To explore the mechanism of action of “Fructus Ligustri Lucidi-Cuscutae Semen” in the treatment of prostate cancer using network pharmacology and molecular docking.  The active ingredients and targets of “Fructus Ligustri Lucidi-Cuscutae Semen” were obtained by searching the TCMSP and DrugBank databases. These were matched and corrected using the UniProt platform. A drug “active ingredient-target” network map was constructed using Cytoscape 3.8.0. Prostate cancer-related targets were acquired from GeneCards, Disgenet, DrugBank, and other databases. The protein-protein interaction (PPI) network between the drug and prostate cancer was constructed with BioGenet; the crossover network of the two targets was extracted derive the key targets of “Fructus Ligustri Lucidi-Cuscutae Semen” for prostate cancer treatment. We used the Metascape platform for GO and KEGG enrichment analysis of the key targets. AutoDockTools1.5.6 and PyMOL software were used to perform molecular docking. β-sitosterol, EGFR, and ESR1 in “Fructus Ligustri Lucidi-Cuscutae Semen” showed good docking activity.  We obtained 13 active ingredients, 221 drug targets, 1511 prostate cancer targets (including 221 key targets), and 305 KEGG pathways from “Fructus Ligustri Lucidi-Cuscutae Semen.” Paclitaxel, quercetin, kaempferol, TP53,  “Fructus Ligustri Lucidi-Cuscutae Semen” is a valuable clinical guide for the treatment of prostate cancer with multicomponent, multitarget, and multipathway characteristics. Prostate cancer (PCa) is a common disease of the male urinary system that causes malignant tumours in men. It has high incidence and mortality . PCa treFructus Ligustri Lucidi and Cuscutae Semen are traditional Chinese herbs that are commonly recommended for the treatment of patients with advanced tumours. These plants have antitumour and immune function enhancing effects; however, the underlying mechanisms of action involved in the treatment of prostate cancer have not been fully elucidated .The application of network pharmacology to construct a “drug-compound-target-disease” network for multidirectional pathway research makes the material basis and mechanism of action of herbal medicines and compounds more systematic. This can compensate for the limitations of single pathway research into herbal medicines and develop an understanding of the holistic and multidirectional nature of herbal medicines in treating diseases , 5. “Druhttps://old.tcmsp-e.com/tcmsp.php) was searched for all the active ingredients of Fructus Ligustri Lucidi and Cuscutae Semen. These were integrated, and any duplications were removed. Any components that passed the ADME screening criteria of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 [https://www.uniprot.org) and standardised to the officially recognised gene names.The TCMSP database  ≥ 0.18  were impCytoscape 3.8.0 software was used to build and analyse the “active ingredient-target” network diagrams of “Fructus Ligustri Lucidi-Cuscutae Semen.” The “node” and “edge” represented the ingredient or target and the relationship between them, respectively. The network parameters, such as degree, betweenness, and closeness, were analysed using the Network Analyzer plug-in in Cytoscape 3.8.0. By analysing these network parameters, the key active ingredients, targets, and their relationships in “Fructus Ligustri Lucidi-Cuscutae Semen” were investigated.https://www.genecards.org/), OMIM (https://omim.org/), and Disgenet (https://www.disgenet.org/) for potential targets for the treatment of PCa using “prostate cancer” as a keyword. In the GeneCards database, targets were screened with a relevance score > 10. The results of the three databases were pooled, and duplicates were removed to obtain the final disease targets for PCa.We explored GeneCards , closeness centrality (CC), and betweenness centrality (BC). The core targets were selected based on the properties of all nodes.Protein-protein interaction (PPI) networks were completed using BisoGenet. The active ingredient targets of Fructus Ligustri Lucidi and Cuscutae Semen, and PCa targets, were successively entered into BisoGenet to generate a PPI network. The intersection of the two PPI networks was extracted using the Merge plug-in within Cytoscape 3.8.0. This extracted the intersection network of the two PPI networks and analysed the properties of each node in the intersection network using CytoNCA . Next, thttps://metascape.org/) for the GO and KEGG analysis. Data were visualised using Image GP (http://www.ehbio.com/ImageGP/).The enrichment analyses commonly used in network pharmacology are GO functional enrichment analysis and KEGG pathway enrichment analysis. The main function of GO analysis is to predict the relationship of potential targets in terms of biological process (BP), molecular function (MF), and cell composition (CC). KEGG analysis maps these molecular objects or targets to molecular interaction, reaction, or relation networks. Potential targets were entered into the Metascape database (https://www.rcsb.org). The 3D structures of the active ingredients were obtained through the PubChem platform. PyMOL was applied to remove ligands and water molecules. AutoDockTools 1.5.6 was used to hydrogenate the target proteins, calculate the charge number, and determine the AD4 type of the atoms. The built-in plug-ins, autogrid4 and autodock4, were run to determine the binding energy of the best docking site between the active ingredient and target protein. Finally, PyMOL was used to draw a molecular docking map and derive the docking hydrogen bond distance and docking target name for optimization and output.To verify the reliability of the key targets of “Fructus Ligustri Lucidi-Cuscutae Semen” in the treatment of PCa, we performed molecular docking between the potential active ingredient and key targets. The 3D structures of the key target proteins  were obtained from the PDB database  with 414 relationships were obtained .As of January 2022, the GeneCards, OMIM, and Disgenet disease databases were searched using the keyword “prostate cancer.” In the GeneCards, we exported data in Microsoft Excel format and set the relevance score to >10 to filter for potential targets for PCa; 1453 potential targets were obtained. The OMIM-entry-retrieval file was downloaded directly from the OMIM database; 156 potential PCa targets were obtained after screening and deweighting. In the Disgenet disease database, 209 potential PCa targets were obtained after screening and deduplication. After aggregating the above targets and removing any duplicates, the final number of potential PCa targets was 1511. The 221 drug targets and 1511 disease targets were imported into Venny 2.1 to obtain 132 common targets. The intersection of the two Venn diagrams is shown in The PPI networks of “Fructus Ligustri Lucidi-Cuscutae Semen” and PCa were constructed by using the BisoGenet plug-in in Cytoscape 3.8.0. There were 7703 targets directly or indirectly interacting with PCa and 173861 relationships between these targets. Moreover, there are 12435 targets directly or indirectly related to PCa and 239731 relationships between these targets. The intersection network between the two is shown in We performed PPI network analysis  to identTo more accurately analyse the mechanism of action of “Fructus Ligustri Lucidi-Cuscutae Semen” in the treatment of PCa, it was necessary to identify the intrinsic modules after obtaining the core PPI network. The molecular complex detection algorithm was used to analyse the interactions and obtain the module . The funP) values, were plotted in bubble diagrams using Image GP (http://www.ehbio.com/ImageGP/)  . The GO-β-sitosterol with EGFR and ESR1 also showed good docking . The local structures of molecular docking are shown in Molecular docking is a technique that simulates the interaction between ligand small molecules and receptor protein macromolecules. It calculates the binding energy between two counterparts to predict their affinity. A binding energy < 0 indicates that the two molecules bind spontaneously. Moreover, a smaller binding energy results in a more stable conformation. In this study, the target genes, TP53, NTPK1, ESR1, MCM2, and EGFR, with high degree values were selected as receptors. The potentially active components were used as ligands for molecular docking. Most of the potential active ingredients could complete good docking with the target proteins according to the binding energy of receptors and ligands . The besThe symptoms of PCa are not obvious in the early stage; difficulty in urination, frequent urination, and erectile dysfunction appear in the middle and late stages of PCa. From a macroscopic perspective, the pathogenesis of PCa is related to factors such as advanced age, genetics, and prostatitis. From a microscopic perspective, genetic gene mutations such as BRCA1/2 and ESR1, activation of signalling pathways such as PI3K-AKT, and expression of the androgen receptor (AR) are potential pathogenic mechanisms leading to the proliferation and migration of PCa cells , 10, 11.β-sitosterol. Both taxifolin and quercetin are flavonoids. Taxifolin inhibits the production of androgens in Leydig cells, inhibits PCa cell growth, and induces PCa cell apoptosis [In vivo studies have found that sesamin inhibits human PCa cell invasion and adhesion, prevents tumour angiogenesis in PCa cells, exerts powerful antioxidant effects, and attenuates oxidative stress [β-Sitosterol is the most abundant phytosterol in the human. It significantly reduces PCa cell growth, induces apoptosis [β-HSD4 activity [Using database predictions, we determined that Fructus Ligustri Lucidi and Cuscutae Semen affect PCa through multiple active ingredients. The main active ingredients included taxifolin, quercetin, sesamin, kaempferol, and poptosis . Quercetpoptosis . Sesamine stress . Therefoe stress . Additioe stress . β-Sitospoptosis , and modactivity .via cell cycle progression control, senescence, DNA repair, and cell death. NTRK1 is a receptor tyrosine kinase that is distributed in prostate tissue. It binds to nerve growth factors on the cell membrane and can activate the Ras/MAPK, PI3K, and PLCγ signalling pathways to promote cell survival, proliferation, and invasion [We analysed the PPI network of potential therapeutic PCa targets and screened the more important targets, TP53, NTRK1, ESR1, MCM2, and EGFR, from 221 core targets. Deletion of TP53 contributes to the development of AR nondependent or neuroendocrine tumour phenotypes into PCa –25. Moreinvasion . Strongeinvasion . SR1 is invasion . Experiminvasion . MCM2 isinvasion  and in ninvasion . EGFR plinvasion .In vitro studies have shown that it is involved in the proliferation, apoptosis, migration, and invasion of PCa cells [In this study, KEGG enrichment analysis was used to explore the main pathways by which Fructus Ligustri Lucidi and Cuscutae Semen treat PCa. We found that they affect the AGE-RAGE, PI3K-Akt, IL-17, and TNF signalling pathways, which have roles in PCa cell apoptosis, migration, and oxidative stress. The AGE-RAGE signalling pathway activates the inflammatory environment and promotes tumour formation and progression by promoting cancer-related processes, such as ECM remodelling, angiogenesis, and metastasis . AdditioCa cells . The mecCa cells . IL-17 iCa cells . EnhanceCa cells . TNF sigCa cells . Taken tCa cells . MoreoveCa cells .β-Sitosterol and EGFR targets and ESR1 targets showed effective binding activities; however, their biological functions for treating PCa need to be further validated.The molecular docking results revealed that taxifolin, quercetin, sesamin, kaempferol, and TP53 were targets in Fructus Ligustri Lucidi and Cuscutae Semen. Androgens promote the development and progression of PCa; targeted AR therapy is a current hot topic in PCa. The active components in Fructus Ligustri Lucidi and Cuscutae Semen found in this study have known antiandrogenic effects. Taxifolin inhibits androgen production in Leydig cells, inhibits PCa cell growth, and induces PCa cell apoptosis . QuercetTarget expression in Fructus Ligustri Lucidi and Cuscutae Semen influences AR activity. For example, deletion of the TP53 target expression signature can lead to a diminished response to AR antagonists . mCM2 anβ-sitosterol in Fructus Ligustri Lucidi and Cuscutae Semen. Key genes, such as TP53, NTRK1, ESR1, MCM2, and EGFR, were targeted to identify these active components. The results of the KEGG enrichment analysis indicated that Fructus Ligustri Lucidi and Cuscutae Semen may alleviate PCa by regulating AGE-RAGE, PI3K-Akt, IL-17, and TNF signalling pathways. Taken together, these data suggest that Fructus Ligustri Lucidi and Cuscutae Semen may have a multicomponent, multitarget, and multipathway effect on PCa. This study investigates the possible pharmacodynamic basis and mechanism of action of Fructus Ligustri Lucidi and Cuscutae Semen network pharmacology to predict the therapeutic effect of PCa. The molecular docking results also showed that the active molecules of Fructus Ligustri Lucidi and Cuscutae Semen have good binding activity to key target proteins. This study provides a scientific basis for further experimental studies and clinical applications.In this study, a network pharmacology and molecular docking approach was used to identify taxifolin, quercetin, sesamin, kaempferol, and


