network. CT network and pathogenic gene regulation network were integrated and then used to find potential effect space (PES) by using novel node importance calculation model. Finally, the contribution index (CI) model was employed to optimize the FCIG in the PES, and the key functional ingredients were obtained, and the molecular mechanism of DTT on ICH was expounded.Function enrichment analysis is the basic way to understand gene function. Here, the clusterProfiler of R package were performed to conduct pathway and GO term enrichment analysis. The p value less than 0.05 were considered to be functionally related pathways and GO terms.Jiangsu Yongjian Biotech Co., Ltd provided 6-shogaol, 6-singerol, kaempferol, and sitostrol (98% purity by HPLC) . Gibco supplied fetal bovine serum (FBS) and Dulbecco's modified Eagle's medium (DMEM) . CHI SCIENTIFIC provided the mouse hippocampal HT-22 cells . Mitsubishi Gas Chemical Company, Inc. provided the hypoxic bags (Japanese). Dojindo Laboratories supplied the Cell Counting Kit-8 (CCK-8) (Japanese).2. Hypoxic bags were used to perform OGD model according to the methods in the literature [The cells were cultured in DMEM with 10% FBS, and incubated at 37 °C under 5% COterature .4 cells/well) were seeded in 96-well plates, and treated with different concentrations of 6-Shogaol, 6-Singerol, kaempferol, and sitostrol . CCK8 was superinduced to 96-well plate for 4 h. The plate reader was utilized to detect the absorbance at 450 nm.HT22 cells  is the most, followed by MOL000358 (degree = 300), MOL002495 (degree = 246), MOL013156 (degree = 217), MOL000442 (degree = 156) and MOL000432 (degree = 146). Most of these ingredients are related to inflammation and oxidation related pathways of DTT. For example, 6-gingerol has anti-inflammatory and antioxidant effects, which can improve the activities of SOD), glutathione peroxidase and catalase in organisms, eliminate hydroxyl radicals and superoxide radicals, and reduce lipid peroxides in tissues [In the ingredient target network, the average target degree of different targets is 3.86. Among the 20 weighted targets, ABCG2, ABCB1, ALOX5, ALOX15, ALOX12, etc. Interestingly, most of these targets are related to oxidation and inflammation, which have been confirmed to be related to the pathogenesis of ICH, and may indicate the potential therapeutic mechanism of DTT on ICH. For example, caspase-3 is closely related to cerebral hemorrhage and is one of the main factors of neuronal apoptosis. The activation of caspase-3 after intracerebral hemorrhage may be the mechanism of ischemia–reperfusion injury caused by secondary cerebral ischemia around hematoma . In addiThese results suggest that DTT can treat ICH synergistically by regulating inflammation and antioxidant function, which further confirms the multi-target role of DTT in ICH treatment.We constructed a C-T-P network based on PPI using the weighted pathogenic gene regulatory network of ICH and the target network of active ingredients. In this network, it contains 2786 nodes and 45,647 edges. We extracted the relationship between drug targets and pathogenic genes from C-T-P network and constructed DTT potential effect space. Node importance is an important factor to be considered in optimizing network. At present, the methods to describe node importance mainly include degree, betweenness, closeness to centrality, shortest path and so on. These methods mainly depend on the nature of a certain aspect of the network to describe the importance of nodes in the network. In this study, we designed a new network importance calculation method, which takes into account the influence and connectivity of nodes. The comparison results show that the therapeutic response protein obtained by our node importance calculation method accounts for 92.57% of the pathogenic gene enrichment pathway in go function enrichment analysis, which shows that our method has good accuracy. There are three types of proteins in DTT potential effect space. The first is the direct interaction between pathogenic genes and drug targets. We define this category as the basic common targets. The second category is the interaction of disease-specific targets. The third category is the interaction of ingredient-specific targets. In order to determine whether the therapeutic response protein we selected from DTT PES is optimal, we examined the coverage of the enrichment pathway of therapeutic response protein, common target, ingredient-specific target and disease-specific target in the enrichment pathway of pathogenic genes from the functional level. The results showed that the coverage rate of therapeutic response protein was as high as 84.9%, which was 3.1%, 80.6% and 16.8% higher than the three, indicating that the therapeutic response protein we selected had better functional coverage Fig. .Fig. 3Fun = 1) Beta-sitosterol (MOL000358), kaempferol (MOL000422), methyl linoleate (MOL001641), etc.The contribution coefficient model is constructed to optimize the effective ingredients and obtain FCIG values, which can be used to clarify the potential mechanism of DTT in treating ICH. According to the result of contribution accumulation rate Fig. , the cumThere is a high consistency between FCIG and C-T network in the number of pathogenic genes. In order to evaluate whether the genes in FCIG are related to ICH, we compared the coverage of pathogenic genes in FCIG and C-T networks. We collected the published literature and the known ICH pathogenic genes in the database, which were verified by the average score of GeneCards greater than 6. It was found that there were 389 pathogenic genes in C-T network and 348 pathogenic genes in FCIG of DTT Fig. A and B. FCIG is highly consistent with C-T network in gene function level: another index to evaluate the importance of FCIG network is determined by their functional consistency, which can be evaluated by their enrichment pathway in KEGG. The purpose is to test whether FCIG found in DTT can represent its complete C-T network. This result shows that our FCIG detection model can cover C-T network functions to the maximum extent Fig. . There iGO enrichment analysis was performed using the R software clusterProfiler package to identify the biological functions of the main targets with p values < 0.05. To further profile the combined effects of DTT, all targets that interacted with FCIG in DTT were enriched by GO enrichment analysis Fig. .Fig. 7GoDTT regulatory targets were abundantly expressed in biological processes related to inflammatory response, according to GO analysis.For example, reducing oxidative stress  leukocyte activation involved in inflammatory responses , the production of molecular mediators involved in inflammatory responses , and inflammatory responses to antigen stimulation . These results confirmed that DTT could treat ICH by reducing inflammatory reaction, reducing oxidative stress and inhibiting apoptosis , PI3K-Akt signaling pathway  and so on. More and more evidences show that these pathways and the pathogenesis or therapeutic targets of ICH, such as MAPK signaling pathway (hsa04010), mainly regulate protein synthesis, cell proliferation and apoptosis. MAPK plays a positive role in the initiation and recognition of oxidative stress, which has a positive correlation with brain injury. Using MAPK-related protein blockers can play a role in brain protection, and the effect of combining blockers is better. This suggests that multiple blocking of oxidative stress-related signaling pathways is one of the research ideas to reduce brain injury. DTT can play a role in pathological changes of cerebral hemorrhage by regulating apoptosis and oxidative stress pathway. In order to deduce the underlying mechanism of DTT on ICH, a comprehensive signal pathway was constructed at the system level. In order to figure out the location of DTT target on the pathway, the first three columns were defined as the upstream and the others were defined as the downstream of the pathway. We found that PI3K-Akt signaling pathway (hsa04151) is one of the most important pathways in the treatment of ICH with DTT Fig. .Fig. 9DiThe effects of 6-Shogaol, 6-Singerol, kaempferol, and sitostrol on OGD model were evaluated. Compared with control group, the cell viability of OGD groups were significantly reduced. Compared with control group, the effect of cell viability was significantly decreased by 31.47% in the hypoxia treated cells Fig. . HoweverReducing non-pharmacological ingredients and improving curative effects are the main objectives of formula optimization. According to the theory of Chinese medicine, different herb medicines make up prescriptions, but whether the herbs or ingredients in prescriptions are necessary, especially for certain indications, needs to be analyzed and verified. By optimizing the formula, the medicinal materials or ingredients with certain efficacy were screened out, which made the formula more clarified and the efficacy more enhanced. In order to better capture the clinical efficacy of classical prescriptions, bioinformatics and systems pharmacology methods were combined to study the coverage rate of changed targets, and the relevant functional analysis was made on the coverage of pathogenic genes. The changed targets responds to the combination changes of different herbs and different chemical ingredients in each formula. In order to find the optimal FCIG, the optimization space definition and ingredient reverse search strategy were applied to evaluate it. At present, how to optimize and obtain FCIG and deduced underlying mechanisms is the foundation of TCM research. TCM analysis focus on the holistic view and regards the body as a whole. Systems pharmacology focuses on analyzing the action mechanisms of formula from a systematic and holistic perspective, which accords with the theoretical system of TCM research. Systems pharmacology emphasizes the multi-target regulation of multiple signal pathways to promote the synergistic effect of drugs and reduce toxic and side effects. At present, systems pharmacology has been widely used in the research of TCM formulas, especially to determine the molecular mechanism of