As a traditional Chinese medicine, rhubarb  is used to treat various diseases.  To explore the possible antitumor mechanism of rhubarb by using network pharmacology and molecular docking in this study.  Bioactive ingredients and related targets of rhubarb were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. And the gene names corresponding to the proteins were found in the UniProt database. Then, the tumor-related targets were screened out from GeneCards and OMIM databases. Key antitumor targets of rhubarb were acquired by overlapping the above targets via the Venn diagram. The antitumor targets network of rhubarb active components was constructed by using Cytoscape 3.6.0 software. The protein interactions network was constructed using the STRING database. The GO and KEGG pathways involved in the targets were analyzed by using the DAVID database. Autodock Vina software was used to verify the molecular docking of rhubarb components and key targets.  Through screening and analysis, 10 active ingredients and 58 antitumor prediction targets were obtained and constructed a compound-target network. The targets such as CASP3, JUN, MYC, TNF, and PTGS2 may play a crucial role. These targets are involved in cancer pathway, calcium signaling pathway, cell apoptosis, small-cell lung cancer pathway, p53 signaling pathway, and TNF signaling pathway. The docking results indicated that the rhein binding with the CASP3 showed the highest binding energy.  Based on the network pharmacology, the characteristics of multicomponent, multitarget, and multipathway of rhubarb were discussed, which provided a scientific basis for explaining the mechanism in treating cancer and new ideas for further research. Rheum L. genus of the Polygonaceae family. Rhubarb has long been used as an antibacterial, anti-inflammatory, antifibrotic, and anticancer medicine in China [Rhubarb , one of the most ancient herbs, belongs to the in China , 2. It cin China –7. In rein China –12. HoweNetwork pharmacology is based on systems biology to construct a biological network, reveal the pharmacological effects of drugs, and explore the relationship between drugs and diseases from a macro perspective. It is especially suitable for analyzing the pharmacodynamic mechanism of traditional Chinese medicines with more complex ingredients –16. MoleIn this study, network pharmacology and molecular docking methods were used to analyze the antitumor pharmacological effects of rhubarb, which may provide a basis for further experimental investigation.http://tcmspw.com/tcmspsearch.php) is an open and accessible database resource containing information about active compounds, potential targets, and related diseases of Chinese medicine. The keyword “Dahuang” was entered into the search box. The effective compounds were screened by pharmacokinetic absorption, distribution, metabolism, and excretion (ADME) criteria: oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 [The Chinese Medicine System Pharmacology Database  [Prediction of possible targets related to rhubarb compounds was performed by searching “Related Targets” of the TCMSP Database. And the gene names corresponding to the proteins were found in UniProt (ot.org/) . The nethttp://www.genecards.org) and OMIM (https://omim.org/) with “tumor” as the keyword. The targets corresponding to rhubarb active components and the targets retrieved from the disease database were crossed, and the repeated targets were deleted to obtain the antitumor targets of rhubarb.The tumor-related genes were collected from GeneCards (https://string-db.org/) is an online protein interaction (PPI) analysis database. It can use computational predictions to supplement the existing information on protein-protein interactions [Homo sapiens, and the minimum interaction score was 0.4 to build a protein interaction network. The results were exported as a “TSV” format file and imported into the Cytoscape 3.6.0 version for visual analysis. Set the size and color of the node according to the degree value, and set the thickness of the edge according to the combination score.The STRING database  is a database established by the Gene Ontology Federation. There are three categories of GO databases, namely, cellular component (CC), biological process (BP), and molecular function (MF), which describe the cellular environment in which the gene product is located, the biological processes involved, and the possible molecular functions, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway is a knowledge base including most of the known metabolic pathways and some of the known regulatory pathways .https://david.ncifcrf.gov/) is an online biological knowledge base and an analytic tool to extract biological information about gene functional classification, functional annotation, and enriched pathways. The targets of rhubarb were imported into the database, and the species was defined as Homo sapiens for GO and KEGG enrichment analysis. P < 0.05The DAVID online database .was used for KEGG enrichment analysis. A bubble chart was plotted using the OmicShare platform, a free online platform for data analysis (https://pubchem.ncbi.nlm.nih.gov) and imported into Chem3D software to draw three-dimensional (3D) structure diagrams and optimize energy and save them in mol2 format. Then, the files were imported into AutoDockTools-1.5.6 software and saved in pdbqt format. Next, the protein crystal structures corresponding to the core target genes were obtained from the PDB database (https://www.rcsb.org/), imported into PyMOL software to remove water molecules and heteromolecules, then imported into AutoDockTools-1.5.6 software to add hydrogen atoms, saved in pdbqt format, and searched their active pockets. Finally, the compound was used as a ligand and the protein as a receptor for molecular docking. PyMOL software was used to analyze and interpret the results [Select the target with the largest value in the PPI network as the receptor and the active ingredients of rhubarb having the number of gene targets >3 as the ligand for molecular docking verification. The method was used as follows: First, the two-dimensional (2D) structure diagrams of these compounds were obtained from the PubChem database , drug-like properties (DL ≥ 0.18), and components without corresponding targets were screened out. Finally, a total of 10 potential effective components were obtained .The collected 10 effective chemical components of rhubarb were searched on the TCMSP platform for the corresponding target protein, and the gene names corresponding to the proteins were found in UniProt. The network analysis software Cytoscape 3.6.0 was used to construct the antitumor active ingredient-target network. The result is shown in The target number of the 10 active ingredients from compound NO.1 to NO.10 was 14, 1, 4, 1, 8, 1, 2, 36, 20, and 8, respectively.The keyword “tumor” was used to search the reported tumor-related genes in the two disease databases OMIM database and GeneCards database. A total of 13462 genes were obtained after deletion of duplicates. Key antitumor targets of rhubarb were acquired by overlapping the above targets via the Venn diagram. A total of 58 antitumor targets of rhubarb were obtained. The results are shown in The 58 antitumor targets of rhubarb were entered into the STRING database, the PPI results were finally obtained, and the results were drawn using the network analysis software Cytoscape 3.6.0. see . The netTo further analyze its target genes, the target of rhubarb was entered into the DAVID database for GO analysis and KEGG pathway analysis. The results showed that