Type 2 diabetes (T2D) is a chronic metabolic disease defined by insulin insensitivity corresponding to impaired insulin sensitivity, decreased insulin production, and eventually failure of beta cells in the pancreas. There is a 30–40 percent higher risk of developing T2D in active smokers. Moreover, T2D patients with active smoking may gradually develop many complications. However, there is still no significant research conducted to solve the issue. Hence, we have proposed a highthroughput network-based quantitative pipeline employing statistical methods. Transcriptomic and GWAS data were analysed and obtained from type 2 diabetes patients and active smokers. Differentially Expressed Genes (DEGs) resulted by comparing T2D patients’ and smokers’ tissue samples to those of healthy controls of gene expression transcriptomic datasets. We have found 55 dysregulated genes shared in people with type 2 diabetes and those who smoked, 27 of which were upregulated and 28 of which were downregulated. These identified DEGs were functionally annotated to reveal the involvement of cell-associated molecular pathways and GO terms. Moreover, protein–protein interaction analysis was conducted to discover hub proteins in the pathways. We have also identified transcriptional and post-transcriptional regulators associated with T2D and smoking. Moreover, we have analysed GWAS data and found 57 common biomarker genes between T2D and smokers. Then, Transcriptomic and GWAS analyses are compared for more robust outcomes and identified 1 significant common gene, 19 shared significant pathways and 12 shared significant GOs. Finally, we have discovered protein–drug interactions for our identified biomarkers. Type 2 diabetes(T2D) is a chronic metabolic disease defined by insulin insensitivity corresponding to impaired insulin sensitivity, decreased insulin production, and eventually, failed beta cells in the pancreas ,2. So, TIt is clinically statistically shown that professional smokers are more likely to develop T2D with a 30–40% higher chance than nonsmokers . AnotherThe development of T2D can be amplified by regular smoking, as well as many complications which can be developed in T2D patients with a smoking history. Hence, we have tried to retrieve common molecular mechanisms that exist between T2D and smokers. Molecular mechanisms and relationships underlying the T2D-smoker are yet unclear in medical endocrinology , but theWe used data from the Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI) . DatasetAccession numbers of the datasets, which is human gene expression, are GSE106177  and GSE4p-value ≤ 0.05, the significant DEGs were filtered. By utilising the match function, we were able to find shared DEGs between T2D and smoking and create a Venn diagram (Shown in The GREIN Gene Expression Omnibus(GEO) provided us with gene expression RNA-sequence datasets. The DESeq2, an R package, was used to identify differentially expressed genes (DEGs) of T2D and Smoking related RNA-Seq count data . In DESeShown in .Many bioinformatic techniques often focus on measuring the relevance of gene set similarity with previously annotated gene sets in order to find functional pathways connected to a particular disease . We usedp-value that is less than 1.0 × 10−8. We also identified the GOs and pathways associated with the identified common significant genes from GWAS analysis. Finally, we have compared the transcriptomic analysis result with the GWAS analysis to better understand and relate the common biological outcomes by both studies.Researchers use genome-wide association studies (GWAS) to examine a group of genetic mutants which are linked with a certain illness or condition. During this procedure, the genomic sequences of a large number of patients were collected and evaluated so that we could determine the DNA polymorphisms that are present in the genomes of the patients. We have collected GWAS data from both T2D patients and smokers. We have collected the GWAS data from two well-known databases such as PheGenI  and GWASWe have collected RNA-Seq data from Grein or NCBI to examine the impact of gene expression on T2D patients and Smokers. As shown in TMEM178B, TAPBP, ZNF469, SPP1, ARHGAP27, XIST, HCN2, TDRD12, METRN, MEGF6, KIT, IDUA, DUSP8, ABR, C11orf96, ZBTB22, GLIS2, EPPK1, CPXM2, PRRC2A, NTRK1, HLA-C, LOC101928994, VGF, ICAM5, FSCN2 and KCNJ12. Moreover, the most significant shared down regulated genes between T2D and Smoking: LRRCC1, EPHA7, ARHGAP20, SELL, FLOT1, PCDH18, CFI, ARHGAP11A, CENPH, KRBOX1, MYB, PRIM1, MICA, HMSD, CDCA7, KIFC1, HLA-DPA1, ZNF385D, GPR158, SMN1, HLA-DPB1, MGP, CERS6-AS1, PSMB8, PCYT1B, E2F2, TCF19 and RNVU1-4.Then, we have compared the upregulated genes of T2D with the upregulated genes of smokers. Similarly, we have compared the downregulated genes of both conditions. Between T2D and Smoking, the number of upregulated common genes are 27 and the number of downregulated common genes are 28 Shown in . The mosp-values below 0.05 are considered. Therefore, we got 169 most significant signalling pathways. Finally, we sorted the pathways in ascending order depending on p-value. The top 20 pathways were represented in In pathway analysis, we have used five different databases named BioCarta, Elsevier Pathway, KEEG, Reactome and WikiPathway. Then, we have looked for highly expressed pathways by utilizing shared DEGs between T2D and smoking. These pathways were then divided into functional groups for analysis. Initially, we got 573 signalling pathways linked with both disease and condition. Manual curation was then utilised to abate the number of pathways. Pathways with p-value less than 0.05 were included as the most significant GO terms. A total of 137 GO terminologies are found as the mostly enriched GO terms between the conditions. The GO approach uses the biological process (BP), cellular component (CC), and molecular function (MF) to categorise them into functional categories, but we only consider the BP database of GO terminologies. At first, 489 GO terms found common between T2d patients and smokers. After that, the terms with a STRING , a databARHGAP27, E2F2, KIT, EPHA7, TAPBP, KIFC1, ARHGAP11A, SPI1, POU5F1, NANOG, SOX2, METRN, TCF19, SMN1, TDRD12, MGP, E2F1, GATA2, NFKB1, NFIC, SRF, YY1, TFAP2A, FOXL1, TP53, POU2F2, FOXC1, PRRC2A, VGF, CDCA7, SPP1, FLOT1, ABR and MYB. For TFs, protein–drug interactions, and gene–microRNA interactions, we have used Net-workAnalyst tools . Transcrp-value which is less than 1.0 × 10−8. After that, we found 595 significant genes for T2D and 535 significant genes for smokers. Then, we compared both significant genes to obtain the candidate genes associated with both conditions. There are 57 candidate genes  to distinguish the most significant pathways and GOs. Thus, the number of pathways reduced to 196; similarly, GOs reduced to 300. A comparison performed between transcriptomic and GWAS analysis where we have found only one biomarker gene (HLA-C) common between two studies. We have also compared significant pathways from both transcriptomic and GWAS analysis and found 19 significant pathways are shared by both studies: Genes with Mutations Associated with Psoriasis, Proteins Involved in Ulcerative Colitis, Proteins Involved in Psoriatic Arthritis, Proteins Involved in Neuroblastoma, Proteins Involved in Spontaneous Abortion, Proteins with Altered Expression in Osteoarthritis, NTRK FOXO/MYCN Signalling, NTRK1/2/3 Acetylcholine Production, Receptors and Adaptor Proteins Activated in Cancer, Proteins Involved in Glioma, Kaposi sarcoma-associated herpesvirus infection, MAPK signalling pathway, PI3K-Akt signalling pathway, Endosomal/Vacuolar pathway, VEGFR2 mediated cell proliferation, PI3K-Akt signalling pathway WP4172, Allograft Rejection WP2328, Pathways Regulating Hippo Signalling WP4540 and NOTCH1 regulation of endothelial cell calcification WP3413. Similarly, 12 significant GOs are shared by both studies: regulation of neuron apoptotic process (GO:0043523), positive regulation of phosphorylation (GO:0042327), antigen processing and presentation of endogenous peptide antigen via MHC class I via ER pathway (GO:0002484), nerve growth factor signalling pathway (GO:0038180), negative regulation of sprouting angiogenesis (GO:1903671), positive regulation of bone resorption (GO:0045780), positive regulation of DNA-binding transcription factor activity (GO:0051091), ovarian follicle development (GO:0001541), neurotrophin TRK receptor signalling pathway (GO:0048011), positive regulation of histone H3-K4 methylation (GO:0051571), positive regulation of cell projection organization (GO:0031346) and sympathetic nervous system development (GO:0048485).GWAS analysis was performed to find the most significant genes for both T2D and smokers. We employed a threshold Shown in  that areWe investigated the previously published literature to verify the presence of biomarker genes that are found both in smokers and T2D Shown in . The expThere is no doubt that T2D is a major threat to humanity. People who smoke cigarettes have a 30–40% increased chance of acquiring T2D in comparison with nonsmokers . MoreoveTo investigate if there was any significant dysregulation, we have performed Differential Expression Analysis (DEA) followed by identification of common genes including up or downregulated genes shown in , pathwayFirst of all, we have focused on four pathways, namely PI3K/AKT signalling pathway, MAPK signalling pathway, Endosomal/Vacuolar pathways and Allograft Rejection pathway, which are found by the shared profile of T2D and smoker. These pathways were resulted from both transcriptomic and GWAS analysis.T2D development is significantly influenced by the MAPK signalling pathway , where cFor significant common genes, we have focused on certain genes named SPP1, PSMB8, PRRC2A, and PRIM1 that are found from transcriptomic analysis. These genes are highly connected with each other, which also resulted in hub–protein analysis  are validated using previous studies. They are also repeated in multiple analyses such as GWAS analysis, protein–protein interaction, protein–drug interactions, and transcriptional and post-transcriptional Regulators. Signalling pathways such as PI3K/AKT signalling pathway, AMPK signalling pathway, Endosomal/Vacuolar pathways and Allograft Rejection are also verified using published literature. However, still, there are many novel findings that make way for further analysis by the researchers. Finally, our study suggests that people should quit smoking as soon as possible in order to reduce the risk of being diagnosed with type 2 diabetes. Moreover, type 2 diabetes patients must quit smoking to avoid other complications/diseases and take medication accordingly.