treating complex diseases with TCM formula. However, there are few reports on optimization of TCM formulas based on systems pharmacology. On this basis, a comprehensive optimization strategy of DTT based on network pharmacology is proposed, and the key ingredients of DTT in treating ICH are obtained, and the potential mechanism of these ingredients is analyzed.Our method has two advantages: 1. In the process of analyzing the treatment mechanism, network pharmacology has formed a fixed analysis rule, that is, according to the chemical properties of Chinese medicine ingredients, through screening, predicting targets and analyzing potential mechanisms of action. This flow chart solves the molecular mechanism of some prescriptions for treating complex diseases with TCM, but there is also a problem that there is no reference in the optimization space. In this paper, we identified the representative pathogenic genes of ICH, and these pathogenic genes have been reported in the literature. For example, APP, NOTCH3 weighted gene regulatory network of ICH, Zhu Bin et al. found that amyloid precursor protein (APP) mutation can cause typical pathological changes of AD and perivascular amyloid deposition. Fu Jiayu et al. showed that NOTCH3 gene mutation may cause cerebral hemorrhage by changing the structure and function of cerebral small vessels. We present a reverse optimization model based on the association between disease genes and ingredient targets is proposed, which provides space for optimization based on effective proteins. This method can well determine the optimization space of the target. Second, reverse searching related ingredients based on the optimized space provided by effective protein. The results showed that the enrichment functional pathway of effective protein could cover 96% of the enrichment functional pathway of disease genes. It is proved that our strategy of selecting effective proteins to construct the target optimization space is correct. On the basis of optimization space provided by effective protein, CI model was used to optimize the contribution degree, and finally 82kgec was optimized. The target of FCIG is closely related to the pathogenesis and functional annotation of ICH. This proves the reliability of our optimization space and CI model again.At present, network pharmacology provides a powerful tool for exploring the compatibility and action mechanism of TCM prescriptions. However, there are some limitations. For example, more main active ingredients should be considered in animal medical research of DTT. We will verify the efficacy and mechanism of active ingredients in treating ICH through in vivo or in vitro experiments. The results show that the model has good accuracy in screening FCIG in TCM formula and provides reference for optimization and mechanism deduction of TCM formulas.A network pharmacology model-based bioinformatics algorithm was established to obtain functional core ingredients group and decode the mechanisms of DTT in the treatment of ICH. Compared with other published work, potential effect space construction strategy based on novel node importance calculation method and validation strategy and maximum targeting weight model for mechanism speculation were reported. In addition, this new systemic pharmacology model closely combines the representative pathogenic genes of ICH, which can reflect the incidence of ICH well, and can effectively screen out the core ingredients of DTT in treating ICH.Results of the in vitro validation that 6-Shogaol, 6-Singerol, kaempferol, and sitostrol possessed protect effect in hypoxia treated HT22 cells demonstrated that the core ingredient group selected by us based on system pharmacology had significant effect on ICH.Our research is a computational mining work based on pharmacological basic data, which provides a feasible scheme to reduce the verification scale for the experiment, provides methodological reference for optimization of core ingredients group and interpretation of the molecular mechanism in the treatment of complex diseases using TCM. Our model has been proved to be effective in the compound optimization of DTT for the treatment of ICH. In future studies, we hope to apply this model to more studies and get better improvements, so as to provide methodological reference for the treatment of ICH with Chinese medicine.Additional file 1:Supplementary Table 1. Protein-Protein Interaction Network.


Cordyceps. This study sought to explore the mechanism underlying the therapeutic effect of Cordycepin against Alzheimer’s disease using network pharmacology and molecular docking technology.Cordycepin is a nucleoside adenosine analog and an active ingredient isolated from the liquid fermentation of TCMSP, SYMMAP, CTD, Super-pred, SEA, GeneCards, DisGeNET database, and STRING platform were used to screen and construct the target and protein interaction network of Cordycepin for Alzheimer’s disease. The results of Gene Ontology annotation and KEGG pathway enrichment analysis were obtained based on the DAVID database. The Omicshare database was also applied in GO and KEGG pathway enrichment analysis of the key targets. The protein–protein interaction network was constructed using the STRING database, and the potential effective targets for AD were screened based on the degree values. The correlation between the potential targets of Cordycepin in the treatment of AD and APP, MAPT, and PSEN2 was analyzed using (GEPIA) databases. We obtained potential targets related to aging using the Aging Altas database. Molecular docking analysis was performed by AutoDock Vina and Pymol software. Finally, we validated the significant therapeutic targets in the Gene Expression Omnibus (GEO) database.A total of 74 potential targets of Cordycepin for treating Alzheimer’s disease were identified. The potential targets of Cordycepin for the treatment of AD mainly focused on Lipid and atherosclerosis (hsa05417), Platinum drug resistance (hsa01524), Apoptosis (hsa04210), and Pathways in cancer (hsa05200). Our findings suggest that the therapeutic effect of Cordycepin on AD is primarily associated with these biological processes. We obtained 12 potential therapeutic targets for AD using the degree value in Cytoscape. Interestingly, AKT1, MAPK8, BCL2L1, FOXO3, and CTNNB1 were not only significantly associated with pathogenic genes  but also with longevity in Alzheimer’s Disease. Thus we speculated that the five target genes were potential core targets mediating the therapeutic effect of Cordycepin against AD. Moreover, molecular docking results analysis showed good binding affinity between Cordycepin and the five core targets. Overall, MAPK8, FOXO3 and CTNNB1 may have significant clinical and treatment implications.via multiple targets and signaling pathways and has huge prospects for application in treating neurodegenerative diseases.Network pharmacology demonstrated that Cordycepin exerts a therapeutic effect against Alzheimer’s disease  In recent years, the prevalence of geriatic diseases has significantly increased in China due to population aging. According to the latest Alzheimer’s disease (AD) International (ADI) data documented in the World Alzheimer’s Report 2021, there are about 55 million dementia patients in the world, and it is expected that by 2030, this number will reach 78 million .Alzheimer’s disease is a progressive neurodegenerative disease related to aging . It is wCordyceps . Cordycepin exhibits a wide range of biological activities and has an enormous impact in many therapeutic research areas . Cordycepin target information was generated by entering “Cordycepin” in these databases, searching for cordycepin targets, and selecting its default option in parameter settings. We merged the results of the six databases and removed duplicate targets to obtain targets of Cordycepin.The biological targets of cordycepin were obtained from TCMSP,The targets of Alzheimer’s disease were obtained from Genecards,p-values <0.05 were considered significantly enriched. The results of pathway enrichment analysis were plotted using the bioinformaticsTo better understand the functions of the intersected targets, gene ontology (GO) and KEGG enrichment analysis were carried out using the DAVID.STRINGTo confirm the relationship between key genes and major causative gene amyloidprecursor protein (APP) or microtubule-associated protein tau (MAPT) and presenilin2 (PSEN2), gene correlation analysis was conducted using GEPIA. Key genes and APP, MAPT and PSEN2 were input into the search words in turn, and all brain tissues were selected from GTEx tissues for correlation analysis.AgingAtlas is a bioinformatics tool used for assessing the genetic correlation between Ageing and Longevity . It is wThe screened key genes served as the key targets for receptors, and the information on receptor structures was obtained from PDBhttp://www.ncbi.nlm.nih.gov/geo/). Differentially expression analysis of AD-related genes was conducted between control and AD patients using the GEO2R tool. Graphs were generated using GraphPad Prism 8. Then, the expression distribution of key targets in neural typical was analyzed using the Human eFP Browser . A total of 152 GO biological process (BP), 12 GO cellular component (CC), 30 GO molecular function (MF) and 96 KEGG pathways were significantly enriched. As shown in 74 intersection genes were uploaded to DAVID for GO/KEGG analyses, and the significance level was set at  process P, 12 GO via the death domain protein recruitment, which in turn affects the occurrence of apoptosis, slowing neuronal cell death.KEGG analysis yielded a total of 96 pathways, and the top 13 enriched pathways are shown in The names of cordycepin-target and Alzheimer’s disease-target genes were converted into gene ID based on the UniProt database. Subsequently, we selected the intersected target genes between cordycepin-targets and Alzheimer’s disease-related targets using Venn plots. The intersected target genes were analyzed with the STRING databaseMissense mutation of APP gene changes A β Metabolize and regulate A β And cause A β Cognitive decline . To confThe docking energy associated with the binding of Cordycepin to the core target was calculated using the AutoDock Vina software. It has been established that the target possesses significant binding capacity with the compound when the docking energy is less than −1.2 kcal/mol or −5 kJ/mol. In our study, the binding energy between 5 core targets and Cordycepin were less than −5 kJ/mol, respectively , which in = 12) and normal controls (n = 11), was used to evaluate the expression changes of 5 core targets in brain tissue of AD patients. Among AD patients, the mRNA expression of CTNNB1 was significantly increased in TC and FC tissues, while MAPK8 was significantly downregulated. In contrast, FOXO3 was significantly reduced only in TC tissues. There was no significant difference in BCL2L1 and AKT1 levels between TC and FC tissues  and temporal cortex (TC) in AD  and Applied Basic Research Program of Liaoning Province 2022JH2/101300160.