the predicted target genes of rhubarb were mainly enriched in 191 biological processes (BP), 27 cellular components (CC), and 62 molecular functions (MF). The top 5 enriched conditions of GO analysis are shown in Y-axis represents the name of the pathway, the X-axis represents the ratio of targeted genes to background genes, the size of the dot represents the number of genes concentrated on the modified pathway, and the color of the dot represents the significance of enrichment. KEGG pathway analysis targets mainly involve hepatitis B pathway, cancer pathway, calcium signaling pathway, apoptosis, small-cell lung cancer pathway, p53 signaling pathway, and tumor necrosis factor signaling pathway, indicating that rhubarb may act on these signaling pathways in the treatment of tumors.The KEGG pathway enrichment has obtained a total of 75 enrichment pathways, of which the first 20 pathways are shown in In this study, CASP3 and JUN with the highest median value in the PPI network were selected as the protein receptors. The active ingredients including EUPATIN, rhein, toralactone, beta-sitosterol, aloe-emodin, and (−)-catechin, which have the number of gene targets >3, were used as ligands for molecular docking verification. The results are shown in The rhein binding with CASP3 showed the highest binding energy . As a kind of traditional Chinese medicine, rhubarb has a wide range of pharmacological effects and has many functions such as antibacterial and anti-inflammatory. There have been increasing reports on the treatment of tumors using rhubarb in recent years, but its mechanism of action is not yet clear. Network pharmacology is consistent with the thinking of traditional Chinese medicine, which helps fully explain the antitumor mechanism of rhubarb. In this paper, a total of 10 active ingredients with OB ≥ 30% and DL ≥ 0.18 were screened in the TCMSP database. Among them, there are increasing reports about emodin, rhein, aloe-emodin, and so on. Many studies have found that they can exert their antitumor effects through a variety of ways, such as inhibiting tumor cell proliferation, promoting apoptosis, inhibiting invasion and metastasis, and combining radiotherapy and chemotherapy drugs to attenuate and increase efficacy.+ and cytochrome C released into the cytoplasm, triggering the activation of caspase 3 and subsequent apoptosis [The component-target network reflects the antitumor features of rhubarb with multiple components and multiple targets. The PPI network shows that there were interactions between the targets of rhubarb. Among them, CASP3 (24), JUN (24), MYC (23), TNF (20), and PTGS2 (19) are the top 5 nodes with the highest degree value, and it was speculated that they may be the main targets of rhubarb's antitumor effects. CASP3 occupies an essential position in cell apoptosis. Once activated, the cell will die irreversibly. Li et al. reported that aloe-emodin treatment could inhibit the cell viability of SCC15 cells, and the potential mechanism of inhibition might be through the induction of apoptosis by regulation of the expression levels of caspase-9 and caspase-3 . Li et apoptosis . JUN (trpoptosis .From the analysis of pathways, the antitumor pathways of rhubarb mainly involve cancer pathway, calcium signaling pathway, apoptosis, small-cell lung cancer pathway, p53 signaling pathway, and tumor necrosis factor TNF signaling pathway. The calcium signal pathway is widely involved in the entire physiological process of the normal cell cycle, such as cell growth, proliferation, differentiation, etc. Increasing studies have found that the remodeling or abnormality of the calcium signal pathway is an important relevant feature of cancer cells. P53 is a tumor suppressor protein, which has a regulatory effect on the expression of various genes, including apoptosis and inhibition of cell cycle progression. The pathways mentioned above are closely related to the antitumor effect, indicating that rhubarb has a good antitumor effect.The active molecules of rhubarb had a good binding activity with key target proteins, indicating that the molecular docking results were consistent with the results of network pharmacology, which further proved the reliability of network pharmacology prediction targets.Traditional Chinese medicine has been passed down for thousands of years in China. This study explored the role of rhubarb in the treatment of tumors through network pharmacology and molecular docking. After screening and analysis, 10 active ingredients and 58 antitumor effect prediction targets in rhubarb were obtained, involving multiple pathways and multiple targets. This study provides clues for understanding the molecular mechanisms of potential targets of rhubarb in the treatment of cancer and provides a reference for further experimental study.There are some defects in the present work. This study lacks experimental verification, which will be further validated in future studies.


Panax notoginseng mixture (PNM) has the characteristics of multicomponent, multitarget, and multieffect, which can cope with the multidirectional and multidimensional complex pathological process caused by hepatic ischemia/reperfusion injury (HIRI). Our animal experiments showed that PNM composed of notoginseng, dogwood, and white peony root could significantly reduce the level of aspartate transaminase and alanine aminotransferase in the blood of mice with HIRI, indicating that this preparation had a protective effect on HIRI in mice. Therefore, on this basis, the molecular mechanism of PNM intervention in HIRI was further explored by network pharmacology. First, target genes corresponding to active components and HIRI were obtained through databases such as TCMSP, Pharm Mapper, Swiss Target Prediction, GeneCards, and so on. All target genes were standardized by Uniprot database, and a total of 291 target genes with their intersection were obtained. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and biological processes (BPs) of 291 target genes were obtained through the online public platform of DAVID. A total of 177 KEGG pathways and 337 BPs were obtained by setting p < 0.01 and false discovery rate <0.05. The network mapping map of components and disease targets was drawn by Cytoscape, and the top 10 Hub target genes related to HIRI were obtained. At the same time, the String database was used to obtain the protein–protein interaction dataset, which was imported into Cytoscape, and the first 10 Hub target genes were obtained. The Hub target genes obtained by the above two methods were molecular docking with their corresponding small molecule compounds through DockThor online tool. The results showed that the docking of paeoniflorin with glyceraldehyde 3-phosphate dehydrogenase (GAPDH), paeoniflorin and loganin with SRC, ginsenoside Rb1 with NR3C2, ursolic acid and oleanolic acid with IL-6, paeoniflorin docking VEGFA, and MMP9. Finally, NR3C2, SRC, and GAPDH were identified as target genes in this study by referring to relevant literature reports. After verification by immunohistochemical experiments, compared with the sham group, the above three target genes were highly expressed in the HIRI group (p < 0.01). Compared with the HIRI group, the expression of three target genes in the PNM + HIRI group was significantly decreased (p < 0.01). The results showed that PNM could protect mouse HIRI by decreasing the expression of NR3C2, SRC, and GAPDH. Hepatic ischemia/reperfusion injury (HIRI) refers to the process of hepatic cells undergoing different degrees of apoptosis and necrosis during blood reperfusion after the temporary loss