Background: Hepatocellular carcinoma (HCC) is a malignant tumor harmful to human health. Ganji Fang (GJF) has good clinical efficacy in the treatment of HCC, but its mechanism is still unclear.Objective: The aim of this study was to investigate the mechanism of action of GJF in the treatment of HCC through network pharmacology, molecular docking and in vitro experiments.Methods: A series of network pharmacology methods were used to identify the potential targets and key pathways of GJF in the treatment of HCC. Then, molecular docking technology was used to explore the binding ability of key active ingredients and targets in GJF. Multiple external databases were used to validate the key targets. In in vitro experiments, we performed MTT assays, wound-healing assays, cell cycle assays, apoptosis assays and RT‒qPCR to verify the inhibitory effect of GJF on the Human hepatoma G2 (HepG2) cells.Result: A total of 162 bioactive components and 826 protein targets of GJF were screened, and 611 potential targets of HCC were identified. Finally, 63 possible targets of GJF acting on HCC were obtained. KEGG enrichment analyses showed that the top five pathways were the cell cycle, cellular senescence, p53 signaling pathway, PI3K/Akt signaling pathway, and progesterone-mediated oocyte maturation. Among them, we verified the PI3K/Akt signaling pathway. CCNE1, PKN1, CCND2, CDK4, EPHA2, FGFR3, CDK6, CDK2 and HSP90AAI were enriched in the PI3K/Akt pathway. The molecular docking results showed that the docking scores of eight active components of GJF with the two targets were all less than -5.0, indicating that they had certain binding activity. In vitro cell experiments showed that GJF could inhibit the proliferation and migration of HepG2 cells, block the cell cycle and induce apoptosis of HepG2 cells, which may be related to the PI3K/Akt signaling pathway. In summary, EPHA2 may be an important target of GJF in HCC, and pachymic acid may be an important critical active compound of GJF that exerts anticancer activity.Conclusion: In general, we demonstrated, for the first time, that the molecular mechanism of GJF in HCC may involve induction of G0/G1 phase cycle arrest through inhibition of the PI3K/Akt signaling pathway and promote apoptosis of hepatoma cell lines. This study provides a scientific basis for the subsequent clinical application of GJF and the in-depth study of its mechanism.  Primary liver cancer (PLC) includes hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma as well as other rare types . HCC, thAlthough the effect of traditional Chinese medicine (TCM) on tumor shrinkage is not as obvious as that of modern Western medicine, reducing toxicity and increasing the efficacy of TCM can help to reduce the side effects, improve the curative effect and improve the quality of life of patients Z. . EvidencNetwork pharmacology uses drug, compound, gene, and disease database information to construct drug-target, target-disease, and drug-disease interaction networks to reveal the complex mechanisms of TCM formulations that have multiple targets and multicomponent characteristics S. , which phttp://lsp.nwu.edu.cn/tcmsp.php, accessed on 12 May 2020). In addition, the potential active compounds of each Chinese medicine were also supplemented through a literature search. Oral bioavailability (OB) and drug-likeness (DL) are some of the most important pharmacokinetic parameters in the process of absorption, distribution, metabolism, and excretion (ADME). We screened compounds on the condition that oral utilization of compounds OB ≥ 30% and DL ≥ 0.18.We obtained the compounds of each drug of GJF from the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform . The potentially effective action targets of GJF were obtained using the SwissTargetPrediction database   . We choshttp://liverome.kobic.re.kr/index.php, accessed on 23 May 2020), OncoDB. HCC databases , GeneCards databases , and OMIM databases  (We consolidated HCC-related targets through the Databases Liverome (ay 2020) .http://cancergenome.nih.gov/, accessed on 23 May 2020), including 370 primary HCC tissues and 50 normal tissues and the corresponding clinical follow-up data. To improve the accuracy of the data, we preprocessed the dataset, including deleting the sites where 70% methylation levels were unavailable and samples expressing more than 30% missing values. Genes with zero RPKM expression in the sample were also excluded  dataset  were FDR <0.05 and β values >0.2, HCC in cancerous tissue and normal liver tissue differentially expressed genes  in line with∣1og 2 (fold change)∣> 1 and FDR <0.05 standard. We intersected HCC-related targets in the public database with DMGS and DEGs in the TCGA database, regarded this intersection as possible key targets in HCC, and mapped these targets to related targets predicted by compounds in GJF as potential targets of GJF for HCC.The data were standardized using the RMA algorithm in the “Limma” package in R software . A falseThe key targets of GJF for HCC were imported into the STRING database to construct a PPI network, and the confidence level was set as ≥0.70. Cytoscape (Version: 3.9.1) software was used for visualization. The key genes and their relationship network of TCM, drug active ingredient, and drug and disease crossing were imported into Cytoscape 3.9.1 software to construct a herb-compound-target network of GJF.https://david.ncifcrf.gov/, accessed on 26 May 2021). GO enrichment analysis included biological processes (BP), molecular functions (MF), and cellular components (CC).Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the “cluster profile” package in R software for better biological interpretation . GO and https://www.rcsb. org/, accessed on 10 June 2021) to retrieve and download 3D structure files for key target proteins, while using the PubChem database  to download 3D structure files for active compounds. For docking analysis, all protein and molecular files were converted into PDBQT format with all water molecules excluded, and polar hydrogen atoms were added. The grid box was centered to cover the domain of each protein and to accommodate free molecular movement. The grid box was set to 30 Å × 30 Å × 30 Å, and the grid point distance was 0.05 nm. Molecular docking studies were performed by Autodock Vina 1.2.2 .The RCSB PDB database  genomics database  database was used to examine the prognostic differences between high and low gene levels. The  containing 10% FBS . Cells were cultured at 37 °C in a 5% COGJF was purchased from the Outpatient Pharmacy of the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine. The dose of 1 fu of GJF was 241 g . Deionized water was added at 10x (approximately 2400 ml), sampled was cooked twice and centrifuged at 1000 r for 5 min. The supernatant was collected and refrigerated at -80 °C for 6–8 h until it was completely frozen. Then, the samples were quickly put into a lyophilized powder machine. After the preparation of lyophilized powder, complete culture medium was used to prepare 50 mg/ml liver-product solution.4/ml, and the cells were inoculated in three 96-well plates. Then, 100 µL of GJF solution with different concentration gradients was added to the cell culture box. One plate was removed at 24 h, 48 h, and 72 h, and a mixture of MTT solution and basic medium (ratio: 1:4) was added under closed light conditions, 100 µL for each well. After further incubation for 4 h, 150 µL DMSO solution was added to each well. The wavelength of the enzyme plate analyzer was set at 490 nm, and the OD values of 3 96-well plates were measured at different times.The logarithmic phase cells were collected, the cell density was adjusted to 5 ˣ 105 cells/well). After the cells reached a subconfluency state (approximately 80%), a 2-mm scratch was made along the midline of the plate. Cells were assigned