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Trachelospermi Caulis et Folium (TCEF) for treating depression.  To reveal the safety, efficacy, and mechanism of action of via PPI network, GO, and KEGG. Subsequently, the binding ability of the core components of TCEF to the core targets was validated via molecular docking and simulation.  The maximum dose method was employed to evaluate the safety of TCEF, and its antidepressant activity was assessed using the tail suspension and sugar water depletion tests. The main components of TCEF were determined using ultrahigh performance liquid chromatography coupled with quadrupole exactive orbitrap mass spectrometer (UHPLC-Q-EOMS). The active ingredients and their action targets were obtained using network pharmacology with SwissADME and SwissTargetPrediction screening, and the targets of depression were obtained using GeneCards, DrugBank, etc. The drug and depression-related targets were intersected and analyzed P > 0.05). Compared with the model group, all dose groups of TCEF had reduced the immobility time of tail suspension (P < 0.05) and increased the rate of sugar water consumption (P < 0.05). UHPLC-Q-EOMS was employed to identify 59 major components of TCEF, and network pharmacology analysis was used to screen 48 active components of TCEF for treating depression, corresponding to 139 relevant targets, including ALB, AKT1, TNF, ESR1, and CTNNB1. The involved pathways include neuroactive ligand-receptor interaction. The molecular docking results indicated that the core components have a good binding activity to the core targets.  No statistically significant difference was observed between the normal and TCEF groups in terms of body weight, visceral index, and biochemical parameters ( TCEF is a relatively safe antidepressant medicine that exerts therapeutic effects through multiple components, targets, and pathways, providing a new idea and theoretical basis for future use of TCEF to treat depression. Depression is a mood disorder in which the patient is in a state of loss for a long period. The disease is characterized by chronic, recurrent episodes, mainly manifesting as emotional dysfunction, excessive negative emotions, delayed thinking, irritability, loss of appetite, abnormal behavior, cognitive dysfunction, and, in severe cases, suicidal tendencies , 2. ThesTrachelospermi Caulis et Folium (TCEF) is a dried leafy vine stem of Trachelospermum jasminoides (Lindl.) Lem. in the family Oleaceae. At present, studies have demonstrated that TCEF can dispel wind and promote blood circulation, cool blood, and subdue swelling; moreover, it has anti-inflammatory and analgesic, antifatigue. Although it is also a type of rattan, relatively few studies have been conducted on its treatment of depression, and comprehensive studies on its antidepressant mechanism of action are lacking [Traditional Chinese medicine, with its safety and effectiveness, fewer side effects, and holistic treatment, has highlighted its unique advantages in preventing and treating depression, which includes rattan herbs –14. Trac lacking . As some lacking –18. Acut lacking . In addi lacking , 21. Mol lacking , 23.Therefore, in this study, we first evaluated the safety of TCEF and its efficacy in treating depression through animal experiments. Then, we used UHPLC-Q-EOMS to characterize the chemical composition of TCEF. Next, we combined network pharmacology and molecular docking techniques to conduct a preliminary study on the mechanism of multicomponent, multitarget, and multipathway treatment of depression in TCEF. The research workflow is shown in ad libitum and acclimatized for 1 week. Animal care and study protocols were approved by the institutional animal ethics committee at Southwest Jiaotong University . Ketamine (100 mg/kg) and xylazine (5 mg/kg) were administered intraperitoneally for anesthesia. Mice were euthanized with a lethal dose of pentobarbital sodium (100 mg/kg).SPF-grade adult Kunming mice  were provided by Chengdu Dashuo Experimental Animal, and the production license is SCXK (Sichuan) 2020-030. The breeding temperature was 22 °C ± 2 °C, the relative humidity was 50% ± 10%, and the light-dark cycle was 12 h. The animals were provided with food and water TCEF . Fluoxetine Hydrochloride Dispersible Tablets . A suspended tail meter . Automatic Biochemistry Analyzer HF-220 . AST (No. 20190313), ALT (No. 20190318), TP (No. 20190703), BUN (No. 20190320), UA (No. 20190323), and CRE Reagents (No. 20190328), purchased from Shandong Collison Biological Co.via evaporation and dried at 105°C for 24 h to obtain the infusion. Before the experiment, configured to the required dose, the concentrations required were 0.74 g/mL for the safety evaluation experiment and 0.52 g/mL (high-dose group), 0.26 g/mL (medium-dose group), and 0.13 g/mL (low-dose group) for the depression experiment. The positive drug was fluoxetine, ground fluoxetine tablets, and added the appropriate amount of distilled water to 0.00026 g/mL concentration.The decoction was boiled thrice for 60, 30, and 30 min, respectively, and the filtrate was combined and concentrated to 50 mL A total of 40 SPF-grade Kunming mice were randomly divided into two groups, the normal group, and the TCEF group, with 10 males and females per group. The mice were acclimatized for 1 week. The normal group was gavaged with distilled water according to the maximum dose administered (0.4 mL/10 g), and the TCEF group was gavaged with TCEF at the maximum dosing concentration (0.74 g/mL). During the 14 days of continuous observation after dosing, the mice were weighed on days 1, 7, and 14. After 14 days, blood was removed, and the blood was left to stand for 30 min and then centrifuged for 15 min at 4°C and 4 000 r/min using a tabletop high-speed frozen centrifuge. The upper serum was collected, and the indicators of the liver  and renal functions  were measured. After the animals were sacrificed, their hearts, livers, spleens, lungs, and kidneys were collected. The visceral index was calculated.A total of 42 SPF-grade Kunming male mice were randomly divided into six groups, with each group having seven mice. The normal group was not treated at all. The model, positive, TCEF low-dose, TCEF medium-dose, and TCEF high-dose groups were used to establish a depression model using the chronic unpredictable mild stress (CUMS) combined with the solitary feeding method. The stimulation methods included food and water fasting, moist bedding, restraint, forced swimming, tail pinning, and shaking of the rat cage . Except The tail suspension and sugar water depletion tests were conducted 4 weeks after modeling and 2 weeks after the drug administration to evaluate the effect of depression mice model preparation and the antidepressant effect of TCEF. For the tail suspension test, the mice were hung upside down on a special mouse tail suspension apparatus. The data were recorded after 2 min of adapting to the environment. The background of the apparatus was black, the experimenter walked as quietly as possible to avoid noise interference, and the immobility of mice within 4 min was recorded . For theSugar water preference index = 1%sucrose solution consumption/(1%sucrose solution consumption + pure water consumption) × 100% .via UHPLC-Q-EOMS. Ultimate 3000 Ultra Performance Liquid Chromatography System . Thermo Gold C18 Chromatographic columns . The chromatographic conditions were as follows: column temperature was 40°C; flow rate, 0.5 mL/min; injection volume, 4 μL; and mobile phase, 0.1% formic acid aqueous solution (a)–acetonitrile (b). A gradient elution sequence was used: 0–0.01 min, 5% B; 0.01–15 min, 5–30% B; 15–35 min, 30–95% B; 35–37 min, 95% B; 37–37.1 min, 95–5% B.The chemical composition in TCEF was obtained The mass spectrometry conditions were as follows: the UHPLC system was equipped with a Q-Exactive Orbitrap MS . The ion source was HESI. Positive and negative ion detection modes were used, and the spray voltage was 3500/−3000 V. The capillary temperature was 320°C; sheath gas, 35 L/min; auxiliary gas, 10 L/min. Can mode: full MS/dd-MS2, full MS resolution 70 000, dd-MS2 resolution 17 500; scan range m/z 100–1 500; and collision energy used was 40 eV in the MS/MS mode. The compounds were characterized using the Xcalibur 4.0 software and Compound Discoverer 3.0 software .https://pubchem.ncbi.nlm.nih.gov/) database to obtain their SDF structures and were uploaded to the SwissADME (http://www.swissadme.ch/) database for screening based on the high GI value of gastrointestinal absorption and meeting two or more “YES” of the five indices of drug similarity [http://www.swisstargetprediction.ch/), with the species limited to human origin (Homo sapiens). After removing the duplicate proteins, the standard names of the proteins were obtained by the correction of the UniProt database (https://www.uniprot.org/).The chemical components obtained using the UHPLC-Q-EOMS method were identified using the PubChem , DrugBank (https://go.drugbank.com/), DisGeNet (https://www.disgenet.org/), and Therapeutic Target Database  for disease gene targets. The retrieved disease targets were combined and deduplicated.The keyword “Depression” was used in GeneCards (https://string-db.org) database, with the species limited to Homo sapiens, to