Rice husk biochar (RHB) is a low-cost and renewable resource that has been found to be highly effective for the remediation of water and soil environments. Its yield, structure, composition, and physicochemical properties can be modified by changing the parameters of the preparation process, such as the heating rate, pyrolysis temperature, and carrier gas flow rate. Additionally, its specific surface area and functional groups can be modified through physical, chemical, and biological means. Compared to biochar from other feedstocks, RHB performs poorly in solutions with coexisting metal, but can be modified for improved adsorption. In contaminated soils, RHB has been found to be effective in adsorbing heavy metals and organic matter, as well as reducing pollutant availability and enhancing crop growth by regulating soil properties and releasing beneficial elements. However, its effectiveness in complex environments remains uncertain, and further research is needed to fully understand its mechanisms and effectiveness in environmental remediation. Heavy metals are regarded extremely dangerous environmental pollutants because of their high toxicity, carcinogenicity, and nondegradability . MoreoveGlobal rice consumption was approximately 486 million metric tons between 2018 and 2019 . The ricAlthough RHB is widely regarded an environment-friendly adsorbent and soil conditioner, its potential risks cannot be ignored. Compared to other biochar, RHB is characterized by its widely available sources, high yield, and abundance of amorphous silica . Althougrice husk preparation AND rice husk biochar modification AND rice husk biochar (adsorption OR sorption) AND (water OR soil) AND ; only documents published in English were considered.This systematic review used the Web of Science, ScienceDirect, SpringerLink, ACS journal, and Scopus databases. Papers published between 2012 and 2022 were gathered using the search string The thermochemical conversion of biomass fuels has three main modes: combustion, gasification, and pyrolysis. The main products of combustion and slow pyrolysis are biochar, and the main products of gasification and fast pyrolysis are biomass oil and biomass gas . Figure Pyrolysis is popular for its low levels of asset utilization and high recovery rate. It is a complex process wherein the feedstock undergoes thermochemical changes under limited oxygen conditions, producing biochar, biomass oil, and biomass gas. Pyrolysis decomposes the biomass into a mixture of organic and inorganic compounds. Biochar is a carbon-rich, fine-grained, permeable, and aromatically stable solid product . DependiSince rice husk undergoes different chemical reactions under different conditions, the process parameters  have a great influence on the yield, structure, composition and properties of RHB.It has been shown that the pyrolysis temperature is closely related to the changes in the structure and physicochemical properties of RHB. The data about these relationships are shown in RHB is often produced at low heating rates . A low hThe pyrolysis of biomass generates vapors that become involved in secondary reactions if they are not removed in time. This affects the composition of the pyrolysis products and thus the RHB yield. The biochar yield was found to stay the same when the nitrogen flow rate exceeded a certain level . This inVirgin RHB generally has a limited adsorption capacity for pollutants. To improve the applicability of RHB to pollutant remediation, it should be modified. The RHB surface is rich in functional groups, which is favorable for modifier loading . Common Physical modification of RHB is applied during the charring and activation processes. The charring process occurs under anaerobic or anoxic conditions; the rice husks undergo pyrolysis at a certain temperature, which opens some of the pores . The actSeveral approaches are available for chemical modification, of which several are listed below.(1)Acid/alkali modification+ and heavy metals [2/g), total pore volume (from 0.046 to 0.558 cm3/g), and number of micropores (from 59.0% to 78.4–81.9%).Acid modification refers to the treatment of rice husks or RHB with acid reagents  ,77 to chy metals ,78. Zhaoy metals  found th2/g and improved the adsorption capacity for malachite green by 4.3 times.Alkali modification uses alkaline reagents instead, which generally activate the internal structure of RHB and increase the specific surface area to improve the adsorption and fixation capacities . Some al(2)Metal  modification2/g. RHB can also be modified by using iron oxide, magnesium, aluminum, titanium dioxide, manganese dioxide, etc. [Metal  modification changes the specific surface area, metal element content, cation exchange capacity, and surface charge of RHB. For example, Xiang et al.  used magde, etc. . These mde, etc. . In situde, etc. . Teng etde, etc.  found th(3)Functional group modification2+, Zn2+, and Cu2+ [2+ adsorption capacity by 2.2 times to 29.11 mg/g.Functional group modification uses organic compounds with target functional groups that are capable of chemically bonding with the RHB surface, which then loads the target functional group onto the RHB . Functioand Cu2+ . Nitrogeand Cu2+ . Gai et and Cu2+  found thXylella vulgaris) to modify RHB, which improved the adsorption capacity for toluene by 31.6%.Microbial modification uses microorganisms, which act synergistically with RHB to considerably improve the adsorption capacity and remediation effectiveness of target pollutants . RHB is Water pollution poses a serious threat to the environment and human health because of its rapid spread, mobility, and impact. The effective removal of water pollution has been an issue of public concern for many years. RHB is widely used for the adsorption of heavy metals and organic substances in water because of its good stability, recyclability, and adsorption capacity .+, Na+, Mg2+) and heavy metals; complexation of heavy metals with functional groups  on the RHB surface; electrostatic interaction between heavy metals and the RHB surface; and coprecipitation between heavy metals and metal salts on the RHB surface .RHB has five main mechanisms for heavy metal adsorption  77,97,9,997,98: 2+ and Cd2+. RHB, wheat straw biochar, and corncob biochar adsorbed 96.41%, 95.38%, and 96.92%, respectively, of Pb2+ and 94.73%, 93.68%, and 95.78%, respectively, of Cd2+. Higashikawa et al. [2+ and Ni2+ in solution than sawdust biochar. Thus, RHB may be more suitable than biochar produced from other types of agricultural waste for adsorbing heavy metals in water.RHB may be able to adsorb more heavy metals in water than biochar produced from other agricultural wastes. Agricultural wastes such as wood, straw, and shells are often used as a raw material for biochar. One ton of agricultural waste is estimated to yield about 0.3 tons of biochar ,100. Taba et al.  showed t2+ adsorption, followed by cotton stem biochar, whereas RHB performed the worst. Yak dung biochar also demonstrated the best Pb2+ removal, and the worst was poplar wood biochar. However, RHB demonstrated the second-worst removal of Pb2+, which was only better than poplar wood biochar [2+ and Cd2+ by RHB was completely inhibited in a multi-metal solution system. Compared with the monometallic solution system, the adsorption capacities for Pb2+, Cu2+, Zn2+, and Cd2+ of dairy manure biochar were reduced by 2.00%, 21.1%, 40.9%, and 39.3%, respectively, in a polymetallic solution system. In contrast, the adsorption capacities of RHB were reduced by 38.4%, 42.7%, 92.3%, and 100%, respectively. Similarly, Zhang et al. [2+ and Pb2+ coexisting in solution.However, RHB has a poor adsorption capacity for specific metal ions in a polymetallic solution ,117. A s biochar . In addi biochar . Xu et a biochar  found thg et al.  found thRHB mainly removes organic pollutants from water via adsorption and the electrostatic effect. The adsorption mechanism of organic pollutants by RHB is related to the pyrolysis temperature. RHB prepared at low pyrolysis temperatures (100 °C–300 °C) mainly adsorbs organic pollutants by partitioning. RHB prepared at higher pyrolysis temperatures (400 °C–700 °C) mainly adsorbs organic pollutants through chemisorption . This is because RHB produced at low temperatures has a high ash content. This causes inorganic minerals to occupy most of the adsorption sites on the RHB surface, rendering partitioning the main adsorption mechanism . In contpzc). In general, the RHB surface is positively charged when the solution pH is less than pHpzc. This inhibits the adsorption of cations in solution by RHB because of electrostatic repulsion. When solution pH > pHpzc, the RHB surface is deprotonated, rendering it negatively charged. This enhances the adsorption capacity of cations in solution because of electrostatic attraction [The magnitude of the electrostatic effect is related to the pH value and zero point charge There is a necessity to strengthen the risk assessment and toxicology experiments on RHB as it is an adsorbent material with potential health hazards .(2)In the study of new RHB modification methods, the adsorption performance and secondary pollution characteristics of RHB must be comprehensively evaluated to design a nonpolluting modification method.(3)The natural environment often contains many different types of pollutants. Therefore, future research should focus on the comprehensive study of the sorption mechanism of RHB on one or several specific pollutants under coexistence of different types of pollutants .(4)To better utilize RHB in soil improvement, it is necessary to conduct pot trial studies to evaluate interactions between soil microorganisms and RHB, such as antagonistic and synergistic effects .(5)Long-term and regional field trials are required to study the value of RHB in agricultural applications to provide an accurate assessment of the use of RHB in this field.Herein, we focused on the preparation and modification of RHB for the remediation of water and soil environments. Through an extensive examination of numerous studies on RHB production, we revealed that the parameters in the RHB preparation process have a considerable influence on the adsorption behavior of RHB. The adsorption capacity of unmodified biochar is limited. Generally, the specific surface area and functional groups of RHB can be enhanced through physical, chemical, and biological modification. RHB can remove pollutants from the environment through adsorption, ion exchange, electrostatic interaction, complexation, and cation exchange. However, RHB tends to be poor at adsorbing heavy metals when multiple heavy metals coexist in an environment. Moreover, RHB is an effective soil conditioner because it not only adsorbs heavy metals and organic matter but also improves crop growth by improving soil conditions and releasing beneficial elements . However, the pollutant adsorption capacity and remediation effectiveness of RHB can be affected by various factors in complex environments. This adds uncertainty to the results reported in the literature, and the mechanisms by which the effectiveness of RHB is affected require further investigation.