of blood supply, which aggravates hepatic function damage. It is commonly seen in the process of hepatic transplantation, hepatic resection, hemorrhagic shock or trauma, and so on . HIRI isHIRI is a common pathophysiological phenomenon in clinical practice, and its pathogenesis remains unknown. There is no good prevention and treatment method . In receAtractylodes macrocephala on polysaccharides inhibited the expression of nuclear factor κB, inhibition of enzyme activity, and reduction of the generation of oxygen free radicals reducing HIRI  is based on the advice of senior clinical experts of traditional Chinese medicine and reference to the relevant Chinese medicine pharmacology books. The whole formula consists of notoginseng radix et rhizoma (notoginseng), corni fructus (dogwood), and Paeoniae radix alba (white peony root). Li et al. reported that the TCM syndrome differentiation diagnosis of HIRI was mainly blood stasis syndrome , indicating that PNM could effectively intervene in the occurrence of HIRI in mice. Therefore, the potential mechanism of PNM alleviating HIRI in mice using bioinformation network pharmacology is described in detail in the following. The overall design of this study is shown in The animal experiments showed that PNM could effectively reduce the expressions of aspartate transaminase (AST) and alanine aminotransferase (ALT) in the blood of HIRI mice model group, and there was a significant difference compared with the sham group , license no. SCXK (Beijing) 2014-0004. Male C57BL mice, weighing 19–21 g and aged 5–6 weeks, were raised in a specific pathogen-free environment with air-conditioning at a controlled temperature of 23.5°C ± 1.0°C and a relative humidity of 65 ± 20%. The mice were fed optionally with laboratory chow and water.PNM (containing loganin 2.502 mg/L and paeoniflorin 6.422 mg/L) was provided by the Key Laboratory of Critical Care Emergency Medicine of the National Health Commission . It was administered intragastrically .Twenty-eight mice were randomly divided into four groups with seven mice in each group, which were sham, sham + PNM, HIRI, and HIRI + PNM, respectively. Among them, sham + PNM and HIRI + PNM were given intragastric administration PNM for 14 days, once a day. The other sham and HIRI were given the same amount of normal saline by intragastric administration. One hour after the last administration of preconditioning, animals were anesthetized by injection intraperitoneally with pentobarbital sodium (40 ng/g). The abdominal cavity is opened in the middle of the upper abdomen, carefully exposing the liver, and the hilar ligament is dissociated. In HIRI and HIRI + PNM groups, the left lobe and middle lobe of the liver were clipped to block blood flow, and the clipping was loosened 1 h later to restore blood flow. The sham group only dissociated the hilum without blocking the blood flow. Six hours after reperfusion, eyeballs were removed for blood collection, and parts of the liver tissues were separated and stored at −80°C, whereas the remaining liver tissues were stored in formalin.The collected blood was placed at room temperature for 60 min and centrifuged at 3,000 revolutions/min for 20 min. The serum was separated, and AST and ALT in the blood of mice were determined using an automatic clinical biochemical analyzer (Sysmex Chemix-180) from the First Central Hospital of Tianjin, China. Liver specimens for histopathological analysis were obtained 6 h after reperfusion. Samples were fixed in 4% paraformaldehyde buffer solution and then embedded in paraffin. The samples were sliced into 5-μm sections, dewaxed with xylene and ethanol, and stained with hematoxylin and eosin (HE), followed by dehydration. Then the slices were observed with microscope inspection. Histological changes were evaluated in randomly chosen histological fields at 400× magnification.http://tcmspw.com./tcmsp.php), refer to the related literature for collecting the chemical component information of notoginseng, dogwood, and white peony root in PNM formula. Oral bioavailability (OB) ≥30% and drug-like drug (DL) ≥0.18  and Swiss Target Prediction (http://www.swisstarget.progress.ch/) database predicts the corresponding target genes. After the elimination of duplication, the target genes were unified in the Uniprot (http://www.uniprot.org/) service platform for correction and transformation and finally represented by Uniprot ID to establish the target database of active ingredients of PNM.In traditional Chinese medicine database and analysis platform system pharmacology TCMSP (L) ≥0.18  were thehttp://www.genecards.org/) database, “hepatic ischemia reperfusion injury” was used as keywords to predict the target genes associated with HIRI, and correction and transformation were performed in the Uniprot service platform. Finally, it is represented by Uniprot IDs. A total of 291 target genes with the intersection of components and HIRI were collected by using the function of finding duplicates in Excel, and their Uniprot IDs were imported into Cytoscape 3.7.2 software to construct a visual network diagram of active components and target genes of HIRI disease. Then, the first 10 Hub target genes were obtained by using the MCC function of Cytoscape 3.7.2 plug-in cytoHubba.In GeneCards (https://string-db.org/) service platform, and the limited species was Homo sapiens to construct a protein–protein interaction (PPI) network. Meanwhile, the PPI dataset was imported into Cytoscape 3.7.2, and the top 10 Hub target genes of PPI were obtained by using the MCC function of cytoHubba plug-in.Gene symbol of 219 target genes with intersection was imported into the String (https://david.ncifcrf.gov/) on 291 target genes with the OFFICIAL_GENE SYMBOL as the select identifier, and the species as H. sapiens. KEGG and BP enrichment datasets were obtained using p < 0.01 and false discovery rate (FDR) <0.05 as screening conditions, and their results were presented by bar plots.The Kyoto Encyclopedia of Genes and Genomes (KEGG) and biological process (BP) analysis were performed using DAVID (http://www.rcsb.org/) and the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) to download the structure of protein and small molecule compounds and use the DockThor (https://dockthor.lncc.br/v2/) online tool for molecular docking. According to the molecular docking results and literature review, three target genes, NR3C2, SRC, and glyceraldehyde 3-phosphate dehydrogenase (GAPDH), were finally selected for experimental verification to reveal the molecular mechanism of PNM intervention in HIRI.First, the Hub target genes (Hub target genes obtained through MCC and PPI) and their corresponding active components were applied to PDB  solution and put into a microwave oven for antigen repair. The box was heated for 8 min to boil, then held fire for 8 min, and then heated at medium and low heat for 7 min. After natural cooling, the sections were placed in PBS (pH 7.4) and washed by shaking on a decolorizing bed for three times, 5 min each. The slices were then incubated in 3% hydrogen peroxide solution at room temperature and protected from light for 25 min to block endogenous peroxidase. Then, 3% bovine serum albumin was added evenly in the section ring and sealed at room temperature for 30 min. Then, the blocking fluid was gently removed; PBS diluted antibody (NR3C2 [1:500]), SRC (1:500), and GAPDH  were added to the sections, respectively. The sections were placed flat in the wet box and incubated overnight at 4°C. On the second day, the sections were washed with PBS, and the secondary antibody (horseradish peroxidase