obtain a protein–protein interaction (PPI) network map of the common targets of TCEF and depression. Then, the Cytoscape 3.8.2 software was imported for network analysis to obtain the key targets of TCEF for depression treatment.Venn analysis (Venny 2.1.0) was conducted to obtain drug–disease common targets. The common targets were imported into the String (https://david.ncifcrf.gov) is a large-scale gene or protein enrichment tool that can identify the most significantly enriched biological annotations. The common targets were imported into the DAVID and background database, and the gene list was restricted to “Homo sapiens” and then subjected to gene ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.The Database of Annotated Biological Information , PyMol was used to remove the water molecules of the target protein, the original ligand and receptor were separated, and the target protein was imported into AutoDockTools 1.5.6 for hydrogenation and charge assignment and saved in the pdbqt format file. Molecular docking tests were conducted using AutoDockVina 1.1.2 on the core compounds and the top five targets in the degree ranking in TCEF. Docking results were visualized using Discovery Studio 3.5 Client.The SDF structure of the compound was downloaded from the PubChem database, saved in the mol 2 format, and then saved in the pdbqt format after assigning charges and setting rotatable keys using AutoDockTools 1.5.6. The 3D crystal structure of the target protein was downloaded from PDB . Significant differences between groups were analyzed using one-way analysis of variance (ANOVA). Statistical analysis and plots were performed using SPSS 22.0 and Graphpad Prism 8.0.2 software, respectively. P > 0.05). The effects of TCEF on the heart, liver, spleen, lung, and kidney of mice were insignificantly different  and lower sugar water consumption rate (P < 0.05), indicating that this experiment successfully replicated the depression model after 4 weeks of solitary feeding and modeling stimulation and could continue the experimental study of TCEF antidepressant. After 14 days of continuous administration, compared with the normal group, the mice in the model group showed an increase in tail suspension immobility time within 4 min (P < 0.01) and a decrease in sugar water consumption rate (P < 0.01); moreover, compared with the model group, the mice in the positive, TCEF low-dose, medium-dose, and high-dose groups had decreased tail suspension immobility time within 4 min (P < 0.01), and an increase in the sugar water consumption rate (P < 0.01) indicated that TCEF has antidepressant effects  of TCEF in positive and negative ion scan modes, respectively, as presented in We conducted an in-depth evaluation of the properties of 59 compounds from TCEF related to absorption, distribution, metabolism, and excretion using the online tool SwissADME. SwissADME predictions indicated that 53 compounds met Lipinski's rule of five and GI absorption for high pharmacological properties  Supplem. The SwiUsing “depression” as the keyword, we searched four disease target databases  and integrated 1078 depression targets, including 824 targets from GeneCards, 125 from DrugBank, 41 from DisGeNET, and 88 from TTD. The final 957 depression-related targets were obtained by eliminating duplicates . Among tHomo sapiens,” 1 free target was deleted, 138 targets were obtained, and the file was saved in the tsv format and uploaded to the Cytoscape 3.8.2 software for network analysis. The network contained 138 nodes and 2730 edges with a mean degree of 39.6, among which ALB, AKT1, TNF, ESR1, CTNNB1, VEGFA, SLC6A4, COMT, SLC6A3, and TH were the top 10 targets in terms of degree , mainly involving chemical synaptic transmission, signal transduction, positive regulation of transcription from RNA polymerase II promoter, etc. There were 83 entries related to cell composition (CC), mainly relating to the plasma membrane, an integral component of the membrane, and integral component of the plasma membrane. There were 151 entries related to molecular function (MF), mainly relating to protein binding, identical protein binding, and G-protein coupled receptor activity. The top 10 enriched pathways for BP, CC, and MF genes were visualized .The DAVID was used to analyze 138 potential targets, and 140 KEGG signaling pathways were obtained. The top 20 pathways were visualized, mainly involving depression-related pathways, such as neuroactive ligand-receptor interaction, calcium signaling pathway, cAMP signaling pathway, PI3K-Akt signaling pathway, serotonergic synapse, and dopaminergic synapse . The neuIn general, it is believed that the lower the energy at which the ligand binds to the receptor in a stable conformation, the greater the likelihood of an effect occurring. The core compounds, luteolin, wogonin, and naringenin, were molecularly docked with the top five potential targets in terms of degree value, ALB, AKT1, TNF, ESR1, and CTNNB1. The results indicated that the docking binding energy was <−5.0 kJ/mol , suggestP > 0.05), indicating that TCEF has low oral toxicity and high administration safety. Meanwhile, the pharmacodynamic evaluation of TCEF was performed, and the depression model was successfully established using CUMS. The results of behavioral experiments after drug administration indicated that the TCEF group had a significant decrease in tail suspension immobility time and an increase in sugar water consumption rate compared with the model group, suggesting that TCEF has antidepressant effects.In this study, the safety of TCEF was first investigated using the maximum dose method. No statistical difference was observed in the visceral adiposity index, body mass, and biochemical blood indices  and brain-derived neurotrophic factor and by modulating HPA axis dysfunction [From the TCEF-active ingredient target interaction network analysis, it can be speculated that luteolin, wogonin, and naringenin are the key chemical components of TCEF for treating depression. Luteolin belongs to the group of flavonoids and is an important natural polyphenol found in various plants. The antidepressant activity of luteolin has been reported to be associated with the inhibition of endoplasmic reticulum stress, improvement of oxidative homeostasis, and neuroplasticity , 37. Wogfunction .Based on the network topology combined with the PPI analysis, we obtained that TCEF can regulate depression through five core targets, namely, ALB, AKT1, TNF, ESR1, and CTNNB1. It was found that the ALB levels were lower in depressed patients than in normal controls, and it is believed that ALB may exert antidepressant effects by regulating oxidative stress responses . Akt is GO enrichment analysis revealed that the processes of depression treatment with TCEF mainly involved chemical synaptic transmission, signal transduction, plasma membrane, an integral component of the membrane, and protein binding. This may be related to the complex pathogenesis of depression, suggesting that TCEF can intervene in the development of depression from different levels and multiple pathways.KEGG enrichment analysis revealed that neuroactive ligand–receptor interaction (hsa04080), cAMP signaling pathway (hsa04024), PI3K-Akt signaling pathway (hsa04151), serotonergic synapse (hsa04726), and dopaminergic synapse (hsa04728) are rich in many targets that may contribute to the treatment of depression with TCEF. The neuroactive ligand–receptor interaction is the pathway with the highest enrichment of targets among the predicted pathways, including 5-hydroxytryptamine receptors, dopamine receptors, cholinergic receptors, adrenergic receptors, and opioid receptors. This pathway is directly related to neurological function and contributes to extracellular signaling into cells, playing a key role in processes such as learning memory capacity, neuroplasticity, and synaptic function, and is associated with the mechanisms of depression and antidepressant effects of many herbal formulations –55. SeroCalcium, cAMP, and PI3K-Akt signaling pathways are associated with signaling. Depression has been found to be associated with the dysregulation of divalent calcium ions, which act mainly through the calcium signaling cascade. The calcium signaling pathway stimulates NO synthesis, which then induces guanylate cyclase to convert guanosine triphosphate to cGMP. Calcium imbalance leads to neuronal apoptosis, which then alters the structure and function of the hippocampal region, ultimately leading to depression . cAMP siThe molecular docking results indicated that the effective core compounds of TCEF had binding energies <−5 kJ/mol to the core target proteins ALB, AKT1, TNF, ESR1, and CTNNB1, and there were multiple binding sites with the amino acid residues of the core targets, forming hydrogen-bonding interactions. The high binding activities of these core compounds to the receptor proteins suggest that the results of this study are relatively accurate and reliable and can provide a theoretical basis for the treatment of depression with TECF at the molecular level. At the same time, there are some shortcomings in this study. The core targets and pathways predicted by the network pharmacology are not supported by relevant cellular and animal experimental data, and follow-up studies will experimentally validate the key targets and pathways through which TCEF plays an antidepressant role.We have experimentally demonstrated that TCEF has a safety profile and antidepressant effects. Furthermore, we preliminarily identified the chemical composition of TCEF using UHPLC-Q-EOMS. Using the method of network pharmacology and molecular docking, the mechanism of TCEF antidepression through multicomponent, multitarget, and multipathway was predicted. TCEF mainly regulates neuroactive ligand-receptor interaction, calcium signaling pathway, cAMP signaling pathway, PI3K-Akt signaling pathway, serotonergic synapse, and dopaminergic synapse pathway, acting on core targets, such as ALB, AKT1, TNF, ESR1, and CTNNB1, to regulate neurotransmitters and their receptors, enhance neurotrophy and protection, and reduce neuronal apoptosis damage, exerting antidepressant effects.