Atractylodes lancea (Thunb.) DC. is a traditional chinese medicine with good anti-inflammatory activity that is commonly used clinically for the treatment of lung diseases in China; however, its mechanism of against ALI is unclear. We clarified the therapeutic effects of ethanol extract of Atractylodis rhizoma (EEAR) on lipopolysaccharide (LPS)-induced ALI by evaluation of hematoxylin-eosin (HE) stained sections, the lung wet/dry (W/D) ratio, and levels of inflammatory factors as indicators. We then characterized the chemical composition of EEAR by ultra-performance liquid chromatography and mass spectrometry (UPLC-MS) and screened the components and targets by network pharmacology to clarify the signaling pathways involved in the therapeutic effects of EEAR on ALI, and the results were validated by molecular docking simulation and Western blot (WB) analysis. Finally, we examined the metabolites in rat lung tissues by gas chromatography and mass spectrometry (GC-MS). The results showed that EEAR significantly reduced the W/D ratio, and tumor necrosis factor-α (TNF-α), interleukin-1 beta (IL-1β), interleukin-6 (IL-6) levels in the lungs of ALI model rats. Nineteen components of EEAR were identified and shown to act synergetically by regulating shared pathways such as the mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K)-protein kinase B (AKT) signaling pathways. Ferulic acid, 4-methylumbelliferone, acetylatractylodinol, atractylenolide I, and atractylenolide III were predicted to bind well to PI3K, AKT and MAPK1, respectively, with binding energies < -5 kcal/mol, although only atractylenolide II bound with high affinity to MAPK1. EEAR significantly inhibited the phosphorylation of PI3K, AKT, p38, and ERK1/2, thus reducing protein expression. EEAR significantly modulated the expression of metabolites such as D-Galactose, D-Glucose, serine and D-Mannose. These metabolites were mainly concentrated in the galactose and amino acid metabolism pathways. In conclusion, EEAR alleviates ALI by inhibiting activation of the PI3K-AKT and MAPK signaling pathways and regulating galactose metabolism, providing a new direction for the development of drugs to treat ALI.Acute lung injury (ALI) is characterized by an excessive inflammatory response.  Acute lung injury (ALI) is characterized by pulmonary diffusion dysfunction caused by a variety of direct or indirect factors, such as trauma, infection, noxious gas inhalation, and shock . The cliThe core theory of the mechanism of ALI is that an imbalance in the inflammatory response exacerbates epithelial or endothelial damage resulting in protein-rich edema fluid entering the alveoli . HistoloIn recent years, network pharmacology has emerged worldwide as an approach that is based on theories from disciplines such as systems biology, genomics, and proteomics. Network pharmacology uses technologies such as high-throughput genomic data analysis, computer simulation and network database searches to reveal the network relationship of drug-gene-target-disease interactions and predict the mechanism of action of drugs based on the network relationship . Since TIn addition, metabolomics has gained popularity as an approach to studying the effects of drugs and diseases based on the use of modern analytical techniques to detect the changes of metabolites in biological systems . This apAtractylodes lancea (Thunb.) DC. is commonly used clinically to treat lung diseases and gastrointestinal diseases  = weight of dried ethanolic extract powder of Atractylodis rhizoma/weight of raw Atractylodis rhizoma herb × 100%Samples of Atractylodis rhizoma prepared using a previously described method  were purWe calculated the experimental doses of the ethanol extract of Atractylodis rhizoma (EEARL) according to the human and rat drug dose relationship of human and rat as previously described . Accordig) for 10 min at 4°C and the supernatant was filtered (0.22 μm pore size) until further use was required.Sample pretreatment was conducted as previously described . BrieflyThe standards atractylodin (PU02410025), chlorogenic acid (PS01311000), ferulic acid (PS01911000), acetylatractylodinol (PS22120806), 4-methylumbelliferone (PS020119), and atractylenolides I (PS010510), II (PS010511), and III (PS010512) were purchased from Chengdu Push Biotechnology Co., Ltd., . All standards were dissolved in methanol at a concentration of 1 mg/mL.UPLC was performed with the Agilent 1,260 Infinity system. We used a Welch Xtimate C18 column  for the separation at 25°C. The mobile phase was acetonitrile (B)-aqueous formic acid  (A), delivered at a flow rate of 0.3 mL/min, using an injection volume of 5 µL. The elution conditions were set as follows: 20%–50% B: 0–15 min; 50%–60% B: 15–30 min; 60%–95% B: 30–35 min; 95% B: 35–45 min; 95%–20% B: 45–46 min; 20% B: 46–56 min.We used an LTQ-Orbitrap-MS spectrometer for the mass spectrometry as previously described . The ionAll male Sprague-Dawley (SD) rats (130 ± 10 g) were purchased from the Three Gorges University . The animal license is No. SCXK (E) 2022-0017. All procedures in the experiments met the ethical requirements and were approved by the Ethics Committee of Hubei University of Traditional Chinese Medicine . Rats were housed in an environment maintained at 24°C ± 1°C and humidity of 45%–60%.via intratracheal instillation to all groups  2 h after saline or drug administration by gavage  was centrifuged to obtain the supernatant. The expression of TNF-α, IL-1β, IL-6 and monocyte chemoattractant protein-1 (MCP-1) in the supernatant was measured by enzyme-linked immunosorbent assay (ELISA) kits  according to the manufacturers’ instructions.Immunohistochemical (IHC) staining of TNF-α, IL-6 and IL-1β expression in lung tissues was performed as previously described . BrieflyWestern blot analysis was performed as previously described . BrieflyAntibodies for the detection of P38 (8690S), p-P38 (4511S), Erk1/2 (4695S), p-Erk1/2 (4370S), AKT (4691S), p-AKT (4060S), PI3K (4249S), β-actin (3700S) and p-PI3K (17366S) were obtained from Cell Signaling Technology, Inc. .Rat lung tissues were fixed in a 4% formaldehyde solution, embedded in paraffin and stained with HE.https://pubchem.ncbi.nlm.nih.gov/) and then entered into SwissADME (http://swissadme.ch/) (The Isomeric SMILES representations of each compound were obtained from PubChem (dme.ch/) ; 2017. Shttps://old.tcmsp-e.com/), SwissTargetPrediction (http://www.swisstargetprediction.ch/) (https://www.drugbank.com/) databases. All targets were summarized and duplicates were deleted. The targets of ALI were identified by searching the OMIM (https://www.omim.org/), GeneCards (https://www.genecards.org/) and DrugBank (https://www.drugbank.com/) databases. All targets were then summarized and duplicates were deleted.The targets of the remaining compounds were retrieved through the TCMSP (ion.ch/) , and Druhttps://cn.string-db.org/, interaction score >0.9 implies high confidence) to obtain the PPI network-related information. Visual analysis was then performed on Cytoscape 3.7.0. Two visualized networks were constructed: (1) Target-Target network (PPI). (2) Compounds-Targets-Pathways network. All networks were constructed by Cytoscape 3.7.0.Targets were analyzed in the STRING database  (https:/https://david.ncifcrf.gov/) (https://www.omicshare.com/).To explore the mechanism underlying the effects of EEAR on ALI, GO and KEGG analyses were performed by using the DAVID (rf.gov/) . Visual https://ww.rcsb.org/) and used AutoDock Tools version 1.5.6 for molecular docking analysis, which was visualized in PyMol software.Molecular docking simulation of component targets was performed as previously described . Brieflyhttps://www.metaboanalyst.ca/) and biomarkers were screened by VIP >1 and p < 0.05.GC-MS analysis of the endogenous metabolites was performed as previously described . Brieflypost hoc test. Statistical analysis was performed using GraphPad 8.0. p < 0.05 was set as the threshold for statistical significance.Data were expressed as means ± standard error of the mean (SEM) and analyzed by a one-way ANOVA, followed by Dunnett’s p < 0.05), while there was a significant decrease in the EEARH group (p < 0.05)  . Evaluat < 0.05) . Thus, tp < 0.01) (p < 0.05). The expression levels of these inflammatory factors were also reduced in the EEARL group, although the effects were not statistically significant. In addition, IHC analysis showed that the expression of TNF-α, IL-1β, and IL-6 were significantly increased in the Model group, while the expression of all these factors was decreased to varying degrees after drug administration, with the most significant effects observed in the EEARH and Dex groups  , while tx groups .We characterized the composition of EEAR by UPLC-MS . A totalTo clarify the mechanism by which EEAR alleviates ALI, we used network pharmacology for further analysis of the UPLC-MS data. First, we entered the data for each compound into the SwissADME database and screened the compounds based on the results of Lipinski, Ghose, Veber, Egan, Muegge and gastrointestinal absorption analyses. Based on the screening criteria, we identified 13 eligible compounds . We thenTo investigate the biological functions of the target genes, we performed three GO functional analyses on the top 100 targets. This approach identifies targets enriched in the categories of biological processes, cellular components and molecular functions. The targets were mainly enriched in protein phosphorylation, signaling and regulation of apoptotic processes, protein kinase binding and protein kinase activity . The resTo further clarify the mechanism underlying the therapeutic effects of EEAR in ALI, we constructed “compounds-targets-pathways” network map using Cytoscape. We filtered the results by degree-value ranking and found that the top ranked compounds were ferulic acid, acetylatractylodinol, 4-methylumbelliferone, and atractylenolides I, II, and III. The top ranked targets, MAPK1, AKT1, PIK3CA and MAPK14, were implicated as the key targets of EEAR for the treatment of ALI. The top ranked signaling pathways included PI3K-AKT the MAPK signaling pathways .We next further validated the results of the network pharmacology analysis, by performing molecular docking simulation. This technique predicts the potential for binding between the target and compound, and assesses the affinity between the compound and the target protein by comparing the docking fraction with that of the original ligand. We selected the top six compounds and the top three targets for molecular docking simulation. All six compounds were predicted to bind to with high affinity to MAPK1 (PDB ID:6RQ4), with binding energies < −5 kcal/mol. With the exception of atractylenolide II, the other five compounds bound with high affinity to AKT1 (PDB ID:6HHF) and PIK3CA (PDB ID:7K6N), with binding energies < −5 kcal/mol. Therefore, we identified MAPK1, AKT1, and PIK3CA as the main potential targets of EEAR in the treatment of ALI .p < 0.01) and decreased to varying degrees after EEAR treatment, with the most significant decrease in the EEARH group (p < 0.01)  . Therefop < 0.05 in Student’s t-test , while the levels of Serine, L-Alanine, and L-Proline were significantly decreased (p < 0.05). The levels of all of these metabolites recovered to varying degrees after treatment with EEAR.Based on VIP >1 in the OPLS-DA model combined with  present , 12 of w present . Cluster present . Metaboa present . Among t present . As showThe inflammatory response is an important pathological mechanism responsible for ALI . FollowiAn increasing number of studies have shown that inhibiting the release of pro-inflammatory factors and modulating the immune system can promote the improvement of endothelial function in patients with ALI . Thus, iAtractylodis rhizoma has good anti-inflammatory activity. In the present study, we clarified the therapeutic effect of A. rhizoma on ALI by measuring the W/D ratio of the lung, analyzing the expression of inflammatory factors in the lung, and evaluating the pathology in histologically processed tissue sections.TCM has great potential to prevent and treat ALI by interfering with the signaling pathways and targets of ALI through different pharmacological effects . Thus, TNetwork pharmacology considers the occurrence and development of diseases as a long-term and complex process caused