to the blank serum group (blank serum) , GJF low-dose group (GJF-L), and GJF high-dose group (GJF-H), with two replicate wells set up for each group. When cells were cultured for 0, 12, and 24 h in drug-containing or drug-free media, they were photographed under an inverted microscope , and the rate of cell migration was calculated.HepG2 cells in logarithmic growth were cultured in a 6-well plate  for 10–20 min, then placed in an ice bath. The cells were tested on a flow cytometer as soon as possible within an hour.Total RNA was extracted from experimental cells using a universal RNA extraction kit. Then, EVO-M-MLVRT Master Mix was used for reverse transcription into cDNA. The reaction system was prepared according to the instructions of the SYBR premixed RT‒qPCR kit for RT‒qPCR amplification. The RT‒qPCR conditions were as follows: pre-denaturation at 95 °C for 30 s, denaturation at 95°C for 30 s, and annealing at 60 °C for 30 s, for a total of 40 cycles. The experiment was repeated 3 times. Gene expression was evaluated by RT-qPCR, using the 2 -∆∆CT method.p < 0.05 indicates that the difference is statistically significant. The above experiments were repeated three times, with the results of the cell cycle and apoptosis experiments presented in FlowJo (version 10.7.1) and the rest in GraphPad Prism (version 8.0).Measurement data are expressed as the mean taxi standard deviation (X ± S) and were statistically processed using SPSS (version: 26.0) software. When the experimental data met the normal distribution and the variance was consistent, one-way analysis of variance and two-way analysis of variance were used, and a non-parametric test was used if the data did not meet the normal distribution. A total of 162 bioactive components and 826 protein targets of GJF were obtained from both TCMSP databases and a literature search . Radix BWe screened 5,714 genes in four public databases. The mRNA and methylation sites differentially expressed in the TCGA were further screened. According to the screening criteria for the differentially expressed genes, we obtained 7645 differentially expressed genes, and after the conversion of the differentially methylated sites, we identified 8938 differentially methylated genes, which we display in the form of heatmaps and volcano plots. .The 611 potential targets associated with HCC were mapped to the targets predicted by GJF , and 63 We performed PPI network analysis of 63 intersection genes between HCC . GJF andWe conducted a molecular docking analysis between HSP90AA1, anhydroicaritin, cubebin, (2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one, paeoniflorin (PF), albiflorin_qt, and bisdemethoxycurcumin to screen the compounds with the strongest binding ability to HSP90AA1. In addition, PF, albiflorin_qt, pachymic acid (PA), and -dehydropachymic acid were used to screen the compounds that acted most closely with EPHA2. The results showed that the docking scores of the eight active components of GJF with the two targets were all less than -4.0, indicating that they had certain binding activity. As shown in p = 0.35). In multiple TCGA datasets, the mutation rate of EPHA2 was 1.9% and that of HSP90AA1 was 1.4% , medium concentration group (1.0 mg/ml), and high concentration group (3.0 mg/ml).The MTT experiment results showed that a certain concentration of GJF freeze-dried powder solution inhibited the proliferation of HepG2 cell strains. With increasing drug concentration and time, the inhibition gradually increased in a time- and concentration-dependent manner . The IC5The wound healing assay showed that compared with the control group, the cell mobility of the high concentration group and the low concentration group showed a downward trend in the HepG2 cell line. After 24 h of treatment, the inhibitory effect of the high concentration group was significantly stronger than that of the control group .The cell cycle can be divided into the GOG1 phase, G phase, and G2/M phase, and the ratio of the GOG1 phase is usually used to represent its inhibitory effect on proliferation. As shown in the figure below, GJF blocked HepG2 cell lines in the GOG1 phase in a concentration-dependent manner . CompareAs shown in We verified the relevant molecules in the PI3K/Akt signaling pathway in the HepG2 cell line . AccordiHCC, the most prevalent type of PLC, is a common malignant tumor threatening human health and life . ClinicaTo further explore the mechanism of GJF intervention in the HCC process, we first screened the potential active compounds of GJF acting on HCC and clarified the molecular mechanism of GJF intervention in HCC through the PI3K/Akt signaling axis using network pharmacology. A total of 63 key targets of GJF acting on HCC were obtained, and the results showed that the top five BPs involved in these genes were epithelial cell proliferation, regulation of epithelial cell proliferation, regulation of cell cycle phase transition, gland development, and embryonic organ development, respectively; In terms of CC, the targets were mainly involved in cell-substrate junction, cell-substrate adherens junction, focal adhesion, adherens junction, and extracellular matrix composition. In the category of MF, they were mainly associated with cell adhesion molecule binding, proximal promoter sequence-specific DNA binding, chromatin binding, RNA polymerase II proximal promoter sequence-specific DNA binding and laminin-binding are related. In addition, the top five pathways were the cell cycle, cellular senescence, p53 signaling pathway, PI3K/Akt signaling pathway, and progesterone-mediated oocyte maturation. Studies have shown that telomere shortening and cell cycle checkpoint inactivation are characteristic of the development of human liver cancer . Cellulap < 0.05). Our hypothesis was further verified by RT‒qPCR results. GJF may inhibit the mRNA expression levels of related molecules in the PI3K/Akt signaling pathway and regulate the mRNA expression levels of proliferation- and apoptosis-related proteins downstream of the pathway. Combined with molecular docking technology, EPHA2 may be the key target of GJF in HCC through the PI3K/Akt signaling pathway.HSP90AA1 plays an important role in the proliferation, differentiation, survival, and angiogenesis of tumor cells . HSP90AAMolecular docking results showed that PF and PA might be the key compounds in hepatocellular formation and have anticancer activity. PF is an important compound that protects the liver, reduces cholestasis, alleviates liver fibrosis, and prevents HCCX. . Yang Zhin vitro experiments. GJF is a well-known prescription with beneficial properties. Therefore, further in vivo and in vitro experimental verification is still necessary.However, our study had certain limitations. Because TCM has the characteristics of multitarget and multipathway effects, the pathway we verified is likely to be only one way for GJF to exert its effects. In this study, we made predictions based on data analysis and a small number of In conclusion, this study revealed a potential pharmacological mechanism by which GJF can treat HCC at the systemic level, possibly involving the synergistic effects of multiple mechanisms, such as cell proliferation, apoptosis, cell migration, immune regulation, and inflammatory induction. Our results suggested that GJF may induce G0/G1 phase cycle arrest by inhibiting the PI3K/Akt signaling pathway and promoting the apoptosis of liver cancer cell lines. EPHA2 may be an important target of GJF in HCC, and pachymic acid may be an important active compound of GJF that exerts anticancer activity. This study can provide a scientific basis for the subsequent clinical application of GJF and the in-depth study of its mechanism. Given that the study is primarily based on data analysis, further biological experiments are crucial to verify the results.