Alcoholic liver disease (ALD) is linked to a broad spectrum of diseases, including diabetes, hypertension, atherosclerosis, and even liver carcinoma. The ALD spectrum includes alcoholic fatty liver disease (AFLD), alcoholic hepatitis, and cirrhosis. Most recently, some reports demonstrated that the pathogenesis of ALD is strongly associated with metabolites of human microbiota. AFLD was the onset of disease among ALDs, the initial cause of which is alcohol consumption. Thus, we analyzed the significant metabolites of microbiota against AFLD via the network pharmacology concept. The metabolites from microbiota were retrieved by the gutMGene database; sequentially, AFLD targets were identified by public databases . The final targets were utilized for protein–protein interaction (PPI) networks and signaling pathway analyses. Then, we performed a molecular docking test (MDT) to verify the affinity between metabolite(s) and target(s) utilizing the Autodock 1.5.6 tool. From a holistic viewpoint, we integrated the relationships of microbiota-signaling pathways-targets-metabolites (MSTM) using the R Package. We identified the uppermost six key targets  against AFLD. The PPI network analysis revealed that TLR4, RELA, IL6, PPARG, and COX-2 had equivalent degrees of value (4); however, CYP1A2 had no associations with the other targets. The bubble chart showed that the PI3K-Akt signaling pathway in nine signaling pathways might be the most significant mechanism with antagonistic functions in the treatment of AFLD. The MDT confirmed that Icaritin is a promising agent to bind stably to RELA (known as NF-Κb). In parallel, Bacterium MRG-PMF-1, the PI3K-Akt signaling pathway, RELA, and Icaritin were the most significant components against AFLD in MSTM networks. In conclusion, we showed that the Icaritin–RELA complex on the PI3K-Akt signaling pathway by bacterial MRG-PMF-1 might have promising therapeutic effects against AFLD, providing crucial evidence for further research. Alcoholic liver disease (ALD) has a causal relationship with the excess consumption of alcohol, which can be accumulated as excessive fats in the liver . The livSteatosis is an initial response to overdrinking, and it is expressed by the accumulation of fat in hepatocytes . SteatosThen, steatohepatitis can progress to fibrosis due to the deposition of an excessive extracellular matrix . FibrosiIn recent years, gut microbiota treatments have been considered as another alternative medicine that provides favorable therapeutic efficacy for fatty liver patients . PreviouNoticeably, flavonoids dampen reactive oxygen species (ROS), cholesterol synthesis, and apoptosis in hepatocytes . The gutThe metabolites from the gut microbiota were identified by the gutMGene database, indicating that targets related directly to the metabolites were meta-analyzed by the Similarity Ensemble Approach (SEA) and SwissTargetPrediction (STP). The overlapping targets between gutMGene and AFLD associated with the meta-analyzed targets were considered the core targets against AFLD. We hypothesize that the core targets are important therapeutic elements to confirm the function on a key metabolite. Based on this hypothesis, we performed MDT to identify the most stable metabolite(s)–target(s) complex in a hub signaling pathway. Thus, we postulated that the gut microbiota-produced metabolite(s) bound most stably to a target on a hub signaling pathway are crucial indicators in treating AFLD.http://bio-annotation.cn/gutmgene/) (accessed on 21 May 2022). We utilized the Similarity Ensemble Approach (SEA) (https://sea.bkslab.org/) (accessed on 21 May 2022) [http://www.swisstargetprediction.ch/) (accessed on 22 May 2022) [https://www.disgenet.org/) (accessed on 23 May 2022) and OMIM (https://www.omim.org/) (accessed on 24 May 2022). The core targets were analyzed by STRING bioinformatics platform (https://string-db.org/) (accessed on 25 May 2022). We performed PPI network and MSTM analyses via R Package. The workflow of this study is as follows.The metabolites from the gut microbiota were retrieved by gutMGene (Step 1: Identification of metabolites from the gut microbiota via gutMGene.Steps 2 and 3: Identification of targets related to the metabolites via the SEA and STP databases. The metabolites were input into the SEA and STP databases in SMILES format.Step 4: Identification of AFLD targets via DisGeNET and OMIM.https://bioinfogp.cnb.csic.es/tools/venny/) (accessed on 25 May 2022) [Step 5: Identification of overlapping targets between Steps 2 and 3 and Step 4. The overlapping targets were identified by VENNY 2.1 (Step 6: Identification of core targets between Step 4 and targets via gutMGene.https://string-db.org/) (accessed on 25 May 2022) [Step 7: The construction of PPI networks from the core targets. Information on the target networks was identified by STRING (ay 2022) .The PPI networks were visualized by the R package.Step 8: The construction of a bubble chart to identify a hub-signaling pathway against AFLD. Based on the expressed gene ratio, the bubble chart was constructed by R Package.http://www.swissadme.ch/) (accessed on 25 May 2022) was utilized to identify the physicochemical properties of drug-likeness about metabolite(s). The toxicological evaluation was confirmed by the ADMETlab 2.0 platform (accessed on 25 May 2022) [Step 9: The first screening of the significant metabolites was based on Topological Polar Surface Area (TPSA) < 140 Å2 or Lipinski’s rule. SwissADME (ay 2022) .https://www.rcsb.org/) (accessed on 26 May 2022). MDT was performed to evaluate the affinity of metabolite(s)-target(s) by utilizing AutoDockTools-1.5.6.Step 10: The second screening via MDT based on the threshold  or the lowest Gibbs energy  of each metabolite. The metabolites were downloaded as. Sdf format from PubChem, which were converted into.pdb format via PyMOL. The .pdb format was converted into .pdbqt format to prepare for MDT via the AutoDockTools-1.5.6 tool. Then, the PDB IDs of the target(s) were identified by RCSB PDB (https://www.ebi.ac.uk/thornton-srv/software/LigPlus/download2.html) (accessed on 27 May 2022) [The docking site was set up with a cubic box on the center: RELA (known as NF-Κb) . The grid box size of the active site was set to x = 40 Å, y = 40 Å, and z = 40 Å. The identification of 2D binding interactions was performed by LigPlot+2.2. (ay 2022) .Step 11: Construction of the MSTM. The MSTM networks were visualized by R Package. The most significant components against AFLD were based on the degree of value.The degree value of the microbiota, signaling pathway, target, or metabolite represents the edge numbers of the microbiota, signaling pathway, target, or metabolite in the MSTM network.Taken together, the relationships of microbiota, signaling pathways, targets, and metabolites were constructed with Microsoft Excel and then incorporated into R Package to assemble the network of metabolites related to AFLD.The workflow of this study is displayed in A total of 208 (metabolites) were retrieved from the gutMGene database, targets of which were identified by the SEA (1256) and STP (947) databases . The numIn the PPI networks, CYP1A2 did not interact with the other five targets  and comprised five nodes and 10 edges .The PPI networks suggested that the five core targets had the same degree value (5). Next, the results of a bubble chart indicated that four overlapping targets were significantly enriched in nine signaling pathways  .The nine signaling pathways were directly associated with the progression of AFLD, suggesting that these signaling pathways might be the modes of action against AFLD. Additionally, RELA (the key target) in the MSTM network was directly enriched in all nine signaling pathways by the PI3K-Akt signaling pathway as a hub signaling pathway against AFLD. Three metabolites  which bound stably to the PI3K-Akt signaling pathway were identified; however, we removed two metabolites (lacto-N-tetraose and quercimeritrin) due to a violation of Lipinski’s rule. Thus, we confirmed that Icaritin  is a proAdditionally, MDT demonstrated that Icaritin  bound stably to RELA, which is associated with the PI3K-Akt signaling pathway  Figure . MSTM neThe results of the bubble chart suggested that the nine signaling pathways might be therapeutic mechanisms that ameliorate AFLD. The relationships of the nine signaling pathways with AFLD are concisely expounded as follows. (1) AGE-RAGE signaling pathway in diabetic complications: The AGE-RAGE interaction accelerates the accumulation of fat in the liver, which causes inflammation, fibrosis, and other disorders of fatty liver disease . It follThe MSTM network suggested that the components directly related to therapeutic effects on AFLD consist of 13 microbiota, 9 signaling pathways, 4 targets, and 49 metabolites. Based on their degrees of value, Bacterium MRG-PMF-1 (97), PI3K-Akt signaling pathway (12), RELA (9), and Icaritin (1) were the most significant elements for alleviating AFLD.Additionally, Lactobacillus paracasei JS1 and Lactobacillus acidophilus ATCC 4357 with the same degree of value as Bacterium MRG-PMF-1 can produce equol with anti-inflammatory effects and function as alleviators of digestive disorders, respectively ,37,38. NFrom a systemic viewpoint via network pharmacology, this study initially elaborates on the key microbiota, critical signaling pathways, significant targets, and crucial metabolites against AFLD. Comprehensively, we have analyzed the uppermost components by integrating networks to expound the therapeutic elements against AFLD. Given the limitations of the database, the indicated four factors are based on data mining; however, the MSTM network represents crosstalk between the host and microbiota. Finally, this study requires further clinical trials to verify its therapeutic benefits based on scientific evidence from this research.In summary, this study provides promising components for treating AFLD via microbiota-based analysis of the network pharmacology concept. In this analysis, we revealed that Bacterium MRG-PMF-1, which produces Icaritin, bound stably to RELA to inhibit PI3K-Akt signaling pathway. Our findings also contribute to revealing the therapeutic value of metabolites from microbiota, which needs further validation in clinical trials.