imbalanced expression of multiple target genes and their products. This approach can be used for systematic analysis of “diseases-targets-drugs” interaction networks and reveal the mechanism of drug synergy. The emergence of network pharmacology has promoted a more accurate understanding of the mode of action and the main active ingredients of TCMs from the holistic “multi-component-multi-target-complex disease” perspective. In this study, we used UPLC-MS to analyze the chemical composition of EEAR and further clarified the mechanism by which EEAR alleviates ALI by network pharmacology analysis. We found that the main active components involved in the mechanism by which EEAR alleviates ALI were identified as ferulic acid, acetylatractylodinol, 4-methylumbelliferone, and atractylenolides I, II and III. In addition, our research indicated that the therapeutic effects of EEAR on ALI may be mediated by regulating the PI3K-AKT and MAPK signaling pathways. Studies have shown that the PI3K-AKT and MAPK signaling pathways are the major intracellular signaling pathways that regulate inflammation and are closely related to LPS-induced ALI . FurtherMetabolomics techniques have been used to study metabolites associated with ALI in animals and patients with the aim of identifying disease-related biomarkers and novel drug targets . For exaIn addition, we found that EEAR significantly reduced the levels of D-Galactose. Under normal conditions, galactose is converted to glucose, but in excess, it is metabolized to generate ROS and advanced glycation end-products, which act on advanced glycosylation end-product receptors . These mThere are few reports on the treatment of ALI by EEAR. In our study, we combined metabolomics and network pharmacology to clarify the effect of EEAR on ALI, and conducted a preliminary study of the mechanism. In addition, we initially clarified the main active components of EEAR for the treatment of ALI. Thus, further research is needed to fully elucidate the mechanism by which EEAR alleviates ALI, and the identity of the component that plays the main role in this process.A. rhizoma in ALI and also provide a rational basis for the clinical application of TCMs.In conclusion, the multi-compound, multi-target and multi-mechanism mechanism underlying the therapeutic effects of EEAR on ALI were systematically elucidated by integrating network pharmacology and metabolomics . We prov


Ischemic strokes (ISs) are commonly treated by intravenous thrombolysis using a recombinant tissue plasminogen activator; however, successful treatment can only occur within 3 hours after the stroke. Therefore, it is crucial to determine the causes and underlying molecular mechanisms, identify molecular biomarkers for early diagnosis, and develop precise preventive treatments for strokes. We aimed to clarify the differences in gene expression, molecular mechanisms, and drug prediction approaches between IS and myocardial infarction (MI) using comprehensive bioinformatics analysis. The pathogenesis of these diseases was explored to provide directions for future clinical research. The IS (GSE58294 and GSE16561) and MI (GSE60993 and GSE141512) datasets were downloaded from the Gene Expression Omnibus database. IS and MI transcriptome data were analyzed using bioinformatics methods, and the differentially expressed genes (DEGs) were screened. A protein–protein interaction network was constructed using the STRING database and visualized using Cytoscape, and the candidate genes with high confidence scores were identified using Degree, MCC, EPC, and DMNC in the cytoHubba plug-in. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed using the database annotation, visualization, and integrated discovery database. Network Analyst 3.0 was used to construct transcription factor (TF) – gene and microRNA (miRNA) – gene regulatory networks of the identified candidate genes. The DrugBank 5.0 database was used to identify gene–drug interactions. After bioinformatics analysis of IS and MI microarray data, 115 and 44 DEGS were obtained in IS and MI, respectively. Moreover, 8 hub genes, 2 miRNAs, and 3 TFs for IS and 8 hub genes, 13 miRNAs, and 2 TFs for MI were screened. The molecular pathology between IS and MI presented differences in terms of GO and KEGG enrichment pathways, TFs, miRNAs, and drugs. These findings provide possible directions for the diagnosis of IS and MI in the future. The prevalence of stroke has increased by 20.5% since 2012, and it is predicted that by 2030, approximately 3.9% of adults in the United States will have had a stroke. Ischemic stroke (IS) accounts for 87% of all strokes. Moreover, acute IS usually occurs suddenly without any distinct early warning sign. It progresses rapidly and can lead to serious complications and even long-term disability. The most common treatment for IS is intravenous thrombolysis using recombinant tissue plasminogen activator; however, the window for treatment is only 3 hours. Thus, it is crucial to determine the causes and underlying molecular mechanisms of stroke, identify molecular biomarkers for early diagnosis, and develop precise preventive treatments.Globally, stroke is characterized by high morbidity and mortality and is the second leading cause of death. MI is a common disease with a high mortality rate that results in health and economic challenges worldwide. Furthermore, acute MI (AMI) is the leading cause of disability and death worldwide. Cardiogenic shock after MI is most commonly caused by severe left ventricular dysfunction and is the most serious complication of AMI, leading to mortality and morbidity. Furthermore, treatment options for heart failure after MI are limited. IS is a serious complication after the onset of MI. Thus, new biomarkers should be identified to better diagnose and treat MI. Although there is evidence of the pathophysiological relationship between IS and MI, to the best of our knowledge, differences between the molecular markers of the 2 diseases have not been reported. Therefore, investigating the differences between the 2 diseases at the molecular level will help better analyze their respective pathogenesis and provide a more accurate direction for future diagnosis and treatment.Myocardial infarction (MI) is caused by the blockage of blood flow in one of the main coronary arteries supplying the heart muscle, usually because of the rupture of atherosclerotic plaques and thrombosis. Prolonged ischemia leads to expansion, necrosis, apoptosis, and necrotic death of the myocardium.In this study, we aimed to distinguish the molecular markers of IS and MI using bioinformatics methods and to provide possible directions for the diagnosis of IS and MI in the future.As all the data were obtained from online databases, written informed consent from patients had already been obtained. Moreover, our study was based on open-source data; thus, there are no relevant ethical issues.Homo sapiens” were searched in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). After screening, 4 datasets  were identified as target datasets, and the corresponding gene expression files were downloaded.Figure The GSE58294 dataset was identified from the GPL570 sequencing platform and included 69 cardioembolic stroke and 23 control group samples. The GSE16561 dataset was identified from the GPL6883 sequencing platform and included 39 patients with IS and 24 healthy control samples. The GSE60993 dataset was identified from the GPL6884 sequencing platform and included 17 patients with MI and 16 healthy cohort participants. The GSE141512 dataset was identified from the GPL17586 sequencing platform and included 6 patients with MI and 6 healthy cohort participants. The gene expression matrix and associated annotation files for each array dataset were downloaded, and the probe IDs were converted to gene names using the corresponding annotation files. Multiple probes were averaged for mapping to the same symbol. Using perl v5.30.0 software, the 2 IS datasets, GSE58294 and GSE116561, and the 2 MI datasets, GSE60993 and GSE141512, were combined to extract the same genes. DEG heatmaps were generated , which is widely used to remove batch effects in high-throughput experiments. The “limma” and “pheatmap” R packages were used to filter genes with missing values and vacancies according to the screening conditions, |logFC| > 0.58 and adj.P.Val.Filter ≤ 0.05. Several important genes were identified for each disease, and the differential dysregulation of the identified genes was used to predict the occurrence of the disease.ted Fig. .https://metascape.org/gp/index.html#/main/step1) was used to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and GO enrichment analyses on key DEGs using the following criteria: min overlap, 3; P value cutoff, .01; and min enrichment, 3.To explore the cellular pathways and GO characteristics affected by DEGs, the database for annotation, visualization, and integrated discovery software Metascape  was used to construct PPI networks of proteins encoded by the selected genes using Cytoscape v3.9.1. The cutoff criterion was a confidence score ≥ 0.4. Candidate gene-encoded protein interactions were analyzed.The PPI network is at the core of the cellular/molecular mechanism that constitutes the interaction between 2 or more protein components.https://www.networkanalyst.ca/) was used to visualize the interaction network between DEG-TFs and DEG-miRNAs. JASPAR (https://jaspar.genereg.net/) and the Encyclopedia of DNA Elements (ENCODE) databases were used to identify and study DEG-TF relationships. The TarBase v8.0 and miRTarBase v8.0 databases were used to study miRNA-DEG interactions.NetworkAnalyst (https://go.drugbank.com/).NetworkAnalyst was used to identify possible drugs to treat IS, MI, and associated diseases. The drug predictions were performed using the DrugBank 5.0 database  and 29 compounds. Five nodes for MI contained 1 gene (HCK) and 4 compounds, as shown in Figure A total of 19 central IS and 13 central MI proteins were used for drug prediction. The IS protein–drug interaction is shown in Figure Salmonella infection, hematopoietic cell lineage, the nuclear factor-kappa B (NF-κB) signaling pathway, viral protein interaction with cytokine and cytokine receptor, and the C-type lectin receptor signaling pathway. MI DEGs were primarily enriched in pathways, such as the NF-κB signaling pathway, osteoclast differentiation, and tuberculosis. The results of the PPI network and cytoHubba analyses showed a correlation between DEGs and 8 candidate genes with high confidence scores related to IS: FCGR1A, CCR7, CLEC5A, GZMK, CD163, MMP9, S100A12, and IL7R. The candidate genes with high confidence scores associated with MI were MYD88, SPI1, HCK, LILRB3, CD300LB, PRAM1, IRF8, and SLC11A1. Via visualization of DEG-TFs, the TFs FOXC1, YY1, and HDGF were predicted to be important for IS, whereas via the visualization of DEG-miRNAs, hsa-miR-27a-3p and hsa-miR-335-5p were predicted to be crucial for IS. The TFs HBP1 and FOXC1 were important for MI, whereas visualization of DEG-miRNAs revealed hsa-miR-34a-5p, hsa-mir-9-5p, hsa-miR-155-5p, hsa-miR-339-5p, hsa-miR-526b-5p, hsa-miR-654-3p, hsa-miR-4533, hsa-miR-4755-5p, hsa-miR-5006-3p, hsa-miR-34a-5p, hsa-miR-155-5p, hsa-miR-27a-3p, and hsa-miR-129-2-3p to be important for MI. We also used DEG–drug predictive networks to identify drugs for IS and MI.In this study, 115 IS, 44 MI, and one common DEG were screened using the GEO database. GO functional analysis results showed that IS DEGs were involved in the regulation of biological processes, such as specific granule, specific granule membrane, and the immune response-regulating signaling pathway. MI DEGs were primarily involved in the response to type II interferon, myeloid leukocyte activation, and secretory granule membrane. KEGG enrichment analysis results showed that these DEGs were enriched in signaling pathways. IS DEGs were primarily enriched in transcriptional misregulation in cancer,  However, there are still differences in the pathogenesis and molecular markers of the 2 diseases. The top 8 genes highly expressed in IS with high scores in 4 algorithms were selected. FCGR1A is an important gene in IS, and CCR7 is a central chemokine receptor.,15 Dendritic cell (DC) migration in the brain parenchyma depends on the chemokine receptor CCR7. DCs accumulate in the central nervous