The AMPK signaling pathway was significantly enriched in the four comparative groups. The weighted gene co-expression network analysis (WGCNA) results showed that the hub genes P4HA2, FBXL4, and PPARA were identified to regulate the meat quality. In summary, 1.5 years was found to be the most suitable slaughter age of the Tibetan sheep which ensured better meat tenderness and higher IMF content. Moreover, the genes LIPE, LEP, ADIPOQ, SCD, and FASN may regulate the transformation of the muscle fiber types through the AMPK signaling pathway, further affecting the meat quality.Tibetan sheep are mainly distributed in the Qinghai–Tibet Plateau. Its meat is not only essential for the local people but also preferred by the non-inhabitant of this plateau also. To investigate the salient development features and molecular mechanism of the meat difference of LT muscle caused by different growth stages in Tibetan sheep, the carcass performance, meat quality, and comparative transcriptome analysis were performed for investigating the potential molecular mechanism of the meat quality difference of the LT muscle caused by four growth stages  in the Tibetan sheep. The shear force increased with the increase of age ( Ovis aries) are mainly distributed on the Qinghai–Tibet Plateau above 3,000-m altitude. The sheep is wholly adapted to the special ecological environment with extreme conditions, such as lower temperature, thinner oxygen level, and stronger ultraviolet rays. The sheep mainly produces meat and is loved by all the consumers in Northwest China for its unique flavor and palatability, which is influenced by the natural environment , tenderness, color, juiciness, and flavor . Liu et  in pork . HoweverThere is still a lack of systematic research on the regulation of changes in the muscle fiber types especially during the growth and development of the skeletal muscle of the animals after birth. In the few years, the next generation sequencing technology and bioinformatics methods have developed rapidly, like the RNA-seq, the molecular mechanism of muscle tenderness changes at different growth stages in Tibetan sheep was explored simply and effectively. The comparative transcriptome analysis of the LT muscle was used for identifying the regulatory genes related to meat tenderness and meat quality at different growth stages in Tibetan sheep. Furthermore, the functions and signaling pathways of DEGs were investigated. The WGCNA regulatory network was further used to analyze the hub genes related to meat quality.The animal study was reviewed and approved by the Faculty Animal Policy and Welfare Committee of Gansu Agricultural University .n = 4), 1.5 years (n = 4), 3.5 years (n = 4), and 6 years (n = 4). For the detailed samples collection process, see our previous study  . The proThe total RNA was extracted according to the methods as described by Bao et al. . After tOar_v1.0 reference genome using HISAT2 (The raw data cleaning was filtered by fastp (v0.18.0) . The detg HISAT2  (v2.1.0)g HISAT2 , 28 was 2(Fold Change)| > 1 were used to identify DEGs.The fragment per kilobase of transcript per million mapped reads (FPKM) was used to calculate the expression levels of mRNA using RSEM . The DEGhttp://david.abcc.ncifcrf.gov/) online analysis software was used for GO function annotation and KEGG pathway enrichment analysis  database (v11.5) was used to construct the network. The top-10 connectivity within the module genes was screened for constructing the network, and it was displayed by the Cytoscape software .GAPDH was used as a reference gene for calculating the relative expression according to 2ΔCt−Δ method  was used to verify the authenticity of the transcriptome results according the methods as described by Wen et al. . The prit method .p < 0.05 and different lowercase letters means that the difference was significant.Statistical analyses included the ANOVA, followed by Duncan's multiple range test for multiple comparisons of the difference of carcass quality and meat quality in SPSS 20.0 software . All data in this study were presented as mean ± standard error (SEM), p < 0.05). The hot carcass weight was increased from 4 months to 3.5 years (p < 0.05), while the weight was decreased from 3.5 years to 6 years (p < 0.05). The dressing percentage was largest at 1.5 years, and then decreased with the increase of age (p < 0.05). The IMF content was increased from 4 months to 1.5 years (p < 0.05), and then decreased with the increase of age. The shear force was increased with the increase of age (p < 0.05). The crude protein was increased with the increase of age, while there was no significant difference between the 3.5 years and 6 years sheep.As shown in Total 16 libraries were constructed in this study. As shown in The criterion of FPKM > 0.01 was used for identifying the potentially expressed genes . A totalvs. 1.5 years, 1.5 years vs. 3.5 years, 3.5 years vs. 6 years, and 4 months vs. 6 years) were identified, each group was identified with 220, 48, 101, and 678 unique DEGs, respectively , and the annotation of the GO database 666 DEGs of 4 months vs. 1.5 years group were found to be significantly enriched to 226 BP, 51MF, and 11 CC (p = 7.39E-5). Also, many DEGs were involved in the coenzyme A metabolic process (GO: 0015936), where the number of genes enriched in the term of binding (GO: 0005488) was the most significant. The organs and bones were speculated to develop first in this stage, and the energy metabolism was relatively vigorous. The IMF content was found to increase significantly from the age of 4 months to 1.5 years, possibly related to the active fatty acid metabolism. The crude protein content also increased significantly in this stage, and was related to amino acid metabolism.The GO enrichment analysis can assist in understanding the function of DEGs between each group more comprehensively, similar to most studies, the DEGs were significantly enriched into the following three main GO categories: Biological processes (BP), molecular functions (MF), and cellular components (CC). Based on the GO significant enrichment analysis (nd 11 CC . Most ofnd 11 CC . The GO p < 0.05), the DEGs in the 1.5 years vs. 3.5 years group were found to be significantly enriched in 146 BP, 33 MF, and 10 CC (p = 7.77E-5) was the most significantly enriched GO term. The protein content increased during this growth stage due to the functions linked with protein synthesis. Although DEGs were also enriched in the functions such as fat synthesis and fatty acid metabolism, the IMF content decreased compared to that in the 1.5-years old, with no significant difference. This might be accountable to the lipids deposited in the subcutaneous fat or other lipids for providing energy.Based on the significant enrichment analysis  the DEGs in the 3.5 years vs. 6 years group were significantly enriched to 230 BP, 38 MF, and 26 CC (p = 0.00018) was the most significantly enriched GO term. It showed the difference in the contractility of different types of muscle fibers, and the IMF was found to significantly decrease due to negative regulation of the lipid biosynthesis.Based on the significant enrichment analysis (nd 26 CC . The GO nd 26 CC  includedp < 0.05), the DEGs in the 4 months vs. 6 years group were significantly enriched to 337 BP, 46 MF, and 31 CC (p = 1.84E-12). The DEGs were mainly enriched in the development, differentiation, and metabolism of the skeletal muscle and other tissues, it was mainly enriched in the process of maturation to aging of the body.Based on the significant enrichment analysis , and there were 14 DEGs annotated in this pathway, which were closely related to the connection of the muscle cells (p < 0.05), and a total of 10 DEGs were annotated. In addition, the adipocytokine, and antigen processing signaling pathway, were also significantly enriched (p < 0.05).The KEGG pathway was used for further analyzing the potential functional signaling pathways of DEGs . The resle cells . The lipvs. 3.5 years group were also significantly enriched (p = 0.038) in the AMPK signaling pathway and ADIPOQ was annotated  in glutathione metabolism, and was related to the protein synthesis, and there were 4 DEGs annotated in this pathway. In addition, the cell adhesion molecules (CAMs), Apelin signaling pathway and Insulin signaling pathway were significantly enriched (p < 0.05).The KEGG enrichment results were shown in nnotated . Gstm5, vs. 6 years group were significantly enriched (p = 0.001215) in the cell adhesion molecules (CAMs), which were related to cell recognition, signal transduction, activated proliferation, and differentiation. A total of eight DEGs were annotated in the above pathway. The DEGs in the 3.5 years vs.6 years group were also enriched in the AMPK signaling pathway (p = 0.079), SLC2A4, and CREB5 were enriched in this signaling pathway (p < 0.05).The DEGs in the 3.5 years  pathway . In addivs. 6 years group were found to be significantly enriched in the axon guidance (p = 0.0001393), while 15 DEGs were found to be significantly enriched in the AMPK signaling pathway (p < 0.05) (There were 24 DEGs in the 4 months  < 0.05) .R2 > 0.8, mean connectivity tends to zero revealed that the power of β = 8 to power processing could construct a scale-free network , hot carcass weight , shear force , and crude protein . There were significant positive correlations found between the MM.gray 60 and shear force , live weight , and crude protein . There were significant positive correlations between the MM.dark-red and IMF   .P4HA2, PLXND1, COL22A1, COL5A1, and NID2) and a transcription factor (Sox8) were identified as the hub genes  and a transcription factor (RORC) were identified with the highest degree of connectivity in the interaction network of MM. grey60 module as hub genes  and a gene (KNDC1) were identified to possess the highest degree of connectivity in the interaction network of MM.dark-red module as hub genes . In this study, was the highest in the 6 years Tibetan sheep meat. The IMF content increased from 4 months to 1.5 years, and then decreased and this was consistent with the result of Saccà (2019) study on the goat meat at different growth stages  in the AMPK signaling pathway (p = 0.00006), with 14 annotated DEGs including LIPE, PCK1, IRS2, PFKFB2, LEP, EEF2, and ACC1, which were closely related to cell proliferation and metabolism. The lipase E hormone sensitive (LIPE) is a type of lipid catabolism enzyme, with a key role in regulating the deposition of fat tissues. The genetic polymorphism of LIPE has been closely related to the dressing percentage of sheep  is a kind of adipokines produced by adipocytes or other cells. In vitro studies have found LEP to stimulate the activation and proliferation of the endothelial cells (EEF2), controls the translational elongation of proteins and inhibits protein production. ACC1 is an acetyl-CoA carboxylase, providing malonyl-CoA for mitochondrial biogenesis . These genes were mainly involved in the lipid metabolism pathways. PRKAG2 participates in regulating the AMPK activity and further affects the AMPK signaling pathway. ADIPOQ plays an important role in fat metabolism regulation, mainly by binding to the receptors and playing a biological function by acting on the target tissues. ADIPOQ receptor 1 (AdipoR1) preferentially binds to the spherical region of ADIPOQ, which is expressed in the skeletal muscle cells and acts through the AMPK and mitogen activated protein kinase (MAPK) pathways . The sterol regulatory element