Retinoblastoma (Rb) is a rare intraocular tumour in early childhood, with an approximate incidence of 1 in 18 000 live births. Experimental studies for Rb are complex due to the challenges associated with obtaining a normal retina to contrast with diseased tissue. In this work, we reanalyse a dataset that contains normal retina samples. We identified the individual genes whose expression is different in Rb in contrast with normal tissue, determined the pathways whose global expression pattern is more distant from the global expression observed in normal tissue, and finally, we identified which transcription factors regulate the highest number of differentially expressed genes (DEGs) and proposed as transcriptional master regulators (TMRs). The enrichment of DEGs in the phototransduction and retrograde endocannabinoid signalling pathways could be associated with abnormal behaviour of the processes leading to cellular differentiation and cellular proliferation. On the other hand, the TMRs nuclear receptor subfamily 5 group A member 2 and hepatocyte nuclear factor 4 gamma are involved in hepatocyte differentiation. Therefore, the enrichment of aberrant expression in these transcription factors could suggest an abnormal retina development that could be involved in Rb origin and progression. This RB1 ,7. IdentRB1 . PathwayRB1 –14. PA aRB1 .ORA methods can detect statistically enriched biological processes from a list of differentially expressed genes (DEGs). Statistical significance will be reached if more DEGs belong to a specific pathway than expected by chance. By contrast, FCS methods overcome the limitation of focusing on a limited number of genes per pathway; these methods account for the expression level of all genes in a pathway or gene set to compute a metric that reflects the state of each pathway or gene set. Pathifier  is an FCA different approach, master regulators analysis (MRA), has been extensively used for the study of cancer –14,17–21et al. [In this work, we revisited the dataset generated and used by Kapatai et al. . They weet al. dataset since the number of tumour samples and the presence of control samples offer a rare opportunity to study Rb with pathway-focused tools. In this study, we present a broader view of the transcriptomic context associated with the Rb phenotype by evaluating individual gene contributions, collective gene expression patterns via master regulators, and identifying functional consequences by deregulated pathways, thus presenting a clear landscape of the effects of gene deregulation underlying this pathologic phenotype.Furthermore, we also identified TF regulators performing an MRA, which we integrated into a pathway-level analysis by interrogating which TMRs control these pathways. We applied all these analyses to the Kapatai . 2. 2.1et al. [https://www.ncbi.nlm.nih.gov/geo/): two normal adult retina  and one 96-day human fetal retina (GSM460264). All CEL files were built up through Affymetrix Human Gene 1.0 ST microarrays. Sample identifications match the original publication [The Kapatai et al.  dataset lication .. 2.2The analysis pipeline was developed in three levels: differential expression, deregulation pathways analysis and master regulation analysis. .Figure . 2.3The CEL files were pre-processed with the Affymetrix Expression Console software. The robust multichip average (RMA) algorithm was used to normalize and summarize probesets . Pre-pro. 2.4p-value < 0.05) were grouped by hierarchical clustering (HC) (using Euclidean distance as distance method and complete agglomeration as clustering method) and the results plotted in a heatmap with gplots from R package [We calculated differential expression via the limma Bioconductor R package , contras package . Overrep package . We chos. 2.5To determine the pathway deregulation score (PDS), we used the Pathifier algorithm , which u. 2.5.1z-score transformed to allow a fair comparison between different pathways. The mean and standard deviation of the PDSs per pathway were obtained and used to calculate the z-score per pathway per sample. This transformation allowed us to compare deregulation values across all pathways. Next, we obtained the median PDS z-score per pathway, taking into consideration either all tumoral samples or all samples per subgroup obtained with clustering analysis. From these, we can obtain the top 10 deregulated pathways in Rb or in each tumoral subgroup, respectively.The PDSs obtained with Pathifier are based on comparing all samples through each single pathway. As different pathways may have different dynamic ranges, each PDS was . 2.6p-value less than 1 × 10−7 were chosen as true interactions [We used corto (Correlation Tool) implemented in R  to generractions . This alt-statistic per gene was obtained from comparing the healthy versus control expression values, and it was transformed into z-scores. These z-scores were considered the molecular signature. Then, the gene labels were reshuffled, and this randomization was repeated 1000 times to obtain the null model. During the MRA, GSEA was run for each regulon using the z-scores as weights, and a NES was obtained. Each regulon NES was then compared with its null model distribution to calculate a p-value. The regulons with the highest NES value, a statistically significant p-value and with at least 20 genes were selected as TMRs [We used MRA, implemented in the package corto, to find the TMRs. MRA requires a regulatory network, a molecular signature and a null model. The network obtained by corto was used as the regulatory network. A  as TMRs ,18. Fina. 3. 3.1Differential expression analysis allows us to measure quantitative changes in expression levels between two conditions. We found 305 DEGs (absolute LFC > 2) by comparing Rb samples to the normal retina, including 186 under-expressed genes and 119 over-expressed genes in Rb compared with the normal retina . et al. .Unsupervised HC of DEGs showed four large clusters. As expected, this result exactly reproduces the grouping found by Kapatai et al. . Cluster. 3.2−10), olfactory transduction (FDR = 9.45 × 10−6) and retrograde endocannabinoid signalling (FDR = 0.04).We did an ORA based on KEGG pathways. The three pathways statistically enriched in DEGs with FDR < 0.05 were phototransduction . Loss of this signal transduction capacity in Rb is related to the undifferentiated state these tumour cells exhibit and may explain the enrichment of these functions in DEGs. Additionally, some studies have found that components of this pathway, specifically members of the phosphodiesterase 6 (PDE6) family, may play a role in the development of breast cancer [p-value = 0.004 and 0.017, respectively). On the other hand, the olfactory transduction pathway is composed of a large number of genes from the G protein family. In recent years, the expression of these genes has been detected in multiple processes across different tissues [Both phototransduction and olfactory transduction are closely related metabolic pathways. Rhodopsin, the main protein responsible for converting light into electrical signals, is under-expressed with a LFC of −3.6975  have been found to affect epidermal proliferation and differentiation . AnotherLastly, changes in the expression pattern of the retrograde endocannabinoid signalling pathway (ECS) have been found in various cancer types, and such changes have been proposed to have an impact on disease progression and patient survival .. 