system during neuroinflammation, and the immune system plays a role in the pathology of IS.IL7R is a novel therapeutic target that promotes functional and histological recovery after IS. Following ischemic brain injury, the ensuing neuroinflammation can lead to additional damage and cell death.CD163 is a monocyte/macrophage-specific marker primarily expressed on cells with strong anti-inflammatory potential. CD163-expressing mononuclear phagocytes, as well as soluble CD163, may be involved in the downregulation of inflammatory responses.CD163 is also a potential inflammatory biomarker and therapeutic target. Thus, research on CCR7 and CD163 is of great importance in controlling inflammation after IS. CLEC5A is a C-type lectin domain family 5 member, which is related to the progression of various acute and chronic inflammatory diseases, and is primarily expressed in macrophages of atherosclerotic lesions. Its increased expression is associated with early plaque progression, promoting macrophage survival. Rupture of unstable atherosclerotic plaques is the pathological mechanism underlying acute ischemic events. High serum levels of MMP9 in the acute phase of IS are associated with an increased risk of death and severe disability. Therefore, serum MMP9 levels may be an important prognostic marker for IS.The pathophysiological mechanisms and common risk factors of cardiovascular and cerebrovascular diseases, including age, hypertension, and diabetes, are similar.MYD88 is a key player in the inflammatory response, and evidence of pathological features indicates that it plays a role in cellular signaling pathways involved in the survival, proliferation, apoptosis, and autophagy of cardiomyocytes, endothelial cells, fibroblasts, monocytes, and stem cells during MI. Targeting these abnormal signaling pathways may help improve the pathological manifestations of MI.MYD88 is of great value in reducing inflammation and restoring myocardial function after MI. The development of heart failure (HF) remains a common complication after AMI and is associated with severe adverse outcomes. HCK and IRF8 were identified to be important in predicting the occurrence of HF after MI. Related studies have found that the upregulation of SPI1 expression during MI can aggravate cardiac tissue damage and disease progression by activating the TLR4/NF-κB axis. Atherosclerosis is the main pathological basis of cardiovascular and cerebrovascular diseases.LILRB3 is a reliable molecular biomarker for plaque status changes in stable coronary artery disease, ST-segment elevated MI progression, and MI recurrence.The top 8 genes highly expressed in MI with high scores in 4 algorithms were selected. Salmonella infection, hematopoietic cell lineage, the NF-κB signaling pathway, viral protein interaction with cytokine and cytokine receptor, and the C-type lectin receptor signaling pathway. The NF-κB pathway is a typical proinflammatory signaling pathway, playing a crucial role in the functional outcome after stroke.,31 In fact, in vitro studies have found that the Sirt1/NF-κB pathway substantially affects cerebral IS in rat neurons affected by apoptosis. Moreover, research in mice shows that inhibition of the NF-κB/NLRP3 signaling pathway partially reduced stroke-induced white matter damage and alleviated microglial pyroptosis. C-type lectin-1/Syk signaling plays a key role in inflammatory activation in IS, and the C-type lectin-1 antagonist LAM reduced infarct volume and improved neurological function 3 days after IS. MI DEGs were primarily enriched in pathways such as the NF-κB signaling pathway, osteoclast differentiation, and tuberculosis. Related studies have shown that osteoclast differentiation may play an important role in the acute phase of AMI.There also exist differences between the 2 diseases regarding pathways. IS pathways are primarily enriched in transcriptional misregulation in cancer, ,36 The transcription of the identified TF FOXC1 in IS is induced by MDL-811 stimulation and is required for the anti-inflammatory effect of MDL-811. MDL-811 is also expressed in human monocytes isolated from IS patients. Studies have shown that neuroinflammation is considered a key factor in the progression of stroke and is of great importance during candidate drug screening for alternative treatments for IS. The HBP1 TF downregulates the expression of HMGB1 and its related TLR4 and NF-κB, which has a protective effect against MI.TFs and miRNAs regulate gene expression. The interaction between TF proteins and DNA underlies the regulation of transcription, a coordinated process that responds to environmental factors to achieve temporal and tissue specificity. hsa-miR-27a-3p and hsa-miR-335-5p were crucial in IS, whereas hsa-miR-34a-5p was crucial in MI.,41 The above information may reveal some unique mechanisms underlying the diseases, suggest new disease markers, and provide a possible basis for predicting IS and MI, as well as identify potential therapeutic targets. The prediction of drugs may have a role in disease prevention.miRNAs regulate gene expression by blocking mRNA translation and play an important role in many diseases.However, this study has certain limitations. The data were derived from public databases, which lacked clinical information for analysis. In addition, the limited sample size prevented the data from being divided into derivation and validation sets. Moreover, the lack of experimental research and validation is a shortcoming. Finally, no in vitro or in vivo assays were performed to validate the identified molecular biomarkers.In this study, core DEGs related to IS and MI were detected using bioinformatics. KEGG pathways and GO terms were investigated. TFs and miRNAs important to core DEGs and gene–drug interrelationships were also analyzed. These core DEGs and regulatory molecules may be involved in the mechanisms underlying the development of IS and MI and thus may be used as potential diagnostic and therapeutic targets for these diseases.Resources: Guangming Wang.Software: Min Wang, Yuan Gao.Visualization: Huaqiu Chen, Ying Shen.Writing – original draft: Min Wang, Yuan Gao.Writing – review & editing: Jianjie Cheng, Guangming Wang.


Although it is widely used clinically and has a remarkable curative effect, the mechanism of RP treatment for endometriosis (EMs) remains unclear due to its complicated components. The aim of this study was to investigate the anti-endometriosis effect of RP, with emphasis on apoptosis via network pharmacology prediction, molecular docking and experimental verification.The related ingredients and targets of RP in treating EMs were screened out using Traditional Chinese Medicine Systems Pharmacology (TCMSP), Tool for Molecular mechanism of Traditional Chinese Medicine (BATMAN-TCM), and GeneCards database. The data of the protein–protein interaction (PPI) network was obtained by the Search Tool for the Retrieval of Interaction Gene/Proteins (STRING) Database. The Metascape database was adopt for Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis. After that, the molecular docking of the main active ingredients and apoptosis targets was performed. Finally, the pro-apoptotic effect of RP was verified in hEM15a cells.A total of 32 RP compounds were collected. Forty-two matching targets were picked out as the correlative targets of RP in treating EMs. Among these, 18 hub targets including P53, CASP3 were recognized by the PPI network. KEGG enrichment analysis discovered that the regulation of apoptosis was one of the potential mechanisms of RP against EMs. Anthraquinone compounds, flavonoids, and triterpenes in RP were identified as crucial active ingredients, involved in the pro-apoptotic effect, which were confirmed subsequently by molecular docking. Additionally, it was verified that RP treatment promoted apoptosis and inhibited the proliferation of EMs cells (assessed by MTT and Flow cytometry). Moreover, the induction of apoptosis in treated EMs cells may be due to the regulation of apoptosis-related protein expression, including P53, BAX, and CASP3.The results of our study demonstrated that RP may exert its therapeutic effects on EMs through the potential mechanism of promoting apoptosis. Anthraquinones, flavonoids and triterpenoids are the possible pro-apoptotic components in RP.The online version contains supplementary material available at 10.1186/s12906-023-04084-8. Approximately 5–20% of women of their reproductive age are diagnosed with endometriosis (EMs) , which iRheum palmatum L., Rhizome and root , and Prunus persica (L.) Batsch, seed . In traditional Chinese medicine, RP has been utilized for the treatment of pain-related disorders. In the case of EMs, it is frequently administered to non-pregnant patients to alleviate symptoms of dysmenorrhea and effectively minimize the incidence of endometrial lesions. The voucher specimen (CNPC2009) of the two plants has been gathered and favorited in the Chinese Field Herbarium for our identification. RP was prepared with the following steps: The pretreated Rhubarb and Peach Kernel were extracted by twice heating water, and then the extracts were combined and filtered. After that, it is vacuum concentrated into a liquid extract with a specific gravity of 1.20–1.35. Finally, spray-dried the liquid extract into granules. Studies have shown that the pharmacological action of RP may relate to proliferation inhibition, apoptosis promotion, oxidative stress improvement, and estrogen effect regulation [The “Rhubarb-Peach kernel” herb pair (RP) is a core medical pair of classical disperse stasis formula and consists of two TCM drugs: gulation –19. Howeimen CNPC09 of theTherefore, in this study, we aim to investigate the key active components of RP and their potential mechanisms for EMs treatment by network pharmacology prediction, molecular docking and experimental verification. The framework of the research is shown in Fig. https://tcmspw.com/tcmsp.php) [https://bionet.ncpsb.org/batman-tcm/) [https://www.uniprot.org/uniprot/).The Traditional Chinese Medicine Systems Pharmacology Database Analysis Platform   and the an-tcm/)  were adoan-tcm/) . Potentihttps://www.genecards.org/) [https://www.omim.org/) [In the GeneCards database (ds.org/)  and OMIMim.org/) , "endomehttps://string-db.org/) [The protein data of overlapping targets were imported into the Search Tool for the Retrieval of Interaction Gene/Proteins database  . The conThe PPI network diagram is exported in TSV file format and imported into the visualization software Cytoscape 3.7.2 . “Degreehttps://metascape.org/) [P-value < 0.05, we screened out the main pathways of clustering and sorting.Metascape database was chosen to perform the Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis (pe.org/) . With thhttps://www.kegg.jp/) was used to retrieve the significant pathways corresponding to the target to build the “target-pathway” network. Then, the two networks were integrated into the final “component-target-pathway” network.The screened active components and their corresponding targets were imported into Cytoscape 3.7.2 to construct a “component-target” network. The KEGG mapper tool in the KEGG database (https://www.rcsb.org/) was used to search the 3D structure files of the active components. The 3D structure files of target proteins were obtained through the PubChem (https://pubchem.ncbi.nlm.nih.gov/) and ZINC (https://zinc.docking.org/) databases. Autodock (https://autodock.scripps.edu/) molecular docking software was adopted to dock active compounds with target proteins. The binding sites were analyzed by PyMOL software. The binding ability between them was evaluated by binding energy, and a value ≤ -5 kcal/mol was considered as tight binding.The RCSB Protein Data Bank . The high performance liquid chromatography system was used for the chemical component identifications of RP extract . Then, the apoptosis of hEM15a cells was evaluated. After the cells were suspended in 500 μL binding buffer, annexin V-PI solution was added and cultured for 5—15 min without light . After that, the percentage of apoptotic cells was detected on a flow cytometer.The total protein of hEM15a cells was extracted with 120 μL RIPA buffer containing PMSF and phosphatase inhibitors. Then, it was resolved by polyacrylamide gel electrophoresis and transferred to polyvinylidene fluoride membranes, which were blocked