label) corresponding to the primary antibody was added to cover the tissues in the circle, and the tissues were incubated at room temperature for 50 min. Then, after washing the slices with PBS, the newly prepared DAB chromogenic solution was added into the ring. The chromogenic time was controlled under the microscope, and the positive color was brown-yellow. The chromogenic process was terminated by washing the slices with tap water. Finally, the nuclei were recolored with hematoxylin and sealed by dehydration.n = 3) in each group by histochemistry score (H-score) method. One-way analysis of variance and Tukey post hoc statistics were used to analyze the differences between groups. p < 0.05 was considered statistically significant.Immunohistochemical assay was performed to quantitatively analyze the positive staining intensity of 3 sections (p < 0.01), indicating successful modeling. Compared with the HIRI model group, the serum ALT and AST levels in the PNM group were significantly decreased (p < 0.01), indicating that PNM had a protective effect on HIRI mice. There was no statistical significance in the changes of ALT and AST in the serum between the sham and sham + PNM groups, indicating that PNM would not cause damage to normal mice, proving the safety of PNM  software was used for its visualization, as shown in , P05231 (IL-6), P01375 (TNF), P02768 (ALB), P05412 (JUN), P42574 (CASP3), P04406 (GAPDH), P27361 (MAPK3), P35354 (PTGS2), and P14780 (MMP9), respectively, as shown in .p < 0.01 and FDR <0.05 were set, and 117 KEGG pathways and 337 BP-enriched datasets of 291 target genes with intersection were obtained from DAVID database and were presented by bar plots. KEGG pathways mainly include TNF signaling pathway, HIF-1 signaling pathway, VEGF signaling pathway, PI3K-Akt signaling pathway, and so on , indicating that the expression of these three target genes was low in normal liver tissue and high in HIRI. Compared with the HIRI group, the expressions of NR3C2, SRC, and GAPDH in the HIRI + PNM group were significantly decreased (p < 0.01), indicating that PNM can protect mouse HIRI through the expression of these three target genes. There were no significant differences in the expressions of NR3C2, SRC, and GAPDH in the sham and sham + PNM groups (p > 0.05), indicating that PNM did not cause damage to normal liver tissue of mice, as shown in  were immunostained with NR3C2 (1:500), SRC (1:500), and GAPDH , respectively. The positive number and staining intensity in each section were converted into corresponding values by H-score, so as to achieve the semiquantitative purpose of tissue staining. H-score is between 0 and 300, and the greater the value, the stronger the comprehensive positive intensity . The resshown in .Partial hepatectomy and liver transplantation are the most effective methods for the treatment of liver malignant tumors and end-stage liver disease. HIRI is a common pathological process during these operations and one of the important causes of perioperative liver failure and death. Therefore, it is of great significance to explore and intervene in the pathogenesis and defense means of HIRI. Notoginseng, dogwood, and white peony root, which make up PNM, are commonly used Chinese medicines in clinical practice, and they are safe and effective. Modern pharmacological studies have shown that these three drugs have regulatory effects as anti-inflammatory, immunomodulatory, antioxidant, anti-tumor, and so on . PreviouTwenty Hub target genes related to HIRI were obtained by applying many online pharmacological public databases and various analysis software, as shown in p ˂ 0.01). Compared with the HIRI group, the expression of SRC in the PNM + HIRI group was decreased (p < 0.01). There was no statistical significance in the expression of SRC in sham and sham + PNM groups (p > 0.05). The above experimental results indicate that PNM can reduce the occurrence of HIRI in mice by down-regulating the expression of SRC and the low expression of SRC in normal tissues. This result is consistent with literature reports that SRC is precisely regulated in normal cells and tissues, but is highly expressed in a variety of human tumors, such as lung cancer, colon cancer, nausea and hematologic diseases, and breast cancer . Compared with the HIRI group, the expression of MR in the HIRI + PNM group was significantly decreased (p ˂ 0.01). The expression of MR in the sham and sham + PNM groups was not statistically significant (p > 0.05), and its expression content was low. The above experimental results indicate that PNM can reduce the occurrence of HIRI in mice by reducing the expression of MR, and the expression of MR is low in normal tissues. Moreover, PNM did not cause damage to normal mouse liver tissue. The expression of MR mRNA in the hypothalamus of Lewis spontaneous kidney-Yang deficiency rats was slightly increased compared with that of normal Wistar rats. Interfering with the Chinese medicine Cordyceps sinensis could inhibit the expression of MR mRNA in the hypothalamus of Lewis spontaneous kidney-Yang deficiency rats  is nuclear receptor subfamily 3 group C member 2. It belongs to the adrenocortical receptor family with glucocorticoid receptor. MR is expressed not only in the epithelial tissues of liver and kidney, but also in human and mouse tissues such as large intestine, salivary glands, airway, sweat glands, and inner ear . Inactivncy rats . It was ncy rats . These rp < 0.01). Compared with the HIRI group, the expression of GAPDH was significantly decreased in the HIRI + PNM group (p < 0.01). The expression of GAPDH in the sham and sham + PNM groups was not statistically significant (p > 0.05), and its expression content was low. The experimental results indicated that PNM could reduce the occurrence of HIRI in mice by reducing the expression of GAPDH, and the expression of GAPDH was low in normal tissues. The results of this study are consistent with the findings of these scholars on the expression of GAPDH in these diseases. Darusman et al. report that levels of GAPDH were significantly higher in patients with Alzheimer disease neurodegenerative disease than in healthy cynomolgus monkeys (In vitro, GAPDH knockdown by siRNA can significantly reduce the proliferation, migration, and invasion of lung squamous cell carcinoma cells (GAPDH is a key enzyme in the glycolytic pathway that catalyzes the conversion of glyceraldehyde 3-phosphate to 1, 3-phosphate glycerate. As a multifunctional enzyme, GAPDH also plays roles in gene expression regulation, DNA repair and replication, neurodegeneration, pathogenic mechanism, regulation of apoptosis and autophagy, and so on . GAPDH p monkeys . Galbiat monkeys . Wang et monkeys . Hao et ma cells . In thisIn short, HIRI is an urgent clinical problem to be solved, so it is of great clinical significance to carry out relevant research. In this study, small molecular compounds in PNM, such as ginsenoside Rb1, paeoniflorin, and loganin, formed complexes with NR3C2, GAPDH, and SRC, respectively, to reduce the expression of these target genes in HIRI, thus achieving the protection of mouse HIRI. There are many targets associated with the occurrence of HIRI. Our study confirmed that NR3C2, GAPDH, and SRC are also important target genes in the prevention and treatment of HIRI. In the future, we will further explore the molecular mechanism of these target genes against HIRI. Inhibitors through NR3C2, GAPDH, and SRC will be developed by ginsenoside Rb1, paeoniflorin, and loganin, making these three target genes promising target genes to weaken or stop the development of HIRI disease.