binding proteins (SREBPs) maintain lipid homeostasis by regulating target genes (SLC2A4) has been related to the contraction of the skeletal muscles  in the AMPK signaling pathway. ACACB was possibly involved in regulating fatty acid oxidation rather than fatty acid biosynthesis, thereby affecting the milk production of dairy cows (IGF1R) located on the cell membrane, is activated by the insulin-like growth factor (IGF1 or IGF2) and regulates the growth and differentiation of the cells, as well as the growth, development, and senescence of higher animals  in the PPAR signaling pathway. These genes are related to lipid metabolism and also play a positive effect in regulating the muscle fiber types , insulin signaling pathway, and FoxO signaling pathway were significantly enriched in this study. The peroxisome proliferator activated receptor (PPAR) is a member of type II nuclear receptor superfamily, and a type of nuclear transcription factor activated by the ligands, which critically regulates lipid metabolism, adipogenesis, insulin sensitivity, inflammation, cell growth, and differentiation . There wer types . The IGFer types . The Foxer types , 63. TheP4HA2, PLXND1, COL22A1, COL5A1, NID2, and Sox8 were identified as the hub gene in the interaction network of MM.green module. FBXL4, FBXO32, TBC1D17, and PCMTD2 were identified as the hub gene in the MM.grey60 module. PPARA, ADAM30, UBTFL1, EGR2, and KNDC1 were identified as the hub gene in the MM.dark-red module. P4HA2 is an important factor affecting hypoxia-inducible factor 1α  can be found in the article/The animal study was reviewed and approved by the Faculty Animal Policy and Welfare Committee of Gansu Agricultural University .YW: data curation and writing—original draft. JW, XL, and GB: formal analysis, methodology, and software. JH, ZZ, BS, and FZ: investigation, validation, methodology, and software. SL: funding acquisition and writing—review and editing. YL: project administration, supervision, and writing—review and editing. All authors contributed to the article and approved the submitted version.This work was supported by the fund of Basic Research Creative Groups of Gansu Province (17JR5RA137), the Fuxi Young Talents Fund of Gansu Agricultural University (Gaufx-03Y04) and the Projects of Gansu Agricultural University (GSAU-ZL-2015-031/033), and the key R&D projects in Gansu Province (18YF1WA082).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. The molecular pathogenesis of HCC varies due to the different etiologies and genotoxic insults. The development of HCC is characterized by complex interactions between several etiological factors that result in genetic and epigenetic changes in proto-onco and/or tumor suppressor genes. MicroRNAs (miRNAs) are short non-coding RNAs that also can act as oncomiRs or tumor suppressors regulating the expression of cancer-associated genes post-transcriptionally. Studies revealed that several microRNAs are directly or indirectly involved in cellular signaling, and dysregulation of those miRNAs in the body fluids or tissues potentially affects key signaling pathways resulting in carcinogenesis. Therefore, in this mini-review, we discussed recent progress in microRNA-mediated regulation of crucial signaling networks during HCC development, concentrating on the most relevant ones such as PI3K/Akt/mTOR, Hippo-YAP/TAZ, and Wnt/β-catenin, which might open new avenues in HCC management. Hepatocellular carcinoma (HCC) is the most frequent primary hepatic neoplasm with variable incidence throughout the geographical locations and represents the world’s fourth most common cause of cancer-related mortality . By 2025The molecular pathogenesis of HCC depends on the etiologies and genotoxic insults involved . Typicalhttp://www.mirbase.org) database, a total of 2,654 mature miRNAs have been reported in the Homo sapiens so far. Studies have shown that miRNAs are key regulators of a variety of biological activities, including cell differentiation, apoptosis, proliferation, and tumorigenesis (MicroRNAs (miRNAs) are small single-stranded RNA molecules (20–24 nucleotides) that mediate post-transcriptional gene regulation either by translational repression or mRNA degradation . Accordiigenesis , and theigenesis . Moreoveigenesis . Intriguigenesis . Hence, The phosphatidylinositide 3-kinase (PI3K)/Akt pathway has been linked to cancer pathogenesis since its enzymatic activity was shown to be allied with viral oncoproteins . It compTo date, it has been well established that miRNA dysregulation is crucial in HCC development and progression. In this context, in vitro and in vivo experiments revealed that miR-660-5p could dramatically increase HCC cell proliferation, clone formation, migration, invasion, and tumorigenic potential, whereas its downregulation suppresses malignant growth. It has been proposed that epithelial cancer cells undergo an epithelial-mesenchymal transition (EMT), which is characterized by cell adhesion loss, E-cadherin suppression, acquisition of mesenchymal markers , and enhanced cell motility and invasiveness  protein enhances cell survival, proliferation, migration, and invasion . In thisAs discussed, the PI3K pathway might represent an attractive candidate for tumor therapeutic targeting. In this sense, multiple kinases in the PI3K/AKT/mTOR pathway were chosen for inhibitory activity, and the development of kinase inhibitors with improved specificity and pharmacokinetics has recently facilitated research on the PI3K pathway inhibition clinical trials. Moreover, since numerous miRNAs modulate PI3K/Akt/mTOR pathway during carcinogenesis, they could also be a promising tool for HCC management.In a highly conserved manner, the Hippo-YAP/TAZ pathway modulates tissue homeostasis, organ size, cell regeneration, and growth , and itsin vitro and in vivo approaches, they demonstrated that MEIS2D enhances hepatoma cell proliferation and metastasis via the Hippo-YAP/TAZ signaling pathway. Interestingly, MiR-1307-3p is a key component of the MEIS2D route because MEIS2D and its synergistic molecule, PBX1, co-activated its expression. They also discovered that LATS1 is a functional target of miR-1307-3p, whose inhibition reduces YAP phosphorylation. These data imply that MEIS2D promotes HCC development via the miR-1307-3p/LATS1/YAP circuit. Following the same line,in vivo by negatively regulating the DNA methyltransferases 3B (DNMT3B). These findings suggest that this miRNA potentially functions as a tumor suppressor in HCC by inhibiting DNMT3B and the LATS1-associated Hippo-YAP/TAZ signaling pathway, representing a novel potential therapeutic target for HCC.Intriguingly, YAP and TAZ directly control miRNA biogenesis ; while sHypoxia is a key component of the microenvironment of solid tumors, and it promotes cancer growth. β transduction and Hippo-YAP/TAZ signaling pathway. Moreover, they demonstrated that one of the most significant targets of miR-21-3p, intranuclear SMAD7, promotes YAP1 translocation to the cytoplasm and hinders YAP1 transcription. Interestingly, YAP1 promotes SMAD7 to activate TbRI and inhibits the TGF-β/SMAD signal transduction; therefore, the counterbalance between SMAD7 and YAP1 significantly impacts the TGF-β signal transduction. This result highlighted the oncogenic role of miR-21-3p in HCC by promoting malignant phenotype progression via the Hippo-YAP/TAZ pathway.It is well established that miR-21 is strongly connected with the Hippo-YAP/TAZ signaling pathway . Recentlvia modulating Hippo-YAP/TAZ pathway, it is only the tip of the iceberg, and further research is needed to deeply understand the mechanism to develop novel therapeutics strategies against HCC.Even though a number of studies have been conducted recently to identify miRNAs and their regulatory role in HCC The Wnt/β-catenin is a conserved signaling axis involved in a variety of physiological settings, including differentiation, proliferation, apoptosis, migration, invasion, and tissue homeostasis . Over thin vitro and in vivo through the activation of the Wnt/β-catenin pathway and epithelial-mesenchymal transition (EMT) promotion. Likewise, via modulating Wnt/β-catenin pathway.In this milieu, C-x-C motif chemokine ligand 12 (CXCL12) is a crucial cancer immunity and angiogenesis regulator that triggers HCC progression through Wnt/β-catenin pathway regulation. in vivo. The authors transfected HCC cells with miR-485-5p mimic to better understand the role of miR485-5p in HCC, and they discovered that E-cadherin expression was upregulated while MMP-9, c-myc, cyclin D, and MMP-7 expression was considerably reduced. E-cadherin, also known as CDH1, is an important cancer suppressor, and its poor expression is allied with EMT, which is known to accelerate cancer cell migration and invasion.The protein regulator of cytokinesis 1 (PRC1) has been shown to exert an oncogenic function by promoting tumor formation, transfer, stemness, and progression of early HCC through the Wnt/β-catenin signaling pathway modulation, and its overexpression has been linked to poor HCC patient survival . Remarkavia blocking EMT as well as inactivating WNT/β-catenin pathway (Wilms’ tumor 1 gene (WT1) is an essential nuclear factor for organ development and cell growth. Overexpression of WT1 has been shown to be oncogenic in several types of cancers . Notably pathway .In the past few years, it has been noticed that the Wnt/β-catenin pathway is abnormally activated in several types of cancer, and hence Wnt-targeted therapy has received much attention, and recently Wnt signaling has been translated to preclinical research since effective small-molecule drugs have been developed to modulate the pathway. Nevertheless, an in-depth investigation regarding the microRNA-mediated modulation of this pathway in HCC is necessary to develop advanced disease management strategies.via the canonical Wnt/β-catenin signaling pathway might have a role in HCC progression; however, specific upstream regulators of miRNAs targeting this pathway must be thoroughly investigated before it reaches the clinics. Nevertheless, a greater understanding of the interaction between miRNAs and the canonical Wnt/β-catenin signaling pathway would disclose the underlying cause of HCC and aid in the development of innovative therapeutic approaches.It is well established that miRNAs are significantly involved in hepatic tumorigenicity and progression, and investigators have observed that dysregulated miRNAs promote tumorigenesis post-transcriptionally by influencing oncogenes and tumor suppressors, impacting associated canonical pathways . In thisAdditionally, significant risk factors for HCC include non-alcoholic fatty liver disease (NAFLD), chronic alcohol consumption, aflatoxin B1 exposure, hepatitis B (HBV) and C virus (HCV) infection ; and strIn recent years, significant progress has been made demonstrating miRNA regulation in various cancer, including HCC, and since several signaling pathways are substantially associated with it , we beli