overnight in TBST containing 5% skim milk powder. Primary antibodies against the following proteins were used: P53 , BAX , CASP3 , and GAPDH . The blots cut prior to hybridisation with antibodies. Subsequently, the matched secondary antibody was added to the membrane, and BandScan was used to analyze the gray value of the films.P-value were less than 0.05.All results were expressed as mean ± standard. IBM SPSS 20.0 software was used for one-way analysis of variance test for statistical analysis. Differences between groups were considered to be statistically significant if the According to the retrieval of TCMSP and BATMAN-TCM database, there are 157 RP-related ingredients in total, including 92 ingredients in Rhubarb and 66 ingredients in Peach kernel. Combined with screening criteria  and literature retrieval, a total of 32 active ingredients in RP were obtained, as shown in Table After searching, 32 active ingredients correspond to 96 protein targets. A total of 1840 disease targets of EMs were obtained through the GeneCards database and OMIM database. Then, 42 overlapping targets were obtained, which were considered potential targets for RP in treating EMs, as shown in Fig. P-value < 1.0e-16. The nodes included JUN, TP53, EGF, TNF, MYC, ESR1, PPARG, PTGS2, MMP9, CASP3, IL1B, TGFB1, AR, CASP8, NOS3, NCOA2, NCOA1, and RXRA, as shown in Fig. Through the STRING database, a PPI network of those 42 overlapping targets was obtained. With confidence ≥ 0.7, DC ≥ 8 and BC ≥ 0.012948, the PPI network with 18 nodes and 115 edges was obtained. The average node degree was 12.778 and the enrichment P-value ≤ 0.05, such as IL-17 signaling pathway (hsa04657), endocrine resistance (hsa01522), P53 signaling pathway (hsa04115), apoptosis (hsa04210), PI3K-Akt signaling pathway (hsa04151), TNF signaling pathway (hsa04668), MAPK signaling pathway (hsa04010), estrogen signaling pathway (hsa04915), Cell cycle (hsa04110), as shown in Fig. On the Metascape platform, KEGG enrichment analysis was performed , and 39 hway hsa0657, endoThe 18 key targets obtained by topological analysis correspond to 23 active components, and an "ingredient-target" network diagram was established to analyze the relationship between them, as shown in Fig. A “module-target-pathway” network that includes 70 nodes  was constructed. As shown in Fig. After identifying apoptosis regulation (hsa04210) of RP as the essential regulation modules in treating EMs, we selected P53, BAX, and CASP3 as potential therapeutic targets . This waOur topological analysis has successfully identified 22 ingredients out of the total 32 active ingredients in RP, which corresponded to 18 key targets, as described in article 3.5.1. It is noteworthy that 72.73% (16/22) of these active ingredients were associated with apoptosis. We conducted a comprehensive review of literature and identified 9 components that lacked significant pharmacological effects in humans. Subsequently, we shortlisted the remaining 13 components for molecular docking verification, which are aloe-emodin, beta-sitosterol, hederagenin, emodin, eupatin, physcion, rhein, crysophanol, crysophanol, (-)-catechin, toralactone, daucosterol_qt, and amygdalin.In order to ensure the reliability of docking results, each target was docked with two different PDB IDs. The binding energy of the ingredients in RP and the protein target was shown in Table The human endometrial stromal cells (primary cells) were spindle-shaped or star-shaped. The cytoplasm of the cells was intact, and the nuclei was large and round, with hyperplastic fibrous tissue Fig. A. VimentTo verify the effect of RP on hEM15a cells, we measured the viability of hEM15a cells treated with Rhubarb and Peach kernel (ratio of 1:1) at different concentrations . In comparison with the control group, different concentrations of drugs significantly inhibited the proliferation of hEM15a cells in a time-dependent and dose-dependent manner, as shown in Fig. Compared with the control group, the percentage of early and late apoptotic cells increased significantly after treatment with RP (3 mg/ml), as shown in Fig. P < 0.05 or P < 0.01), as shown in Fig. Western blot analysis showed that the protein levels of P53, BAX, and CASP3 in hEM15a cells treated with RP were significantly higher than those in the control group  of signaling pathways and almost all (32/33) target proteins are related to apoptosis and proliferation. During the progression of endometriosis, there are increased proliferation of epithelial and stromal cells as evidenced by in vitro and in vivo experiment . And theDuring EMs, the survival of endometriotic cells could be due, at least in part, to the reduction of apoptotic-mediated pathways . It has The binding ability between the main components and apoptotic proteins was detected by the molecular docking technique. According to the network pharmacology, the triterpenes including beta-sitosterol, campesterol and hegeragenin are critical components of RP for treating EMs. It has been reported that beta-sitosterol, one of the triterpenes compound contained in both Rhubarb and Peach kernel, exert a promising anti-EMs effect , 44. BetWe identified that Anthraquinones, flavonoids and triterpenoids are considered potential active components in RP by network pharmacology, and the results of KEGG enrichment highlight the potential of RP as the target to treat EMs by inducing apoptosis, P53 signalings. Furthermore, it was confirmed that active components had high binding energy with P53, BAX, and CASP3 via molecular docking. The results of in vitro experiments detected the depressed proliferation and increased apoptosis of EMs cells in RP treated group. The levels of P53, BAX, and CASP3 significantly increased in the RP group compared with the control group. Taken together, our results suggest that RP has an anti-EMs effect by activating apoptosis, P53 signalings. Anthraquinones, flavonoids and triterpenoids might be the possible pro-apoptotic components in RP. Therefore, RP could be employed as an alternative medical approach for endometriosis treatment.However, the computational analysis also has some limitations. The targets of the drugs and diseases we study mainly rely on existing databases and statistical analysis, so there are certain limitations. We mainly verify our analysis results through cell experiments. Therefore, other animal experiments and clinical experimental studies are still needed to verify these findings.Additional file 1.


Ganoderma lucidum fermented extract, exert multiple pharmacological activities, including immunomodulatory properties. Our study aimed to reveal the pharmacological effects and potential mechanisms of Ganoderic acid C2 (GAC) against cyclophosphamide (CY)-associated immunosuppression. Target genes were collected from several public databases, including the DisGeNET, Comparative Toxicogenomics Database, GeneCards, and PharmMapper. STRING database was used to construct the protein–protein interaction of network. Subsequently, molecular docking was carried out to visualize the protein-GAC interactions. Experimental validations, including ELISA and qRT-PCR were performed to confirm the pharmacological activities of GAC on CY-induced immunosuppression model. A total of 56 GAC-related targets were identified to be closely associated with CY-induced immunosuppression. Enrichment analyses results revealed that these targets were mainly involved in immune and inflammatory response-related pathways. STAT3 and TNF were identified as the core targets of GAC. Molecular docking indicated that GAC combined well with STAT3 and TNF protein. In addition, animal experiments indicated that GAC improved immunity as well as STAT3 and TNF genes expression in CY-induced immunosuppression, which further verified the prediction through bioinformatics analysis and molecular docking. We successfully revealed the potential therapeutics mechanisms underlying the effect of GAC against CY-induced immunosuppression based on the combination of bioinformatics analysis, molecular docking, and animal experiments. Our findings lay a theoretical foundation for the in-depth development and utilization of Ganoderma lucidum fermentation product in the future, and also provide theoretical guidance for the development of innovative drugs that assist in improving immunity.Triterpenoids, as the main active ingredient of  As an important defense system of the body, the immune system has important physiological functions. It protects the host from the damage of pathogenic microorganisms by identifying and monitoring non-self and self substances, so as to maintain the health of the body3. When tumor cells are killed by drugs or radiation, the body’s immune function and bone marrow hematopoietic system will also be damaged, resulting in immunosuppression4. Cyclophosphamide (CY) is a commonly used antineoplastic drug in clinical practice. In addition, it is also a highly effective immunosuppressant, commonly used in blood and bone marrow transplantation5. It could cause oxidative stress, immunosuppression, bone marrow suppression, and other side effects, which are the primary limiting factors in clinical chemotherapy7. In clinical practice, in order to reduce those side effects, chemotherapy patients usually take immunoactive substances to enhance immunity8. Thus, it is very important to develop novel immunomodulators that could effectively reduce CY-induced immunodeficiency.The immune system is an interactive network of cytokines, humoral factors, cells, and lymphoid organs12. Ganoderma lucidum has long been used primarily for the prevention and treatment of various diseases. More and more studies have shown that it has immunomodulatory functions14. Triterpenoids and polysaccharides are the major bioactive ingredients in Ganoderma lucidum15. Triterpenoids have a wide range of biological activities, including antioxidation, hepatoprotective, anti-inflammatory, anti-apoptotic, and immune restoration effects19. Ganoderic acid C2 (GAC) is a bioactive triterpenoid in Ganoderma lucidum fermented extract. However, the immunomodulatory effects of GAC in CY-induced immunosuppressed mice remain unclear.Many natural immunomodulators, including polypeptides, flavonoids, and polysaccharides, have the ability to boost immunity and improve CY-induced immunodeficiency20. Molecular docking, an in silico approach, is widely recognized as one of the most prevalent and effective structure-based methods for predicting the interactions between molecules and biological targets22. In the present study, a network pharmacology prediction and molecular docking-based approach, combined with animal experiments verification, was carried out to reveal the potential therapeutic mechanisms and targets of GAC against the CY-induced immunodeficiency. The workflow of the research is presented in Fig. Network pharmacology is a tool for systematically describing complex interactions between drugs and biological systems from a network perspectivehttps://ctdbase.org/)23, and the SuperPred (https://prediction.charite.de/index.php)24. The Retrieve/ID mapping tool (www.uniprot.org) was used to transform protein target manes into their official gene symbols. Finally, CY-related targets were obtained by removing duplicate genes.The CY-related targets were collected from several online databases by using “Cyclophosphamide” as keywords, including the Comparative Toxicogenomics Database  and similarity ensemble approach (https://sea.bkslab.org/)25. The Retrieve/ID mapping tool (www.uniprot.org) was used to transform protein target manes into their official gene symbols. Finally, GAC-related targets were collected by removing duplicate genes.The potential targets of GAC were obtained by using following databases by entering a SMILES of GAC: the SuperPred (https://www.genecards.org/)26, the DISGENET database (https://www.disgenet.org/)27, and the Comparative Toxicogenomics Database  to collect potential targets of immunosuppression by using “immunosuppression” or “leukopenia” as the search keywords. Finally, immunosuppression-related targets were collected by removing duplicate genes.We searched the GeneCards database (https://bioinfogp.cnb.csic.es/tools/venny/index.html) was used to identify the intersection of GAC, CY, and immunosuppression targets. Then, those intersection targets were imported into the STRING database (https://cn.string-db.org/) to construct the PPI network; the protein interaction selection score was set to > 0.4. The TSV file was downloaded and used to visualize the PPI network results by using the Cytoscape software (version 3.2.1) (http://cytoscape.org/). The cytoHubba plugin of Cytoscape software was used to identify the core targets of GAC28.First, the Venn diagram tool  databases, and the 3D conformations of proteins with a crystal resolution < 3 Å were selected. For the preparation of receptor macromolecule: firstly, the proteins were removed from solvent and proto-ligand small molecules using PyMOL software and saved as PDB format. Then using AutoDock software, the protein was subjected to de-watering, hydrogenation, charge calculation and atom type addition operations, and finally saved as a PDBQT format file. For the preparation of small molecule ligand: firstly, the small molecule ligand was opened using AutoDock software, in which the charge was adjusted and finally saved as a PDBQT file. The AutoDockTools 1.5.6 software was used to open the receptor macromolecule and ligand PDBQT files. The size of the grid was designed to cover the binding pocket, with XYZ dimensions of 80 Å × 80 Å × 80 Å. The spacing between grid points was set to 0.6 Å. The docking process utilized the Vina force field and the Lamarckian GA (4.2) algorithm. The results of molecular docking were visualized by using the PyMOL software.We performed molecular docking analysis to investigate the potential binding mode of GAC to a macromolecular receptor. The core genes were selected for molecular docking analysis. The 3D structures of targets protein (TNF and STAT3) were downloaded from PDB (The specific-pathogen-free Kunming mice (22–25 g and 6-week-old) were purchased from the Harbin Medical University. Mice were kept with tap water and food ad libitum, and housed in air-conditioned room (relative humidity 45–65% and temperature 20–24 °C) with a 12 h dark/light cycle. The animal experimental protocol was approved by the Ethics Committee of the Harbin Normal University.The mice were divided into two groups, namely the vehicle control group and GAC control group, with 6 animals in each group. GAC was dissolved in normal saline and administered to the animals at a dose of 2000 mg/kg body weight through oral gavage. The mice were monitored for 14 days to detect any signs of toxicity or mortality. The animals' behavior was closely observed for the first 4 h, and their body weight was recorded at the beginning of the experiment, on the 7th day, and at the end of the experiment.29. Control and CY groups were given saline alone with the same operation. GAC was obtained from Shanghai Standard Technology Co Ltd . After the experiments, the mice were fasted without water overnight. Then, the mice were weighted and killed by cervical dislocation. We collected spleen and thymus tissues and weighted immediately. The immune organ index was calculated based on the following formula: organ index (mg/g) = organ weight (mg) / body weight (g).After 7 days of acclimation, all mice were divided into five groups (n = 10 per group): control group (Control), CY-induced immunosuppression group (CY), CY group of mice received with GAC at a dose of 10 mg/kg (LGAC), CY group of mice received with GAC at a dose of 20 mg/kg (MGAC), CY group of mice received with GAC at a dose of 40 mg/kg (HGAC). CY-induced immunodeficiency mice were given intragastrically with GAC  for consecutive 14 days. Control group was given intragastrically with normal saline. The immunodeficiency model was induced by injecting intraperitoneally with CY (50 mg/kg/d) at day 8, 10, 12 and 14 based on the previously studyAfter the last GAC treatment, the whole blood sample was collected into EDTA-2 K tubes. After mixing 100 μL of blood sample with 900 μL of phosphate buffer solution, the resulting mixture was homogenized and centrifuged at 3000 r/min for 10 min at 4 °C. Finally, the supernatant was collected. The white blood cell (WBC), neutrophil (NEUT), and lymphocyte (LYMPH) were counted using a hematology analyzer .After the last GAC treatment, the whole blood sample was collected into EDTA-2 K tubes. After mixing 100 μL of blood sample with 900 μL of phosphate buffer solution, the resulting mixture was homogenized and centrifuged at 3000 r/min for 10 min at 4 °C. Finally, the supernatant was collected. The level of serum inflammatory cytokines  and immunoglobulin (lgA and lgG) were measure by Enzyme Linked Immunosorbent Assay (ELISA). The blood samples were collected from orbital venous plexus after the last GAC treatment. We also measured the serum inflammatory cytokines and immunoglobulin levels by ELISA kit  based on the manufacturer’s instruction.−ΔΔCt method was used to measure the mRNA expression levels. The primers were presented in Table Total RNA was extracted from spleen tissues using TRIzol Reagent  based on the manufacturer’s protocol. In addition, the RNA was measured using spectrophotometry at a wavelength of 260 nm to determine its concentration, while its purity was assessed by calculating the absorbance ratio at 260/280 nm. It is recommended that the absorbance ratios of RNA samples fall within the range of 1.8 to 2.0 for optimal purity. The purified RNA was synthesized into the cDNA using the cDNA synthesis kits (Takara Bio). Then, the qRT-PCR was performed on a StepOnePlus Real-time PCR system . The 2p < 0.05 was indicated statistically significant.The GraphPad Prism 8.0 software was used to perform the data analysis. Experimental data were shown as mean ± standard deviation (SD). One-way analysis of variance was used to compare the group differences. All experiments protocols and animal use were approved by the Laboratory Animal Ethics Committee of Harbin Normal University.In Fig. In our study, we conducted functional enrichment analyses to gain further insights into the biological functions of the potential genes targeted by GAC in the treatment of CY-induced immunodeficiency. In the category of biological processes (BP), the target genes exhibited significant enrichment in various pathways, including regulation of inflammatory response, immune response-activating cell surface receptor signaling pathway, cellular response to peptide, regulation of small molecule metabolic process, and activation of protein kinase activity Fig. A. RegardBased on the results shown in Table In this study, we conducted molecular docking analysis on GAC using two hub proteins, STAT3 and TNF, to further investigate the potential targets of GAC against CY-induced immunosuppression. The key binding sites between GAC and the amino acid residues of the target proteins were illustrated in Fig. There were no observed alterations in behavioral parameters in mice administered with a dose of 2000 mg/kg of GAC orally. Additionally, there were no significant changes in body weight recorded throughout the duration of the study Table . Moreovep < 0.05). However, after treatment with GAC, the mice in the MGAC and HGAC groups exhibited a significant increase in body weight when compared to the CY group (p < 0.05). Furthermore, the spleen and thymus indices of mice in the CY group decreased significantly compared to the control group (p < 0.05), as shown in Fig. p < 0.05).Figure p < 0.05). However, after a 14-day treatment with GAC (20 or 40 mg/kg), the decline in inflammatory cell counts induced by CY was significantly improved (p < 0.05). These results demonstrate the immunomodulatory activity of GAC in CY-treated mice.According to the data depicted in Fig. p < 0.05). However, after a 14-day treatment with GAC (20 or 40 mg/kg), the decline in inflammatory cytokine levels induced by CY was significantly alleviated (p < 0.05).According to the data illustrated in Fig. p < 0.05). However, after a 14-day treatment with GAC (20 or 40 mg/kg), the decline in immunoglobulin levels induced by CY was effectively reversed (p < 0.05).According to the findings presented in Fig. p < 0.01).To confirm the findings of the molecular docking analysis, a qRT-PCR analysis was conducted. As depicted in Fig. 5. Simultaneously, prior research has indicated that prolonged chemotherapy can result in the depletion of T cells and lymphopenia30. As a result, the discovery of new medications that can safeguard against the immunosuppressive impact of CY is of utmost importance. Chinese herbal medicine and natural products offer a complementary and alternative approach to combating CY-induced immunodeficiency due to their ability to target multiple pathways and mechanisms33. Ganoderma lucidum has been utilized for a considerable period to prevent and treat a diverse range of diseases owing to its immunomodulatory properties14. Ganoderic acid C2 (GAC) is a significant bioactive component found in Ganoderma lucidum. However, the therapeutic effects and mechanisms of GAC in treating immunodeficiency remain unexplored. Network pharmacology, as an advanced technology, is primarily employed to predict the intricate interaction between drugs and diseases. It has proven useful in researching novel drug targets, understanding drug mechanisms, and discovering new medications35. In our study, we integrated network pharmacology, molecular docking, and animal experiments to investigate the potential therapeutic targets and mechanisms of GAC in combating cyclophosphamide-induced immunosuppression.The immunosuppressive environment plays a critical role in facilitating the development and advancement of tumors. In clinical practice, certain chemotherapeutic drugs, like cyclophosphamide (CY), not only target cancer cells, but also hinder the immune system of patients, ultimately leading to a lower rate of tumor recovery36. The TNF family members and their receptors are involved in activating, differentiating, proliferating, or promoting the survival of immune cells37. Furthermore, recent research has shown a decrease in TNF levels in mice with CY-induced immunosuppression38. Signal transducer and activator of transcription 3 (STAT3), a transcriptional regulator, plays a vital role in mature tissue function and vertebrate development including control of immunity and inflammation40. STAT3 involved in tumor immunity and inflammation through contributing to pro-oncogenic inflammatory pathways, such as IL-6-GP130-JAK and NF-kappaB pathways41. Moreover, recent studies showed that the JAK2/STAT3 signaling pathway was inactivated in CY-induced hematopoietic dysfunction44. In this study, it was observed that GAC could increase the expression levels of TNF and STAT3 in mice with CY-induced immunodeficiency. This suggests that the TNF and STAT3 genes are likely to be crucial in the development of CY-induced immunodeficiency, and targeting them could be a potential therapeutic approach for GAC in treating immunodeficiency.Initially, we identified 56 target genes that serve as therapeutic targets for GAC in combating cyclophosphamide-induced immunosuppression. Notably, two hub genes (TNF and STAT3) were identified as playing a pivotal role in the PPI network. Moreover, molecular docking results revealed that hydrogen bonding is the primary mode of interaction between target proteins and GAC, providing further validation to the network pharmacology findings. Tumor necrosis factor (TNF), an inflammatory cytokine, plays a crucial role in disease pathogenesis and maintaining homeostasis46. Previous research has shown that natural compounds have immunomodulatory effects by activating immune cells and increasing levels of inflammatory cytokines and immunoglobulins11. In our current study, we discovered that GAC can enhance immune response in mice treated with CY, providing further confirmation of the network pharmacology findings.Neutrophils, lymphocytes, and white blood cells, along with immune organs such as the thymus and spleen, as well as immunologic active materials like interleukins, interferon, and immunoglobulin, are vital components of the immune systemBy utilizing network pharmacology, molecular docking, and experimental validation, our study demonstrates that GAC possesses immunomodulatory properties by activating TNF and STAT3 expression. This research provides a comprehensive understanding of the therapeutic targets and mechanisms of GAC in the treatment of immunodeficiency, integrating network pharmacology with experimental validation.Supplementary Tables.