Zao-Jiao-Ci (ZJC), a traditional Chinese medicine, is considered as a promising candidate to treat laryngeal squamous cell carcinoma (LSCC). However, the underlying molecular mechanism remains unclear. R package; subsequently, enrichment analysis on KEGG and GO of DEGs was performed. The active ingredients of ZJC were screened from the TCMSP database, and the matched candidate targets were obtained by PharmMapper. Furthermore, we constructed protein-protein interaction (PPI) networks of DEGs and candidate targets, respectively, and we screened the core network from the merged network through combining the two PPI networks using Cytoscape 3.7.2. The key targets derived from the core network were analyzed to find out the associated KEGG signal enrichment pathway. By the GEPIA online website, Kaplan–Meier analysis was used to complete the overall survival and disease-free survival of the selected genes in the core module.  Gene expression profiles of GSE36668 were available from the GEO database, and differentially expressed genes (DEGs) of LSCC were obtained by  We identified 96 candidate targets of ZJC and 86 DEGs of LSCC, the latter including 50 upregulated genes and 36 downregulated genes. DEGs were obviously enriched in the following biological functions: extracellular structure organization, the extracellular matrix organization, and endodermal cell differentiation. The 60 key targets from the core network were enriched in the signal pathways including transcriptional misregulation cancer, cell cycle, and so on. We found that LSCC patients with high expression of HIST1H3J, HIST1H3F, and ITGA4 had worse overall survival, while higher expression of NTRK1, COPS5, HIST1H3A, and HIST1H3G had significantly worse disease-free survival.  It suggested that the interaction between ZJC and LSCC was related to the signal pathways of transcriptional misregulation cancer and cell cycle, revealing that it may be the mechanism of ZJC in the treatment of LSCC. Laryngeal squamous cell carcinoma (LSCC) is the most common malignancy of the larynx, and its clinical manifestations are hoarseness, stridor, dyspnea, and even dysphagia , 2. DisaGleditsia sinensis, is a traditional Chinese medicine with a variety of bioactivities, especially antitumor activity, which has been widely used in clinic [Gleditsia sinensis (EEGS) could suppress the growth of human colon cancer HCT116 cells in vitro and in vivo [Gleditsia sinensis fruit performed inhibitory effects on esophageal squamous cell carcinoma (ESCC) cells, breast cancer MCF-7 cells, hepatoblastoma HepG2 cells, and so on [Gleditsia sinensis on LSCC, and the mechanism remains unclear.An increasing number of studies confirmed that traditional Chinese medicine (TCM) including multiple ingredients and targets play a critical role in the treatment of cancer. Zao-Jiao-Ci (ZJC), also known as n clinic . It was nd so on . HoweverNetwork pharmacology has exhibited specific utility in analyzing multicomponent and multitarget, consistent with the therapy hypothesis of complex diseases. By constructing a multilevel, multifaceted network model comprised of components, targets, pathways, and diseases, we can investigate TCM in the treatment of disease involved in the regulation of a variety of signaling pathways, key targets taxa, and biological process analysis, aiming to reveal the mechanism from the molecular level .In this study, we used network pharmacology to investigate whether ZJC exerts anticancer effects on LSCC based on the GEO microarray dataset. And through the pathway enrichment analysis of the interaction targets between differentially expressed gene (DEGs) of LSCC and key node targets of ZJC, we further predicted the therapeutic mechanism of ZJC on LSCC. To our knowledge, this study is the first to explore the efficacy and mechanism of ZJC on LSCC, providing theoretical support and directions for further basic research.http://lilab.ecust.edu.cn/pharmmapper/) to get the targets of the pharmacophore model. The first 15 targets sorted by the fit score were seemed as candidate targets of ZJC.Through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), all components of ZJC could be found by searching the term “Zao-Jiao-Ci.” We set oral bioavailability (OB) > 30% and drug-likeness (DL) > 0.18 as screening conditions supported by the published literatures to obtain the final active ingredients , 11. PhaTo explore the association between ingredients and targets, we established an interaction network. Cytoscape 3.7.2, one of the most favorite open-source software tools, provides visually biomedical interaction networks composed of protein, gene, and other types of interactions. It was used to develop an active ingredient-target PPI network to visualize the relationship between the active ingredients and their targets of ZJC.R language was used to process the original data sets, and the RMA algorithm of Affy software package was used to perform background correction and quartile standardization of the expression matrix. The gene ID, the gene probe name of the expression matrix, was replaced by the gene symbol provided by the GPL17843 Agilent-042818 Human lncRNA Microarray 8_24_v2 platform, and the average value of multiple probes for the same gene was used for analysis. Limma package was used to identify the significant differentially expressed genes (DEGs) according to P < 0.01, |log2 (FC)| > 3.The original data series GSE84957 was downloaded from the Gene Expression Omnibus (GEO) microarray dataset, which contained gene expression profiles of 18 tissue samples . The R language heatmap package for intuitive vision; finally, the clusterProfiler package was used to carry out GO enrichment analysis and KEGG pathway enrichment analysis for DEGs.The screened DEGs were mapped into a volcano map using the P < 0.01 was taken as the inclusion standard for pathway items. The results of enrichment analysis were presented in the form of the pie chart and nodes.BisoGenet plugin, comprising of six available PPI