By using network pharmacology and molecular docking technology, we have explored the mechanism of action of Sanqi in the treatment of endometriosis (EMS), in order to provide reference for clinical studies of Chinese medicine treatment of Ems and Chinese medicine pharmacology.There are 123 intersecting targets between the active ingredients of Sanqi and disease targets. In the Protein-Protein Interaction network, Jun proto-oncogene, AP-1 transcription factor subunit, tumor necrosis factor, interleukin 6, etc., are the core proteins. The top 20 genes ranked by degree have been analyzed according to the Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analysis, and 20 pathways have been identified.On the Kyoto Encyclopedia of Genes and Genomes pathway, the most important part is the phosphatidylinositol 3’-kinase-Akt signaling pathway, and on the Gene Ontology pathway, it is the Heme binding. The top 3 targets docked to quercetin have a certain affinity when it is docked to their degree value. Among the chemical components of Sanqi, quercetin has the most targets, suggesting that it may play a major role in the treatment of EMS.The results of molecular docking provide further evidence of the potential role of Sanqi for EMS. Overall, our study provides a new direction for the treatment of EMS and provides the basis for Sanqi as a drug for the treatment of EMS. The cause is the overgrowth of endometrial glands and the stroma in areas other than the lining of the uterus. The clinical manifestations include pelvic pain, an irregular menstrual period, and an abnormal menstrual flow, which can result in infertility and mental illness, as well as malignant cancers. It is easy to recur and causes a greater economic burden on patients. At the present time, EMS is a major public health concern of global concern.Endometriosis (EMS) is the most common problem in women of child-bearing age.,7 EMS belongs to the category of Zheng Jia (blood stasis) diseases according to traditional Chinese medicine, where its disease mechanism has been described as “blood stasis” for a long period of time. Traditional Chinese medicine is usually used by Chinese doctors in order to remove “blood stasis” as part of their treatment. Sanqi is referred to as a “Pharmacy.” “BenCaoGangMu” records that Sanqi has the effect of reducing “blood stasis” and hemostasis, and it can be used for the treatment of traumatic bleeding, traumatic injury, and “ZhengJia” Class diseases. Pharmacological research indicates that,13 there are various active ingredients in Sanqi, such as panax notoginseng saponins, panax notoginseng elements, which may have hemostasis, anti-inflammatory, effects, etc. In this research, we utilized the research method of network pharmacology to predict the mechanism of action of Sanqi on EMS, with the hope of providing reference for further experimental studies.In terms of treating EMS, traditional Chinese medicine offers many advantages, such as reducing its recurrence rate, reducing pain, and having fewer side effects.  for all the active ingredients contained in Sanqi, and based on the active ingredients in the database, we queried the target. And we further searched for important targets related to “Sanqi” on target prediction websites such as SwissTargetPrediction (http://swisstargetprediction.ch/), DrugBank (https://go.drugbank.com/), and PubChem (https://pubchem.ncbi.nlm.nih.gov/). A database such as this will facilitate our research into the modern pharmacology of Chinese medicine and assist us in discovering the essence of Chinese medicine in treating disease. As the active ingredients of Sanqi cannot be fully studied, as well as the clear mechanism of related diseases, this study is limited to all of the chemical components of Sanqi. The study protocols were performed according to Declaration of Helsinki. The data for this study were all obtained from public databases and therefore did not require ethics committee approval.In order to clarify the chemical components contained in Sanqi, we searched the Traditional Chinese Medicine Systems Pharmacology databasehttps://www.genecards.org/), DisGeNET (https://www.disgenet.org/), and OMIM (https://omim.org/). Once the EMS target has been retrieved, we can take the union retrieved from the 3 databases. GeneCard database, DisGeNET database, and OMIM database offer comprehensive, free, and authoritative records of genes and are widely used in network pharmacology research.The therapeutic targets of EMS were collected from 3 sources. Among these are GeneCard (https://www.uniprot.org/), and human target genes are selected during conversion.A set of target points derived from the Traditional Chinese Medicine Systems Pharmacology database is entered into the UniProt database ( to create a Protein-Protein Interaction (PPI) network, imported the PPI network into Cystoscap 3.6.0 software, and used the Cellular Angular plug-in cytoHubba to filter PPI-based central genes to draw network diagrams based on the top 20 rankings.We used the target genes acted upon by the EMS gene and the active ingredients in Sanqi to draw the intersection map of the two and to identify the intersection gene by the software Venny 2.1. We entered intersection genes into the String database facilitates the progression from data collection and analysis to biological significance and enriches target genes according to their functions. For Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analysis, enter the intersection target into the David database (https://david.abcc.ncifcrf.gov/) and use R programming language to generate a degree value bubble chart based on the top 20 targets. The higher the value of degree, the more critical it demonstrates that the target protein is. Use the KEGG pathway analysis website (https://www.kegg.jp/) to draw a pathway map showing the top 20 targets with the highest degree values.David DatabaseGenes corresponding to 17 chemical components associated with EMS in Sanqi and the genes corresponding to the top 10 pathways analyzed by the David website were entered into Cytoscape, and a network of target genes corresponding to the chemical components of Sanqi was constructed. (https://pubchem.ncbi.nlm.nih.gov/) query, and molecular energy was minimized through Chem3D software. The target protein is downloaded from the protein data bank database (https://www1.rcsb.org/) using PyMOL software. The protein ligand is deleted and dehydrated. The protein molecule is hydrogenated, and finally, PyMOL software is used for visualization and docking.A chemical composition is selected for molecular docking with a target gene based on the interaction network between the chemical compositions and the target gene and the degree value. Chemical composition of Panax notoginseng was derived from PubChem databasehttp://links.lww.com/MD/H15.The Figure P value: <1.0 × 10–16. A number of genes have been identified as pivotal genes, including Jun proto-oncogene, AP-1 transcription factor subunit, tumor necrosis factor, interleukin 6, etc. Darker the color of the hub gene network graph, the more critical the gene  as the minimum interaction threshold, and leave the remaining settings as default. Make a PPI network to obtain the intersection target of Sanqi and EMS PPI network diagram Fig. . Number ene Fig. .P value; and the horizontal position of the bubble represents the proportion of genes. The GO pathway in Figure The top 20 degrees ranked by the KEGG pathway analysis of the phosphatidylinositol 3’-kinase-Akt (PI3K-AKT) signaling pathway illustrated in Figure As shown in Figure We selected the 3 targets MAPK1, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma, and RELA, which are most enriched in the top 10 pathways, for docking simulations with quercetin. A hydrogen bond represents the mimic action site of quercetin when it interferes with a target protein. Details are shown in Figures  The treatment ideas and methods of traditional Chinese medicine have, similarly to this epidemic, played a significant role in the development of disease prevention and control. Many factors come into play when treating diseases with traditional Chinese medicine compound prescriptions. It has been found that the interplay between chemical components, oral absorption, and in vivo metabolism of the respective drugs makes it difficult to conduct clear research on the treatment of diseases with traditional Chinese medicine. Accordingly, this network pharmacology study selected the meridian of traditional Chinese medicine, and the efficacy of traditional Chinese medicine is in accordance with the treatment of EMS.It has become increasingly common today for traditional Chinese medicine to be used as a treatment for various diseases in other countries. As a result, it inhibits tumor growth by increasing autophagy and apoptosis and inhibiting angiogenesis. Ectopic endometrial tissue of EMS has characteristics similar to those of tumor tissue, including invasiveness, adhesion, and metastasis. Animal experiments have found that quercetin reduces Estrogen levels in mice suffering from EMS when it is used to treat the condition. EMS is estrogen-dependent and proliferates continuously in response to estrogen. Consequently, quercetin may have a therapeutic effect on EMS. In recent years, research on the anti-tumor properties of beta-sitosterol has been extensive. Beta-sitosterol has analgesic, anti-inflammatory, and anti-tumor properties. However, in terms of interventions for EMS, research on the effect of oleic acid and beta-sitosterol on EMS is inconclusive. This is because both of these components can act on the target PTGS2 gene, and the reduction in PTGS2 expression in the cumulus cells of infertile patients caused by EMS may result in a decrease in cyclooxygenase levels, which in turn will result in impaired oocyte capacity. It can serve as a reference for further study.According to the multicomponent and multitarget network diagrams, the 3 components of quercetin, oleic acid, and beta-sitosterol have the greatest number of targets. Several high degree targets can be affected by quercetin, such as C-C motif chemokine ligand 2, interleukin 6, tumor necrosis factor, RELA, etc. Quercetin itself has anti-inflammatory and anti-tumor properties. As shown in Figure  It will act on the important tumor gene MDM2 proto-oncogene, and MDM2 proto-oncogene will affect the TP53 gene transcription protein P53 via the P53 signaling pathway.,36 According to the research, the positive rate of P53 protein produced by TP53 gene in normal human endometrial tissue is higher than that in abnormal endometrial tissue from Ovarian endometriosis cyst patients. It has been shown that the P53 protein induces apoptosis, indicating that the abnormal expression of this gene is closely related to the abnormal hyperplasia of EMS. Quercetin can act on AKT serine/threonine kinase 1. Quercetin’s ability to influence EMS via the above pathways remains to be investigated. In addition, the results of molecular docking provide further evidence of the potential role of SanQi for EMS.The pathway that is most relevant to the top 20 targets with the highest degree is the PI3K-Akt signaling pathway, which is involved in the process of supporting the progesterone resistance effect in EMS. The pathway is also an important pathway for tumor intervention.In summary, network pharmacology shows that the main active ingredients of Sanqi, especially quercetin, oleic acid, and beta-sitosterol, can interfere with multiple targets. Pancreatic cancer, PI3K-Akt signaling pathway, non-small cell lung cancer, and other pathways play a role. Molecular docking shows that quercetin can interfere with 3 high degree targets, which indicates that quercetin may play a major role in the treatment of EMS by Sanqi.ZL and WF conceived and designed the study; ZL and XY searched the related articles; ZL, WF, XY, and JL analyzed the data; ZL, XY, and JL wrote the manuscript. PL supervised the whole process. All authors read and approved the final manuscript.