FaLOX6, FaHPL, FaADH, FaAAT, FaAAT1, FaDXS, FaMCS, and FaHDR in ‘Xiaobai’. However, the content of eugenol in ‘Benihoppe’ was higher than that in ‘Xiaobai’, which may result from the higher expression of FaEGS1a in ‘Benihoppe’ compared with ‘Xiaobai’. The results provide insights into the somaclonal variations that affect the volatile compounds in strawberries and can be used for strawberry quality improvement.Somaclonal variations in tissue cultures can be used in plant breeding programs. However, it is still unclear whether somaclonal variations and their original parent have differences in volatile compounds, and the candidate genes which result in the differences in volatile compounds also need to be identified. In this study, we utilized the ‘Benihoppe’ strawberry and its somaclonal mutant ‘Xiaobai’, which has different fruit aromas compared with ‘Benihoppe’, as research materials. Using HS-SPME-GC-MS, 113 volatile compounds have been identified in the four developmental periods of ‘Benihoppe’ and ‘Xiaobai’. Among them, the quantity and content of some unique esters in ‘Xiaobai’ were much higher than that in ‘Benihoppe’. In addition, we found that the contents and odor activity values of ethyl isovalerate, ethyl hexanoate, ethyl butyrate, ethyl pentanoate, linalool, and nerolidol in the red fruit of ‘Xiaobai’ were much higher compared with ‘Benihoppe’, which may result from the significantly increased expression of  Strawberries belong to the Rosaceae family. Strawberry fruits with pleasant aromas are popular with consumers all over the world . The volHPL, ADH, AAT, TPS, etc. [Previous research has illustrated that esters account for 25~90% of total volatile components in most strawberry cultivars . Methyl PS, etc. . In addiPS, etc. , temperaPS, etc. ,25, matuPS, etc. , pre-harSomaclonal disparity originated from tissue culture germinating variations in regenerated plants . The varWe first compared the phenotypes of ‘Benihoppe’ (WT) and ‘Xiaobai’ (mut) strawberry fruit at four different developmental stages. The fruit skin and flesh color of ‘Benihoppe’ and ‘Xiaobai’ showed no significant differences at the green fruit, white fruit and turning fruit stages. During the ripening fruit stage, ‘Benihoppe’ had red skin and red flesh, whereas ‘Xiaobai’ had red skin and white flesh A. To invWe further analyzed the relative abundance of the volatile compounds in the four developmental stages of ‘Benihoppe’ and ‘Xiaobai’ fruit B. We fouDifferent ratios of volatile substances often determine aroma properties. In the fruit of ‘Benihoppe’ and ‘Xiaobai’, 113 compounds were identified . We founTo further compare the differences in volatile compounds between ‘Benihoppe’ and ‘Xiaobai’, the unique volatile compounds in the fruit of ‘Benihoppe’ and ‘Xiaobai’ at four developmental periods were compared. As shown in The OAVs of 30 volatile compounds in ‘Benihoppe’ and ‘Xiaobai’ fruit across four developmental stages were calculated based on the threshold values which have been reported in strawberries ,14,29,30To detect the changing trend of the relative contents of volatile substances in the fruit of ‘Benihoppe’ and ‘Xiaobai’ in four developmental stages, principal component analysis was conducted based on the content of nine categories of volatile substances. The first principal component (PC1) accounted for 75.26% of the total variance, and the second principal component (PC2) accounted for 15.19%. PC1 and PC2 successfully distinguished the contents of volatile compounds in ‘Benihoppe’ and ‘Xiaobai’ at four developmental stages . There aIn order to understand why the volatile compounds were different between ‘Benihoppe’ and ‘Xiaobai’, we examined the correlation between the transcript abundance of volatile-related genes and the contents of volatile compounds in ‘Benihoppe’ and ‘Xiaobai’ at four different developmental stages.The aroma volatiles of fruit are mainly synthesized through the fatty acid pathway; this pathway can produce alcohols, aldehydes, esters, and lactones. This pathway contains the lipoxygenase (LOX) and β-oxidation pathways. For the LOX pathway, linoleic acid or linolenic acid is first oxidized to hydroperoxide by LOX, which is subsequently cleaved by hydroperoxide lyase (HPL) to form hexanal and hexenal. The C6 aldehyde is reduced to the corresponding C6 alcohol by alcohol dehydrogenase (ADH), then converted to ester by alcohol acyl-transferases (AAT) ,32 FiguA.FaLOX6, FaHPL, FaADH, FaAAT, and FaAAT1 genes in the fatty acid pathway in the fruit of ‘Benihoppe’ and ‘Xiaobai’ at four different developmental stages. During the fruit development of ‘Benihoppe’ and ‘Xiaobai’, the expression level of FaLOX6, FaHPL, and FaADH first increased and then decreased. However, the expression levels of these genes reached the highest values in the turning fruit stage of ‘Benihoppe’ and the white fruit stage of ‘Xiaobai’. In addition, the content of hexanal in ‘Xiaobai’ also first increased and then decreased  and farnesyl diphosphate (FPP), respectively [FaDXS, FaDXR, FaCMS, FaMCS, FaHDS, and FaHDR in the MVA pathway and FaHMGR2, FaMVK, and FaPMK in the MEP pathway first increased and then decreased in ‘Benihoppe’ and ‘Xiaobai’. The expression levels of these genes reached their maximum in the turning stage of ‘Benihoppe’ and in the white fruit stage of ‘Xiaobai’. In addition, the transcript abundance of FaNES1 gradually increased with the ripening of the fruit in ‘Benihoppe’ and ‘Xiaobai’ , 16 days (white fruit stage), 24 days (turning fruit stage), and 32 days (red fruit stage) after pollination . The fruThe extraction of volatile compounds from fruit samples was based on the methods described in . We weigThe detailed method used for the identification of volatile compounds in strawberries using HS-SPME-GC-MS described in  was foll2O to 10 μL. The reaction was conducted at 42 °C for 5 min. Total RNA (1 μg) was reverse-transcribed using a PrimeScriptTM RT kit , and the volume of the cDNA was 20 uL. The gene sequences involved in volatile biosynthetic pathways were based on the published literature [Fragaria × ananassa database  [Fragaria × ananassa with the highest similarity were selected, and RT-qPCR primers were designed using the online software Primer 3.0 based on the conserved sequences. The sequences of primers used in this study are listed in TM 6 Flex system  according to the manufacturer’s instructions. Measurements of the gene expression levels of aroma-related genes were carried out using SYBR® Premix Ex Taq TM II . The reaction system was established as described in [Fa26S) is a housekeeping gene in strawberries and was selected as a reference gene [2−∆∆Ct method was used for calculating the relative gene expression level [Total RNA was extracted from the fruit at four developmental stages of ‘Benihoppe’ and ‘Xiaobai’ using a modified CTAB method as described in . We testterature . Then, wne 2021) ,57. The ribed in . Strawbence gene ,59. Eachon level .p < 0.05), different letters represent significant differences between different treatments. For the nine categories of volatile compounds, a T-test was used to determine whether there was a significant difference between the two treatments . SigmaPlot 12.5  was used to visualize the data. Principal component analysis was conducted using Origin 8.0 software . We analyzed the results of RT-qPCR using Microsoft Excel, and the heatmap of gene expression of fruit at four developmental stages was generated using TBtools1.082 [Statistical analysis was performed using IBM SPSS Statistics 19. Three replicates were used for each measurement. Based on Duncan’s test  and the terpenoid pathway  in the red fruit stage of ‘Xiaobai’ compared with ‘Benihoppe’. The results provide insights into the somaclonal variations that affect the volatile compounds in strawberries and can be used for strawberry quality improvement.In addition, the expression of


To explore the anti-tumor effects of Radix Astragali on osteosarcoma and its mechanism. We analyzed the PPI network of Radix Astragali’s potential targets for treating osteosarcoma and got the hub targets. We used KM curves to screen hub targets that could prolong sarcoma patients’ survival time. We performed GO and KEGG enrichment analysis of Radix Astragali’s potential targets and predicted Radix Astragali's molecular mechanism and function in treating osteosarcoma. The binding process between the hub targets, which could prolong sarcoma patients' survival time, and Radix Astragali was simulated through molecular docking. PPI network analysis of potential therapeutic targets discriminated 25 hub targets. The KM curves of the hub targets showed there were 13 hub targets that were effective in improving the 5-year survival rate of sarcoma patients. GO and KEGG enrichment demonstrated that Radix Astragali regulates multiple signaling pathways of osteosarcoma. Molecular docking results indicated that Radix Astragali could bind freely to the hub target, which could prolong the sarcoma patient's survival time. Radix Astragali act on osteosarcoma by regulating a signaling network formed by hub targets connecting multiple signaling pathways. Radix Astragali has the potential to become a drug for treating osteosarcoma and prolonging the sarcoma patient's survival time. It originates from primitive mesenchymal stem cells and occurs most often in the epiphysis of long stem bones3, such as the distal femur and the proximal tibia4. The current treatment strategy for osteosarcoma is neoadjuvant chemotherapy, radical resection, and adjuvant chemotherapy5. With the addition of chemotherapy, the overall 5-year survival rate of patients with osteosarcoma has reached approximately 60–70%6, but the overall 5-year survival rate decreases to lower than 15%after the occurrence of distant metastasis7. Although new drugs for OS treatment, such as immune checkpoint inhibitors and targeted drugs are constantly being explored9, osteosarcoma’s five-year overall survival rate has not significantly improved in the last few decades10. There is still an urgent need to discover new treatment options.Osteosarcoma is one of the malignant tumors that occur in children and adolescents11, It is the root of Astragalus memeranaceus (Fisch.) Bge. Var. mongholicus (Bge.) Hsiao, or A. membranaceus (Fisch.) Bge12. Existing evidence has proven that Radix Astragali exerts inhibitory effects on tumor cells by inhibiting proliferation, migration, and invasion14. However, research related to the role of Radix Astragali in inhibiting osteosarcoma and its underlying mechanism is insufficient.Radix Astragali is one of the traditional Chinese medicines16. It explores the relationship between diseases and drugs by integrating systems biology, bioinformatics, and computer science17. The pharmacology has been transformed from a single-drug-single-target model to a multi-drug-multi-target model18. Therefore, this experiment systematically investigated the effects and mechanisms of Radix Astragali on osteosarcoma using network pharmacology and molecular docking techniques.Network pharmacology is efficient and cost-effectiveThe flow chart of the study design is shown in Fig. https://old.tcmsp-e.com/tcmsp.php) with the keyword "Radix Astragali”. The drug ingredients were screened with the criterias of oral bioavailability (OB) > 30% and drug Likeness (DL) > 0.18. The obtained drug ingredients were regarded as the drug ingredients of Radix Astragali. The TCMSP database (https://old.tcmsp-e.com/tcmsp.php) is a platform on the systemic pharmacology of herbal medicines where we can get the relationship between drugs, targets, and diseases. The database provides information on the identification of drug ingredients, drug target networks, networks of related drug target diseases, and pharmacokinetic properties such as OB and (DL) for natural compounds.The drug ingredients of Radix Astragali were obtained from the TCMSP database (https://old.tcmsp-e.com/tcmsp.php) based on the drug ingredients of Radix Astragali. Radix Astragali-related targets were screened in the TCMSP database (https://old.tcmsp-e.com/tcmsp.php) based on the drug ingredients of Radix Astragali. Using "Radix Astragali" as the keyword, we searched the ETCM (http://www.tcmip.cn/ETCM/) and Symmap databases (http://www.symmap.org/) for Radix Astragali-related targets. Finally, we integrated and de-duplicated the Radix Astragali-related targets obtained from the above three databases, and the targets obtained were regarded as