databases , Biomolecular Interaction Network Database (BIND), Molecular Interaction Database (MINT), Human Protein Reference Database (HPRD), and Database of Interacting Proteins (DIP)), was used to build the PPI network for DEGs and candidate target genes, respectively . Then, tκ-core = 2 and conducted KEGG signaling pathway enrichment analysis for the most significantly clustered modules.The MCODE plugin in Cytoscape software was used to screen the highly clustered important modules in the core PPI network. We set the parameters as degree cutoff = 2 and http://GEPIA.cancer pku.cn/) [The correlation between survival rates of LSCC patients  and the gene expression levels  were calculated using GEPIA online database (pku.cn/) .Searching for all the reported components in the TCMSP database, 30 active ingredients of ZJC were collected. Sequentially, only 11 active ingredients were retained which conformed to OB > 30% and DL > 0.18, such as fisetin, fustin, flavanone, and kaempferol . Then, 9A PPI network of the active components and relevant targets, containing 107 nodes and 165 edges, was constructed by the network graphing tool Cytoscape 3.7.2. The 11 active ingredients could be connected with multiple targets, respectively, and each target also could be connected with multiple active ingredients, which directly demonstrated the relationship between active ingredients and targets of ZJC .By analyzing the gene chip of GSE84957, a total of 81 genes with significant different expression of the LSCC tissues compared with adjacent nonneoplastic tissues were obtained, among which 50 genes were upregulated and 31 genes were downregulated in tumor tissues .Staphylococcus aureus infection, and viral protein interaction with cytokine and cytokine receptor were the major pathways involved in DEGs  in the BP category, DEGs were mostly enriched in the extracellular structure organization, the extracellular matrix organization, endodermal cell differentiation, endoderm formation, and endoderm development; (ii) in the category of CC, DEGs were mainly enriched in the extracellular matrix, collagen-containing extracellular matrix, endoplasmic reticulum lumen, collagen trimer, and extracellular matrix component; (iii) in the MF category, extracellular matrix structural constituent, cytokine activity, and receptor ligand activity were selected for main MF. The results of KEGG pathway analysis showed that ECM-receptor interaction, protein digestion and absorption, focal adhesion,  in DEGs .A PPI network based on the targets of ZJC active ingredients was constructed. It showed that ZJC had direct or indirect correlation with the 1572 targets, and there were 29,098 interconnections between these targets. At the same time, the PPI network was mapped for DEGs, and 2,262 targets were directly or indirectly related to LSCC, with 50,181 interconnections between these targets. Then, the intersections of the two PPI networks were used to construct a merged network with 510 nodes and 10950 edges Figures . FurtherThe KEGG signaling pathways analysis suggested that 60 key targets were mainly enriched in cell cycle, central carbon metabolism in cancer, and DNA replication, indicating the mechanisms of ZJC in the treatment of LSCC. The other signaling pathways included prostate cancer, protein processing in endoplasmic reticulum, spliceosome, transcriptional misregulation in cancer, and ubiquitin-mediated proteolysis . ThroughTo demonstrate the relationship between key genes and LSCC, we analyzed the genes in core module through the GEPIA online database and Kaplan–Meier curve. We found that LSCC patients with high expression of HIST1H3J, HIST1H3F, and ITGA4 had worse overall survival, while LSCC patients with high expression of NTRK1, COPS5, HIST1H3A, and HIST1H3G had significantly worse disease-free survival .Based on the network pharmacology analysis of drug and disease target, collateral relationship can effectively reveal the mechanism of ZJC in the treatment of LSCC. Here, we found 96 candidate targets of ZJC and 81 DEGs of LSCC. Then, we constructed the PPI network for them separately. The huge genes involved in the interacted PPI network were analyzed to derive the possible mechanisms of anti-LSCC of ZJC, including transcriptional misregulation cancer, alcoholism, and cell cycle.β-catenin [In our study, we identified 11 active ingredients of ZJC, which synergistically regulated 96 candidate targets. A large number of published literatures showed that the 11 active ingredients had anticancer activities, respectively. As reported, fisetin could inhibit the proliferation and migration of human laryngeal cancer via ERK1/2 and AKT/NF-KB/mTOR signaling pathways and induce apoptosis in human lung cancer through the MAPK signaling pathway , 16. It -catenin . Additio-catenin , 21. To P < 0.01). It is apparent that both signal pathways of transcriptional misregulation in cancer and central carbon metabolism in cancer were closely associated with cancer [P < 0.001). As reported, cancer is more likely to occur in the mucous membrane in direct contact with alcohol; therefore, an intermediate increase in the risk of laryngeal cancer was found among alcoholics [To investigate the possible mechanism of anti-LSCC of ZJC at a system level, we applied GlueGO to complete KEGG enrichment signaling pathway analysis, through analyzing the huge targets of the core PPI network in tight corresponding to LSCC and ZJC. We identified 11 items, in particular, transcriptional misregulation in cancer, alcoholism, cell cycle, and central carbon metabolism in cancer (all h cancer , 23. Seqcoholics . Aberrancoholics . Zhou etcoholics . Proteincoholics , 28. TheOur study revealed that the anti-LSCC mechanism of ZJC was closely connected to transcriptional misregulation cancer, alcoholism, and cell cycle signaling pathway, which provided an important basis for further discussion of the follow-up experiment al design, making the experimental research more reasonable and more instructive.