3.3We used the Pathifier algorithm  to deterFor each studied pathway, the tumoral samples with gene expression values similar to the average of control healthy tissue will have a PDS close to 0. By contrast, the samples with the highest PDS (close to 1) will have the most significant differences in gene expression levels compared to the average normal tissue. Previous studies have used this methodology to determine pathway deregulation of metabolic pathways in multiple cancer studies –49.N = 296) for every sample. Unsupervised HC shows that deregulation levels are consistent with Rb subgroups. Figure 3a Illustrates that samples are grouped according to the same subgroups obtained previously via differential gene expression analysis . However, the global clustering presents clear differences, grouping all Rb samples in a higher level cluster. On the other hand, pathways are clustered into two high-level clusters: a larger cluster composed of 246 pathways (cluster (i)) and a small cluster composed of 50 pathways (cluster (ii)). We can observe that pathways in cluster (i) tend to be more deregulated in subgroup 1 compared to subgroup 2 and normal-like samples. On the contrary, pathways in cluster (ii) tend to be more deregulated in subgroup 2 compared to subgroup 1 and normal-like samples. Also, we can see that most pathways tend to be less deregulated in normal-like samples compared to subgroup 1 and subgroup 2 samples; this could be caused by contamination with normal retina cells.We estimated a PDS for each KEGG pathway  were steroid hormone biosynthesis, focal adhesion, 2-oxocarboxylic acid metabolism, fatty acid degradation, Rap1 signalling pathway, peroxisome proliferator-activated receptor (PPAR) signalling pathway, morphine addiction, retrograde endocannabinoid signalling, amyotrophic lateral sclerosis (ALS) pathway and arrhythmogenic right ventricular cardiomyopathy (ARVC). These pathways were selected and grouped by unsupervised HC (b). This clustering shows that the normal-like samples are grouped with the normal retina and the two Rb subgroups are clustered together. Also, we can observe the same two high-level clusters of pathways as in the global deregulation heatmap: a top cluster that is more deregulated in subgroup 1 compared to subgroup 2 and the bottom one which seems to be equally deregulated in both subgroups (b).The most deregulated pathways in Rb were selected from the median PDS vised HC b. This cubgroups b.We also determined which are the most deregulated pathways per Rb subgroup. Interestingly, the most deregulated pathways vary across subgroups .. 3.4We did an MRA to search possible TMRs in Rb. We used the corto algorithm to infer the regulatory network; this algorithm uses Spearman correlation supplemented with the data processing inequality between a list of previously defined TFs and genes to find statistical correlation from expression data ,45,46. T. 4In this work, we performed the functional analysis of Rb on three levels: differential gene expression, pathway deregulation and phenotype transcriptional regulation. Differential expression was carried out to characterize the gene level in contrast with the normal retina. We used Pathifier to analyse the biological functions and metabolic pathways level and MRA for the global transcriptional regulation level. In this approach, as a microscope focuses on different depths, we focus the analysis on different biological layers, and we find evidence of alterations at each level revealing the mechanisms behind Rb.a) are in agreement with previous findings by the group of Kapatai [As previously mentioned, the main cluster features revealed by such analyses a are in  Kapatai . (i) Difin vitro and in vivo [Most of the DEGs found at this work (6 out of 10) belong to the snoRNA class, a small non-coding RNA class. One well-studied function of snoRNAs is their role in the modification, maturation and stabilization of rRNA. . Emergin in vivo .Most top deregulated pathways have been previously associated with cancer. Steroid hormone biosynthesis is a pathway of recognized importance in the establishment and proliferation of various types of cancer –58. Vert+ [On the other hand, focal adhesion plays an essential role in processes such as cell motility, cell proliferation, cell differentiation, regulation of gene expression and cell survival . Closely+ ,74. The + ,76. More+ .PPARs are nuclear hormone receptors that are activated by fatty acids and their derivatives. PPAR protein family plays a role in processes like the clearance of circulating or cellular lipids via the regulation of gene expression involved in lipid metabolism in liver and skeletal muscle, lipid oxidation and cell proliferation . It has Morphine addiction and retrograde endocannabinoid (ECS) signalling pathways are also closely related to carcinogenic processes . Gamma-aThe ALS pathway, which is a progressive and lethal motor disorder, includes in its gene list TP53, several members of the caspase family pathway, TNF and MAP kinases; all of these genes are widely documented as important agents in many cancer phenotypes –94. FurtMetabolic and signalling pathways previously implicated in Rb development were also found. Fatty acid synthesis is suggested to support an essential functional aspect of Rb. Most tumours present high levels of fatty acid synthase (FAS), and higher FAS expression has been correlated with more advanced choroid and optic nerve invasion, high mitotic index, and less differentiated histology .Throughout the different sections of this work, we have presented the results of three different classes of pathway-level analysis derived from the same initial data: expression data from 29 Rb samples and three normal tumour-free retina samples. In Only one pathway is enriched in DEGs and belongs to the top10 most deregulated pathways: retrograde endocannabinoid signalling. This pathway is also presumably regulated by one TMR: ELF5 regulates MAPK9. ELF5 is a transcription factor of the ETS family. This family is important for its role in cell proliferation, cell differentiation, cell development, apoptosis and tissue remodelling . It has The phototransduction pathway contains a lot more DEGs than the number expected by chance (highest enrichment ratio); besides, this pathway is one of the most highly deregulated (ranking 22). This fact is interesting, in particular since genes of the PDE6 family previously associated with the action of photoreceptors as signal transducers of luminal stimuli ,103 havep-value < 0.001) and present in the HOXA2 and ELF5 regulons, respectively.The olfactory transduction pathway has a noticeable behaviour as it is enriched in DEGs. It has a substantial number of genes regulated by the proposed TMRs . This pathway is also significantly enriched (FDR < 0.05) for 6 of the 10 TMR regulons , HNF4G, homeobox A2 transcription factor (HOXA2), nuclear receptor subfamily 5 group A member 2 (NR5A2) and POU1F1). This pathway comprises many molecules (448) involved in the transduction of chemical signals into the brain, including receptor proteins, olfactory specific Gs-proteins and type III adenylyl cyclases. Although the structure and function of its components in the nasal cavity are well known –106, theAlthough 2-oxocarboxylic acid metabolism is the most deregulated pathway, it is not enriched in DEGs, and it contains only one gene (IDH3B) regulated by a TMR: melanocyte-inducing transcription factor (MITF). Importantly, this interaction has been experimentally validated . FurtherALS is a highly deregulated pathway (ranking 4), and 10 of its genes are regulated by the proposed TMRs. Indeed, five different TMRs regulate ALS genes  and NR5A2), several of which have been related to different types of cancer  ,116–130.Hepatocyte differentiation was not dysregulated in our analysis but NR5A2 and HNF4G were identified as TMRs. NR5A2 is also known as hepatocytic TF, and HNF4G is a critical factor for hepatocyte differentiation. It has been reported that HNF4G can exert a carcinogenic effect by promoting cell proliferation and inhibiting cell apoptosis in lung cancer . MoreoveThere is, of course, a close relationship between Rb (the intrusive intraocular mostly pediatric tumour) and Rb (the tumour suppressor protein). It is widely known that Rb, when fully functional, often acts to prevent excessive cell growth by inhibiting cell cycle progression by binding to cell cycle proteins. Rb malfunction has been also linked to other cancers such as sarcomas, astrocytomas, melanomas, as well as other epithelial tumours , most no. 5In this work, we studied Rb genetic expression at several organizational levels. For each of these levels, the existence of healthy tissue samples was of utmost importance. We identified the individual genes whose expression is different in Rb in contrast with normal tissue. We determined the pathways whose global expression pattern is more distant from the global expression observed in normal tissue. Finally, we identified which TFs regulated the highest number of DEGs and proposed them as TMRs. The integration of all these layers allows us to pinpoint genes and pathways relevant to the Rb phenotype.Rb has been described as a highly undifferentiated malignancy. The enrichment of DEGs in the phototransduction and retrograde endocannabinoid signalling pathways could be associated with abnormal behaviour of the processes leading to cellular differentiation and cellular proliferation. On the other hand, the TMRs NR5A2 and HNF4G are involved in hepatocyte differentiation. The enrichment of aberrant expression in the regulons of these TFs could suggest an abnormal retina development which could be involved in Rb origin and progression.