Inflammation and immune response play a key role in myocardial injury and repair after myocardial infarction (MI), while the relevant regulatory mechanisms of immune infiltration in MI have been fully explored. Ferroptosis is an iron-dependent form of regulated cell death characterized by an excessive accumulation of iron and lipid peroxides and involves in the pathogenesis of myocardial infarction. In the present study, by integrating intelligent data acquisition, data mining, network pharmacology, and computer-assisted target fishing, we developed a highly efficient system for screening immunity- and ferroptosis-related biomarkers and immunomodulatory ability of herbal ingredients.  Immune infiltration analysis of GSE97320 showed significant neutrophil infiltration in the myocardial infarction group compared to the healthy group, and 807 differentially expressed genes (DEGs) were obtained (526 up-regulated and 281 downregulated). Among these DEGs, 73 immune-related and 8 ferroptosis-related DEGs were obtained. Further protein-protein interaction network analysis revealed 30 hub genes. The DEGs were enriched in a total of 107 biological processes, of which neutrophil-related biological processes were the most significant, enriched in 31 cellular components such as bead-binding hemoglobin complex, hemoglobin complex, and enriched in 36 functions such as bead-binding hemoglobin complex and hemoglobin complex. The DEGs were also enriched in 21 KEGG pathways such as lipid-atherosclerosis and formation of neutrophil extracellular traps. Further analysis identified Toll-like receptor-4 (TLR4) as the key gene, and based on TLR4, 17 herbal ingredients and 6 herbal medicines were predicted by using HERB and Coremine databases. Further molecular docking analysis showed that TLR4 could bind to salvianolic acid b and stigmasterol. The molecular dynamics analysis revealed that TLR4 could bind to salvianolic acid b, stigmasterol, and resveratrol in the stable phase with the binding between TLR4 and salvianolic acid b being the most stable.  TLR4 is a key gene that is related to ferroptosis and immune cell infiltration. Further analysis revealed that 17 herbal ingredients and 6 herbal medicines were predicted to have potential interactions with TLR4. These predicted herbal ingredients/medicines may act synergistically to protect against myocardial injury after MI through suppressing neutrophil extracellular traps. The protective effects may be associated with immune cell infiltration and ferroptosis. Acute myocardial infarction is the leading cause of human death, though the prognosis of acute myocardial infarction has improved significantly with the more widespread use of invasive treatment strategies such as revascularization and percutaneous transluminal coronary intervention (PCI) . MyocardPanax ginseng, Ginkgo biloba, and Gynostemma pentaphyllum have been used for many years in the clinical treatment of cardiovascular disease and have been proven to be effective [In general, multiple mechanisms are often involved in the development of a disease, and the complexity of Chinese herbal medicine demonstrates its potential for multiple therapeutic pathways. The use of Chinese herbal medicine in the treatment of cardiovascular disease is reaching an unprecedented level of acceptance, and many herbal medicines such as ffective . For exaffective ; resveraffective . Fu et affective . Fan et ffective . Studiesffective . Recentlffective . Thus, whttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97320) dataset from Gene Expression Omnibus (GEO) database was included in this study for analysis, and the dataset was contributed by Meng F from Department of Cardiology, China-Japan Union Hospital, Jilin University. In the GSE97320 dataset, three patients with acute myocardial infarction and three healthy people in Northeast Chinese Han people were recruited, total RNA of each sample were extracted from peripheral blood to hybridize with Affymetrix microarrays. Immune cell infiltration analysis was performed by using the cibersort R package for simulation 1000 times after normalization of the genetic data set using the limma R package, and the results were filtered based on adjusted p value (adj. P). Differentially expressed genes (DEGs)  were also screened using the limma R package in R software. In addition to immune cells, we also downloaded immune-related genes from the ImmPort database (https://www.immport.org/home), and the immune-related genes were intersected with DEGs to obtain immune-related DEGs. The ferroptosis gene set was downloaded from the FerrDb database (http://www.zhounan.org/ferrdb/), and the ferroptosis-related genes were then intersected with DEGs to obtain the ferroptosis-related DEGs. The workflow of the analysis is summarized in GSE97320 .The DEGs were imported into the String database for protein−protein interaction (PPI) network analysis with the medium confidence ≥0.400 and PPI enrichment https://www.kegg.jp/kegg/mapper/color.htmlv).GO and KEGG enrichment analyses of DEGs were performed by using the clusterProfiler R package  and the top 20 pathways were selected for visual presentation. Subsequently, up- and downregulated genes enriched on immune-related KEGG pathways were tagged using the Color tool of the KEGG database (http://herb.ac.cn/) and Coremine databases  were used to predict the target herbs and herbal ingredients.The hub genes from DEGs, ferroptosis-related DEGs, and immune-relate DEGs genes were intersected to obtain the original key genes. We considered that the significance of the role of these original key genes in ferroptosis and immune infiltration after myocardial infarction may be different, thus, we also matched these genes with genes in the immune-related KEGG pathway to obtain key genes. Based on key genes, the Herb (https://pubchem.ncbi.nlm.nih.gov) and was converted to the 3D structure by using Chemoffice (Chem3D 19.0). For the Toll-like receptor (TLR4), to obtain the exact structure, we firstly obtained its identity (ID) in the Uniprot database (https://www.uniprot.org/) and then searched the PDB database (https://www.rcsb.org/) for this ID and selected the high-resolution structure for download. Autodock Vina was used to molecularly dock TLR4 to the active ingredients of Chinese herbal medicines, and to verify whether the predicted Chinese herbal medicines could bind key genes for efficacy. The model with the lowest binding free energy was selected as the best model. The molecular dynamics analysis was then performed using Gromacs software (http://www.gromacs.org/) for the receptor and ligand docking models (30 ns), as follows: the simulated systems were all solventized in a dodecahedron box with an accompanying periodic boundary condition of 1 ns, and the system was first stabilized in equilibrium at PVT of 500 ps and 1000 ps, followed by the Nose-Hoover heat bath coupling algorithm and Parrinello-Rahman pressure coupling method were used to bring the system temperature and pressure to 300 k and 1 bar, respectively, and finally the molecular dynamics of the complex was simulated for 30 ns. The simulation parameters are: non-bonded interaction cutoff = 1.2 nm, particle mesh Ewald calculation of long-range electrostatic interaction with a Coulomb radius of 1.2 nm and a time step of 2 fs, temperature coupling using modified Bredesen with a target temperature of 300 k for both the complex and water and a coupling time constant of 0.1 ps, pressure coupling using the Parrinello–Rahman algorithm with a target pressure of 1 bar and a coupling time constant of 2 ps; root-mean-square deviation (RMSD) was used as the evaluation index for the stability of the complexes, and the smaller the average RMSD, the more stable the binding.The 2D structure of herbal ingredients was downloaded from the PubChem database  network analysis and the PPI network is shown in The DEGs derived from GSE97320 dataset were further processed for GO and KEGG enrichment analysis. In the GO enrichment analysis, a total of 107 GO terms such as neutrophil activation and neutrophil-mediated immunity were enriched in biological processed category . A totalIn this study, we found an interesting gene, TLR4, which was presented in immune-related DEGs, ferroptosis-related DEGs, hub genes, and DEGs enriched immune-related KEGG pathway-neutrophil extracellular trap formation, thus, we considered it as the key gene. Based on TLR4, we directly predicted 17 herbal ingredients such as salvianolic acid b, stigmasterol and resveratrol, and 6 herbal medicines such as Radix Salviae liguliobae, Rhizoma Sparganii, and Rhizoma Coptidis in HERB and coremine databases. We found that the 17 herbal ingredients can be extendedly matched to 467 herbal medicines through the HERB database. However, since all data in the Herb database were derived from high-throughput experiments and there is a lack of solid evidence for the extended predicted herbal medicines, thus, we only showed 17 ingredients and 6 herbal medicines in this study .We found that two of the 17 directly predicted herbal components and 6 herbs corresponded to each other, namely, salvianolic acid b-Radix Salviae liguliobae and stigmasterol-Rhizoma Sparganii. Therefore, we molecularly docked TLR4 with salvianolic acid b and stigmasterol, respectively. We selected the model with the lowest binding free energy as the best model . In the In this study, we identified a key gene, TLR4, which was presented in immune-related DEGs, ferroptosis-related DEGs, hub genes, and DEGs enriched immune-related KEGG pathway-neutrophil extracellular trap formation. Studies have demonstrated that neutrophils infiltrated the infarcted tissue and mediated tissue damage through the release of matrix-degrading enzymes and reactive oxygen species after myocardial infarction . NeutropIn the field of traditional Chinese medicine, ginsenoside Rg1 could reduce inflammatory cardiomyocyte apoptosis by inhibiting TLR4 . Immune-Salviae liguliobae and stigmasterol-Rhizoma Sparganii. Molecular docking of the two herbal ingredients with TLR4 separately revealed that salvianolic acid b bound best to TLR4 . Salvianolic acid b has been shown to protect against myocardial ischemia in rats with perfusion injury [κB/NLRP3 signaling pathway [After we obtained this key gene (TLR4), we have two ways to predict the target herbal medicine: one is to directly match TLR4 in the HERB database and Coremine database to predict herbal medicines and herbal ingredients, and the other is extendedly to predict herbal medicine based on these ingredients. We predicted 6 herbal medicines and 17 herbal ingredients directly; while in the latter, we predicted 467 herbal medicines indirectly. As we all know, the composition of herbal medicines is very complex, and it is difficult for us to grasp whether these indirectly predicted herbal medicines are meaningful for the regulation of TLR4. HERB database is the world's largest and most advanced traditional Chinese medicine database, which can demonstrate the traditional Chinese medicine-ingredients-gene-disease relationship. More importantly, herbal medicines data of HERB database are derived from high-throughput tests with high confidence . In our n injury . It exer pathway . Stigmas pathway . Besides pathway . In addi pathway , and to  pathway . TherefoIn this study, we also found that the optimal binding sites for salvianolic acid B and resveratrol were very consistent. TLR4, as one of their common targets, may have competing binding sites and therefore the combination may exhibit reduced efficacy or even serious side effects. This gives us a hint as to whether any of the herbs we predicted should not be used in combination when the best binding site is the same. Nevertheless, our findings were mainly based on the prediction of the online databases, and further experimental assays should be performed to consolidate our findings. In addition, our study was only focused on one dataset, and future studies may explore more datasets to consolidate these findings. The results from the molecular docking and molecular dynamics analysis should be interpreted with caution, as molecular docking and molecular dynamics simulation are the computational simulation of the binding ability of compounds to target proteins, and cannot reach the conclusion of excitation or inhibition.TLR4 is a key gene that is related to ferroptosis and immune cell infiltration. Further analysis revealed that 17 herbal ingredients and 6 herbal medicines were predicted to have potential interactions with TLR4. These predicted herbal ingredients/medicines may act synergistically to protect against myocardial injury after MI through suppressing NETs. The protective effects may be associated with immune cell infiltration and ferroptosis.