This study aimed to decipher the bioactive compounds and potential mechanism of traditional Chinese medicine (TCM) formula Fuzi Lizhong Decoction (FLD) for nonalcoholic fatty liver disease (NAFLD) treatment via an integrative network pharmacology approach.  The candidate compounds of FLD and its relative targets were obtained from the TCMSP and PharmMapper web server, and the intersection genes for NAFLD were discerned using OMIM, GeneCards, and DisGeNET. Then, the PPI and component-target-pathway networks were constructed. Moreover, GO enrichment and KEGG pathway analysis were performed to investigate the potential signaling pathways associated with FLD's effect on NAFLD. Eventually, molecular docking simulation was carried out to validate the binding affinity between potential core components and key targets.  A total of 143 candidate active compounds and 129 relative drug targets were obtained, in which 61 targets were overlapped with NAFLD. The PPI network analysis identified ALB, MAPK1, CASP3, MARK8, and AR as key targets, mainly focusing on cellular response to organic cyclic compound, steroid metabolic process, and response to steroid hormone in the biological processes. The KEGG pathway analysis demonstrated that 16 signaling pathways were closely correlated with FLD's effect on NALFD with cancer pathways, Th17 cell differentiation, and IL-17 signaling pathways as the most significant ones. In addition, the molecular docking analysis revealed that the core active compounds of FLD, such as 3′-methoxyglabridin, chrysanthemaxanthin, and Gancaonin H, had a high binding activity with such key targets as ALB, MAPK1, and CASP3.  This study suggested that FLD exerted its effect on NAFLD via modulating multitargets with multicompounds through multipathways. It also demonstrated that the network pharmacology-based approach might provide insights for understanding the interrelationship between complex diseases and interventions of the TCM formula. Nonalcoholic fatty liver disease (NAFLD) is a metabolic disorder with excessive hepatic fat deposition in the absence of significant alcoholic consumption, application of susceptible medication, or other preexisting liver conditions . EpidemiSan Yin Ji Yi Bing Zheng Fang Lun  written by Dr. Wu-ze Chen. It has been widely utilized to treat NAFLD clinically. Previous studies have shown that FLD can promote the proliferation and inhibit the apoptosis of NAFLD cells via upregulating the expression of cyclin-associated and antiapoptotic proteins and downregulating the expression of proapoptotic proteins and apoptotic actors [κB signaling pathway and the secretion of inflammatory response factors TNF-α and IL-6 in the liver [Traditional Chinese medicine (TCM) has a long history of treating liver disease in China, and compelling lines of preclinical and clinical evidence have demonstrated that the TCM formula may yield effective outcomes for the management of NAFLD. Fuzi Lizhong Decoction (FLD) is a classic TCM formula from Volume 2 of c actors . It can he liver . These sNetwork pharmacology integrates systematic medicine and bioinformation science and is consistent with the holistic concept of TCM treatment. It systematically decodes the therapeutic effects of complex TCM formula on disease via analyzing the interactions between herbal components, targets, diseases, and pathways. Hence, it has been considered as a frontier in the field of drug research and development. A growing body of research has currently verified its potential in investigating the possible molecular mechanisms of the TCM formula . In thishttps://tcmspw.com/tcmsp.php) [Chines Pharmacopoeia (2010 edition) with 29,384 ingredients, 3,311targets, and 837 related diseases. The thresholds further filtering the obtained active compounds were set as Oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. The protein targets of the collected active compounds were screened with a standard of “Norm Fit≥0.90” using the PharmMapper web server (http://www.msftconnecttest.com/redirect) [https://www.uniprot.org/) to acquire the gene names, IDs, and functions, respectively [FLD comprises five herbs, namely FuZi , GanJiang , RenShen , BaiZhu , and GanCao . The active compounds of FLD were extracted from the TCMSP data platform (msp.php) , which cectively .The FLD-active compound-target network was constructed and visualized using Cytoscape 3.8.2 software , and thehttp://www.omim.org), Human Gene Database (GeneCards) (https://www.genecards.org), and DisGeNET (http://www.disgenet.org/) databases. After removing the duplicate targets, the protein targets related to NAFLD were imported to the Uniprot data platform. Then, the acquired genes were input to draw Venn diagrams using Venny2.1(https://bioinfogp.cnb.csic.es/tools/venny/), and the intersection genes were obtained as candidate genes.Keywords such as “nonacholic fat liver disease” or “NAFLD” were employed to search the disease-related targets from the Online Mendelian Inheritance in Man (OMIM) (https://www.string-db.org/) to construct a PPI network. The visual network graphs were created by Cytoscape 3.8.2 software. Degree, betweenness, and closeness were three major topological parameters used to identify the key genes.The candidate genes were imported into the Search Tool for the Retrieval of Interacting Genes online analysis software  to explore the relevant biological processes (BPs), cellular components (CCs), molecular functions (MFs), and signal pathways of potential anti-NAFLD targets. The results were saved and sorted with the False Discovery Rate algorithm for each term. The bioinformatics platform (http://www.bioinformatics.com.cn/) was applied to visualize GO and KEGG enrichment results by bubble diagrams.The functional annotation of GO and KEGG pathways was performed using the Metascape platform  network was constructed using Cytoscape 3.8.2. In this network, the compounds, target genes, and pathways are symbolized with nodes in different colors and shapes, and the association between nodes is represented by the edge.http://www.rcsb.org/) and TCMSP data platform, respectively. The AutoDockTools were utilized to convert the candidate compounds into PDB format. The proteins were virtually dehydrated and hydrogenated, and the core compounds were hydrogenated. The original ligands were extracted and stored separately. AutoDockTool was utilized to convert compounds, ligands, and proteins into the “pdbqt” format and to define if the location of each protein or its ligands was the active pocket of the protein. When a binding energy value < 0, the molecular proteins were considered spontaneously binding and interacting. Accordingly, the lower the binding energy required for docking, the more stable the molecular conformation.A molecular docking simulation was conducted to assess the binding energy of the core compounds with the key targets. Autodock Vina 1.5.6 software developed by Olson's research group in Scripps Research Institute was adopted to assess molecular docking . The topInitially, 738 compounds were retrieved from the five herbs in FLD. Among them, 65, 55, 190, 148, and 280 compounds were from