Radix Astragali-related targets.The ETCM database (http://www.tcmip.cn/ETCM/) is a comprehensive resource database for traditional Chinese medicine that went online in 2018 by the Chinese Academy of Traditional Chinese Medicine, bringing together information on a wide range of herbal medicines, herbal compounding, herbal chemical composition, drug targets, and related diseases. SymMap (Symptom Mapping) (http://www.symmap.org/) is a TCM evidence association database. The database herbs and the corresponding TCM symptoms, and corresponds TCM symptoms to Western medicine symptoms, and includes diseases, herbal ingredients, drug targets associated with these symptoms, as well as the correlations between these six data types.Radix Astragali-related targets were screened in the TCMSP database (https://www.genecards.org/) for osteosarcoma-related targets using the keyword "Osteosarcoma", and the targets obtained were osteosarcoma-related targets. Genecards is a comprehensive, searchable database of genes where we can access information on almost all known human genes.We searched the Genecard database (https://cn.string-db.org/), with species set to "Homo sapiens" and confidence set to "0.9" to construct the PPI network.With R software (R 4.2.0), we used Radix Astragali-associated targets and osteosarcoma-associated targets to draw Venn diagrams. The potential targets of Radix Astragali for osteosarcoma were imported into the String database  to further analyze and obtain the coefficients of the targets, such as degree, betweenness Centrality, and Closeness Centrality, and then screened the targets twice with degree ≥ median degree, betweenness Centrality ≥ median Closeness Centrality, and Closeness Centrality ≥ median Closeness Centrality as the screening criteria. Centrality ≥ Betweenness Centrality and Closeness Centrality ≥ Closeness Centrality were used as the screening criteria to screen the hub targets twice, and the obtained targets were the hub targets of the PPI network.https://kmplot.com/analysis/) to plot KM curves. Since there is no separate database for osteosarcoma in this platform, we plotted KM curves based on the sarcoma database, and P < 0.05 means statistically significant. The division between high and low groups was chosen as the median for gene expression levels. The Kaplan–Meier Plotter database  was constructed based on gene microarray and RNA-seq data from public databases such as GEO, EGA, and TCGA and was used to integrate gene expression information and clinical prognostic values for meta-analysis and the study, discovery, and validation of survival-related molecular markers.We imported the hub targets into the Kaplan–Meier Plotter online platform  to perform GO and KEGG enrichment analysis of potential targets of Radix Astragali for osteosarcoma treatment, and to illustrate GO enrichment in terms of biological processes, cellular components, and molecular functions. R-package-Bioconductor Cluster Profiler is an R package (R × 64 4.0.3) widely used for gene bioinformatics analysis.https://www.rcsb.org/) and 3D structures of the drug ingredients from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/), and the target proteins and drug ingredients were pre-processed using Autodock 4 (4.2.6) and Pymol software (2.5), and Autodock Vina software (1.1.2) was used as batch molecular docking.Autodock software is one of the software used for molecular docking. In this experiment, two sub-software, Autodock 4 (4.2.6) and Autodock Vina (1.1.2), were used for molecular docking, and Pymol software (2.5) was used for molecular pre-processing and visualization of docking results. The hub targets whose differential expression impacts the survival of sarcoma patients and the corresponding drug ingredients were used to perform molecular docking. 3D structures of the targets were obtained from the The Protein Data Bank (PDB) database  using the keyword "Radix Astragali," and the drug ingredients were screened using the screening criteria of OB > 30% and DL > 0.18. We finally obtained 20 drug ingredients of Radix Astragali , 272 Astragalus-related targets were obtained from the ETCM database (http://www.tcmip.cn/ETCM/) and 100 Radix Astragali-related targets were obtained from the Symmap database (http://www.symmap.org/). Each database has a different focus, and there are differences between databases, therefore, there may be potential risks in the joint analysis of multiple databases, We compensate for the absence of some targets in a single database by merging targets from multiple databases. The above targets were integrated and de-weighted to obtain 451 Radix Astragali-related targets as the final Radix Astragali-related targets for further processing.209 Radix Astragali-related targets were obtained from the TCMSP database (https://www.genecards.org/) for osteosarcoma-related targets using the keyword "osteosarcoma" and obtained 5347 targets, which were considered osteosarcoma-related targets for subsequent data processing.We searched the Genecard database (https://cn.string-db.org/) to construct a PPI network and imported into Cytoscape software (3.8.0) for processing and analysis , and the KM curves were plotted. The results showed that the differential expression of 13 hub genes had an impact on the overall survival of sarcoma patients (Table The hub targets were imported into the Kaplan–Meier Plotter online platform (To investigate the potential functions of Radix Astragali for osteosarcoma, we subjected the potential targets of Radix Astragali for osteosarcoma to GO enrichment analysis, and we presented the results of GO enrichment analysis in terms of BP, CC, and MF. p-values are arranged from smallest to largest, and the top 10 BP, CC, and MF are shown in Fig. To simulate the process of mutual binding between the hub target whose differential expression of the target has an impact on the survival rate of sarcoma patients and the corresponding Astragalus active ingredient, we did molecular docking, and the docking with the free energy of release < − 7 kcal/mol indicated that the corresponding active ingredient and the target and bound effectively in the natural state. Based on the docking results, we plotted the heat map Fig. , and we 19, most often occurring in the epiphysis of long stem bones20. The current clinical treatment is so-called "sandwich therapy", which consists of neoadjuvant chemotherapy, radical resection surgery and adjuvant chemotherapy20. The 5-year overall survival rate of primary OS patients who receive classical treatment is approximately 60–70%6, but once distant metastasis occurs, the 5-year survival rate of patients decreases to less than 15%7. Radix Astragali is one of the traditional Chinese medicines and its various components have been shown to affect the biological behavior of tumor cells25, but its effects on osteosarcoma and the mechanisms have not been reported in the literature. By network pharmacology techniques, this study demonstrated that Radix Astragali acts on osteosarcoma through a signaling network formed by hub targets connecting multiple signaling pathways.Osteosarcoma is one of the common malignant bone tumors in adolescents and children29. According to KEGG enrichment results, multiple hub targets are involved in the signaling process of numerous signaling pathways, such as RELA is involved in the IL-17 signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway and PD-L1 expression and PD-1 checkpoint pathway in cancer, while TNF is involved in IL-17 signaling pathway and TNF signaling pathway. For example, activation of the PI3K-Akt signaling pathway inhibits proliferation, migration, and infiltration of tumor cells and promotes apoptosis32, while the TNF signaling pathway inhibits proliferation, migration, and infiltration of tumor cells and promotes apoptosis34. Thus, it can be concluded that hub targets connect multiple signaling networks to form a signaling network, and Radix Astragali regulates the whole signaling network by acting on the hub targets, and regulates the biological behavior of tumor cells, such as apoptosis, proliferation, migration, and infiltration,etc. To explore the docking of Radix Astragali’s drug ingredients and hub targets, we used the molecular docking technique to simulate the binding process of the hub targets with the corresponding drug ingredients. When the free energy of docking released is < − 7 kcal/mol represents that the ligand and receptor can bind freely in the natural state, we visualized the top 20 docking results with the most free energy released. Among the docking results, quercetin effectively bound to the most hub targets and was the most potential ingredient to become a therapeutic drug acted on osteosarcoma, while among the hub targets, PPARG, RXRA, and ESR1 were able to effectively dock with the most drug ingredients and were the most promising therapeutic targets when Radix Astragali acted on osteosarcoma.In this study, By analyzing the PPI network of potential targets of Radix Astragali for osteosarcoma, we obtained 25 hub targets, including CAV1, ESR1, RB1, RXRA, STAT3, etc. By constructing KM curves, we screened 13 targets that have an impact on the overall survival of sarcoma, including CDK1, MAPK1, MAPK14, MAPK8, MYC, PPARG, PRKCA, etc. The results showed that the 5-year survival rate of sarcoma patients was effectively improved when the expression of genes CAV1, ESR1, RB1, RXRA, STAT3, and TNF was upregulated, and the genes CDK1, MAPK1, MAPK14, MAPK8, MYC, PPARG and PRKCA were downregulated. By analyzing the KEGG enrichment results, we know that Radix Astragali acts on osteosarcoma through multiple signaling pathways, such as IL-17 signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway and PD-L1 expression and PD-1 checkpoint pathway in cancer. Meanwhile, according to GO enrichment results, GO results suggest that potential therapeutic targets of Radix Astragali for osteosarcoma have multiple molecular functions, such as RNA polymerase II-specific DNA-binding transcription factor binding, DNA-binding transcription factor binding, nuclear receptor activity, and ligand-activated transcription factor activity, etc. Studies have shown that these molecular functions are closely related to the transcriptional processes of tumor cells, cell proliferation, apoptosis, migration, and infiltrationThrough pharmacological and molecular docking assays, he regulatory effect of Radix Astragali on osteosarcoma. As we all know, network pharmacology is an efficient tool for the investigation of drug mechanisms. However, it is clear that the present study still has some limitations. Firstly, In vitro and in vivo experiments were not conducted in this study, and the effect of Radix Astragali on osteosarcoma in the In vitro and in vivo situations was not explored, but we have predicted which pathways are targeted by Radix Astragali via network pharmacology, molecular biological validation is our next step. Secondly, each database has a different focus, and there are differences between databases; therefore, there may be potential risks in the joint analysis of multiple databases. Therefore, the discovery of new targets and pathways still needs to be done through basic laboratory experiments. Lastly, Because the molecular docking technique in this study can only simulate the process of free binding between the small molecule and the target when the free energy released from the binding of the two is < − 7 kcal/mol, it indicates that the two can be bound in the natural state, but it cannot predict the effect of the binding of small molecules and the target on the target, so it cannot predict the effect of the small molecule on the target directionality, and the effect of the directionality of the expression of the target still needs to be further explored by basic experiments, we will pursue these experiments in future work.We found that Radix Astragali acts on several hub targets that can prolong the survival time of osteosarcoma patients, thus regulating the signaling network formed by several signaling pathways, than achieved the effect of regulating the biological behavior of osteosarcoma cells, such as proliferation, apoptosis, migration, and infiltration, etc. Among the drug ingredients of Radix Astragali, quercetin has the most potential to become an anti-osteosarcoma drug, while the hub targets PPARG, RXRA, and ESR1 are the most potential therapeutic targets when Radix Astragali acting on osteosarcoma.