Objective: Curcumin is a plant polyphenol extracted from the Chinese herb turmeric. It was found that curcumin has good anti-cancer properties in a variety of cancers, but the exact mechanism is not clear. Based on the network pharmacology and molecular docking to deeply investigate the molecular mechanism of curcumin for the treatment of colon cancer, it provides a new research direction for the treatment of colon cancer.Methods: Curcumin-related targets were collected using PharmMapper, SwissTargetPrediction, Targetnet and SuperPred. Colon cancer related targets were obtained using OMIM, DisGeNET, GeneCards and GEO databases. Drug-disease intersection targets were obtained via Venny 2.1.0. GO and KEGG enrichment analysis of drug-disease common targets were performed using DAVID. Construct PPI network graphs of intersecting targets using STRING database as well as Cytoscape 3.9.0 and filter core targets. Molecular docking via AutoDockTools 1.5.7. The core targets were further analyzed by GEPIA, HPA, cBioPortal and TIMER databases.Results: A total of 73 potential targets of curcumin for the treatment of colon cancer were obtained. GO function enrichment analysis yielded 256 entries, including BP:166, CC(celluar component):36 and MF(Molecular Function):54. The KEGG pathway enrichment analysis yielded 34 signaling pathways, mainly involved in Metabolic pathways, Nucleotide metabolism, Nitrogen metabolism, Drug metabolism - other enzymes, Pathways in cancer,PI3K-Akt signaling pathway, etc. CDK2, HSP90AA1, AURKB, CCNA2, TYMS, CHEK1, AURKA, DNMT1, TOP2A, and TK1 were identified as core targets by Cytoscape 3.9.0. Molecular docking results showed that the binding energies of curcumin to the core targets were all less than 0 kJ-mol-1, suggesting that curcumin binds spontaneously to the core targets. These results were further validated in terms of mRNA expression levels, protein expression levels and immune infiltration.Conclusion: Based on network pharmacology and molecular docking initially revealed that curcumin exerts its therapeutic effects on colon cancer with multi-target, multi-pathway. Curcumin may exert anticancer effects by binding to core targets. Curcumin may interfere with colon cancer cell proliferation and apoptosis by regulating signal transduction pathways such as PI3K-Akt signaling pathway,IL-17 signaling pathway, Cell cycle. This will deepen and enrich our understanding of the potential mechanism of curcumin against colon cancer and provide a theoretical basis for subsequent studies. Colon cancer (CC) has the third highest incidence of all tumors worldwide and is a common cause of oncologic death . MeanwhiChinese medicine has multi-target and multi-pathway mechanisms of action for treating diseases. Therefore, it is necessary to use big data to mine all existing targets and pathways related to curcumin and CC. Network pharmacology is a method that integrates bioinformatics and pharmacology. Through data integration and computational analysis, it can systematically clarify the relationship between drugs and diseases and explore the mechanism of drug action . In thisThe databases involved in this study  and the https://pubchem.ncbi.nlm.nih.gov/) (http://lilab-ecust.cn/pharmmapper/), SwissTargetPrediction (http://www.swisstargetprediction.ch/), Targetnet (http://targetnet.scbdd.com/) and SuperPred (https://prediction.charite.de/) (https://www.uniprot.org/) (Details of curcumin are available in PubChem (ih.gov/)  using CAite.de/) . The obtot.org/) . Finallyhttps://www.ncbi.nlm.nih.gov/geo/) as the study subject (https://www.omim.org/), DisGeNET (https://www.disgenet.org/), and GeneCards databases (https://www.genecards.org/) (GSE74602 was selected from the GEO database ( subject . Differe subject . Some CCds.org/)  using thhttps://bioinfogp.cnb.csic.es/tools/venny/index.html), and the intersecting genes represented the potential targets of curcumin for CC treatment. The protein-protein interaction network of common targets was constructed using STRING database (https://cn.string-db.org/), and the tsv file was downloaded and imported into Cytoscape 3.9.0 software for visualization.The intersection of curcumin targets and CC targets was performed using Venny 2.1.0 (https://david.ncifcrf.gov/) (http://www.bioinformatics.com.cn/).The GO and KEGG enrichment analysis of potential targets of curcumin for CC treatment was performed by “Functional Annotation” in the DAVID website (rf.gov/) . The obtThe drug-target-pathway network was constructed by introducing curcumin, potential targets of curcumin for CC treatment and KEGG pathway into Cytoscape. The nodes represent curcumin, genes or pathways, and the connecting lines represent the relationship of biomolecules.In “cytohubba” of Cytoscape 3.9.0. Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC) and Closeness were used to filter the top 15 targets respectively. The intersection of the targets obtained by these four calculation methods is the core targets.https://www.rcsb.org/). We convert the SDF format to mol2 format via OpenBabel-3.1.1. We import the mol2 format of the ligand into AutoDockTools 1.5.7 to set the torsion and output it as a pdbqt format file. The PDB file of the target protein is downloaded and the details of the protein are collected in the PDB database , the mRNA expression levels and pathological stages of the core targets were verified   . The prohttps://www.cbioportal.org/)  dataset containing 110 samples was selected for analysis in cBioPortal  . Informahttps://cistrome.shinyapps.io/timer/)   to exploA total of 448 curcumin action targets were obtained. A total of 1732 differentially expressed genes were screened from the GSE74602 dataset . A totalThe results of Venn diagram showed that 73 common targets were screened by matching 448 drug targets and 704 disease targets . These 773 potential targets of curcumin for CC treatment were imported into DAVID for GO and KEGG enrichment analysis. The GO enrichment analysis yielded 256 entries, including BP : 166, CC(celluar component):36 and MF (Molecular Function):54. The top 6 entries of each type of analysis were selected for visualization in Bioinformatics . 73 targThe top 20 KEGG pathways were imported into Cytoscape to construct a drug-target-pathway network . The bluThe PPI network diagram of potential targets  was analp <0.01) (p <0.01)  . In addip <0.01) .Immunohistochemical staining images in the HPA database were analyzed to observe the expression levels of core target proteins in CC. We found elevated expression levels of CDK2, HSP90AA1, AURKB, CCNA2, TYMS, AURKA, DNMT1, TOP2A, and TK1 in CC tissues compared with normal colon tissues . No immuWe found that 57 of 110 CC patients (52%) had genetic mutations in these targets . We foun+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells. The expression of HSP90AA1 was negatively correlated with purity. The expression of AURKB and TK1 were positively correlated with infiltration of purity, B cells, neutrophils and dendritic cells. The expression of AURKB and TK1 were negatively correlated with infiltration of CD8+ T cells, CD4+ T cells and macrophages. The expression of CCNA2 was positively correlated with infiltration of purity, B cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells. The expression of CCNA2 was negatively correlated with infiltration of CD4+ T cells. The expression of TYMS was positively correlated with infiltration of B cells, CD8+ T cells, macrophages, neutrophils and dendritic cells. The expression of TYMS was negatively correlated with infiltration of purity and CD4+ T cells. The expression of AURKA was positively correlated with infiltration of purity, B cells, CD8+ T cells, CD4+ T cells, macrophages and neutrophils. The expression of AURKA was negatively correlated with infiltration of dendritic cells. The expression of DNMT1 was positively correlated with infiltration of purity, B cells, CD4+ T cells, macrophages, neutrophils and dendritic cells. The expression of DNMT1 was negatively correlated with infiltration of CD8+ T cells. The expression of CDK2, CHEK1 and TOP2A were positively correlated with the infiltration of purity, B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells  stimulates the class la Pl3K subtype, and binding of chemokines, hormones and neurotransmitters to G protein-coupled receptors (GPCR) stimulates the class lb Pl3K subtype. PI3K catalyzes the production of phosphatidylinositol-3,4,5-trisphosphate (PIP3) in cell membranes. PIP3 acts as a second messenger and helps to activate Akt. Akt can regulate a number of key cellular processes by phosphorylating substrates for apoptosis, protein synthesis, metabolism and the cell cycle, promoting cancer cell growth and survival .The interleukin 17 (IL-17) family is a subgroup of cytokines consisting of IL-17A-F that plays a key role in both acute and chronic inflammatory responses. Studies have shown that when IL-17 signaling pathway expression is inhibited, the number of colorectal tumors is reduced and cancer cells have a reduced ability to proliferate . Cell cyThe targets of curcumin and CC were taken to intersect and 73 potential targets of curcumin for the treatment of CC were obtained. The top 10 core targets  were further screened. CDK2 is a central factor in the oncogenic signaling pathway and has an important role in the tumor process. When CDK2 is inhibited, cancer cells undergo apoptosis and growth arrest . HSP90AAAURKB is involved in the bipolar attachment of spindle microtubules to kinetochores and is a key regulator of cytokinesis onset during mitosis. Histone H3 on serine 10 and serine 28 can be phosphorylated by AURKB, which is associated with chromosome number stability and chromatin condensation during mitosis . AURKB iTYMS is highly expressed in patients with colorectal cancer and non-small cell lung cancer, and patients with lower TYMS mRNA levels have higher survival rates than those with higher expression . CHEK1 hTOP2A is a key enzyme that alters the topology of DNA by binding to double-stranded DNA molecules. The proliferation and invasion of CC cells can be inhibited and apoptosis can be induced by down-regulating the expression of TOP2A . TK1 is The molecular docking results showed that curcumin spontaneously bound to core target proteins. It suggested that curcumin could regulate the biological activity of CC-related targets. The reliability of the core targets of curcumin for CC treatment screened by network pharmacology was verified.In summary, this study systematically illustrated the potential mechanism of curcumin for the treatment of CC through network pharmacology and molecular docking. Curcumin plays an important role in the treatment of CC through multiple targets and pathways after entering the body. The results showed that curcumin could exert anti-cancer effects by binding to CDK2, HSP90AA1, AURKB, CCNA2, TYMS, CHEK1, AURKA, DNMT1, TOP2A, and TK1. Curcumin interferes with tumor cell proliferation and apoptosis by regulating PI3K-Akt signaling pathway,IL-17 signaling pathway, Cell cycle and other signal transduction pathways. These reflect the anti-CC mechanism of curcumin. Due to the poor accuracy and completeness of the database during the network pharmacology study, biological experiments and extensive evidence-based medical validation are still needed at a later stage to ensure the reliability of the study results. Our study provides a new basis for further exploration of the role of curcumin in the treatment of CC and subsequent experimental validation.