FZ, BZ, RS, GJ, and GC, respectively. A total of 147 active compounds were acquired upon OB ≥ 30% and DL ≥ 0.18. Specifically, 21, 7, 22, 5, and 92 compounds were from FZ, BZ, RS, GJ, and GC, respectively. In addition, some compounds were overlapped across different herbs, including sitosterol (ID: MOL000359) in FZ, GJ, and GC, beta-sitosterol (ID: MOL000358) in GJ and RS, and kaempferol (ID: MOL000422) in RS and GC. A total of 143 active compounds were identified after deleting duplicated entries. After the protein targets corresponding to the active components of herbs were imported into the Uniport data platform to acquire the gene names and eliminate the duplications, 129 gene targets with those putative components were acquired. Among which, 45 were associated with FZ, 7 with BZ, 30 with RS, 4 with GJ, and 43 with GC. The information of each candidate compound is shown in The FLD-C-T network contained 280 nodes  and 3,199 edges . SitosteA total of 3,181 targets for NAFLD were integrated from multiple databases, including 512 targets from OMIM, 1,611 from GeneCards, and 1,058 from DisGeNET. The final list of 2,608 NAFLD-related targets was obtained after eliminating duplicates , and the 16 pathways associated with NAFLD were displayed in To further explore the biological functions of the 61 target genes of FLD for NAFLD, GO enrichment analysis was performed based on BP, CC, and MF and yielded 722 entries. BP enrichment analysis provided 617 entries, and the top 20 most enriched terms were presented in a bubble chart with cellular response to organic cyclic compound, steroid metabolic process, and response to steroid hormone as the top three . CC analThe compound-target-pathway network contained 83 nodes  and 268 edges . AnalysiThe docking of the top ten active compounds obtained by the C-T network analysis with three potential core targets, CASP3, ALB, and MAPK1 acquired from the PPT network analysis, was performed. The information of compounds and targets is shown in The treatment of NAFLD remains a major public health challenge globally. Increasing lines of evidence indicate that FLD can treat NALFD effectively, but its mechanism of action remains obscure, especially from a perspective of holistic review. Network pharmacology can reveal the interaction relationships between multicompounds, multitargets, and multipathways of complex formula and therefore and therefore may play a guiding role in drug research and development . MoreoveIn this study, 143 active compounds and 61 relative intersection targets were acquired. The topological analysis on the C-T network indicated the core compounds included sitosterol, beta-sitosterol, and ginsenoside rh2. Intriguingly, previous studies have shown that both sitosterol and beta-sitosterol can reduce hepatic lipid accumulation and regulate the hepatic lipid metabolism of NAFLD. In addition, sitosterol has been reported to reduce inflammation caused by FLD , 18. Gin+, Na+ and fatty acids and promote the occurrence and development of NAFLD through the interaction between Yes-associated protein and TGF-β signaling pathways [α, IL-6, and IL-1β and reduce the damage of inflammatory factors to liver cells [The PPI network was constructed with 61 potential intersection targets. The results indicated that the active compounds in FLD might play pivotal roles in anti-NAFLD through the core targets such as albumin (ALB), mitogen-activated protein kinase1 (MAPKI), caspase-3 (CASP3), mitogen-activated protein kinase 8 (MAPK8), and androgen receptor (AR). ALB binding function has been reported as a novel biomarker to evaluate early liver damage and disease progression of NAFLD . ALB binpathways . Previoupathways , 24. Morpathways . In addipathways . It can er cells . The aboGO and KEGG analyses were conducted using the Metascape data platform. The Go results suggested that putative targets were mainly enriched in cellular response to organic cyclic compound, steroid metabolic process, and response to steroid hormones in BP, vesicle lumen, secretory granule lumen, and ficolin-1-rich granule lumen in CC, and nuclear receptor activity, transcription factor activity, direct ligand regulated sequence-specific DNA binding, and lipid binding in MF. Additionally, the KEGG analysis yielded 181 entries, 16 of which were directly related to the NAFLD, and three pathways that significantly enriched, including pathways in cancer, Th17 cell differentiation, and IL-17 signaling pathway, were retrieved as core pathways for FLD to treat NAFLD. Specifically, the most aggregated targets were pathways in cancer, such as PPAR signaling, MAPK signaling pathway, and Estrogen signaling pathway . Their rFurther, the molecular docking analysis results revealed that all compound-target pairs were lower than 0, indicating that all ten core compounds have good binding affinity to each of the three key targets, namely CASP3, ALB, and MAPK1. The docking pair of 3′-methoxyglabridin-ALB presented the tightest binding (−9.5), followed by chrysanthemaxanthin-MAPK1(−9.2) and Gancaonin H-ALB (−9.1), suggesting that the core compounds of FLD might relieve NAFLD through binding ALB, MAPK1, and H-ALB.In conclusion, this network pharmacology-based study revealed the active compounds and potential mechanism through which FLD is effective on NAFLD by modulating various pathways, such as pathways in cancer, Th17 cell differentiation, and IL-17 signaling pathway. Subsequent molecular docking demonstrated that the top ten compounds of FLD presented desirable binding energy with CASP3, ALB, and MAPK1, further revealing the potentional mechanism of FLD's action on NAFLD. Thus, this study preliminarily reflected the multicomponent, multitarget, and multipathway characteristics of FLD and may provide some insights for future research and development of new anti-NAFLD drugs. However, further experiments are still needed to validate these findings. Special attention will be paid to interpret the mutual interactions between various compounds (or compound compatibility) that may attenuate potential toxicity, decode a structure-activity relationship between the compounds that suggests a chemical nucleus and implies a possibility of structural modification to increase activity, and identify the potential drug “cocktail” - a small collection of drug molecules ideally binding all desired variants .


Long non-coding RNAs (lncRNAs) were considered to be involved in vascular complications in diabetes mellitus, but still only limited knowledge in this regard has been obtained. Herein, we further explored the roles of lncRNAs and mRNAs in diabetic vasculopathy (DV) through conducting bioinformatics analysis using data set downloaded from GEO database. The differentially expressed lncRNAs and mRNAs were identified by edge package. GO enrichment analysis and KEGG pathway analysis were performed based on clusterprofiler package. The relationship between lncRNA and miRNA was predicted using starBase database, and the potential mRNAs targeted by miRNAs were predicted by TargetScan, miRTarbase and miRDB database. The string database was used to analyze the protein-protein interaction (PPI). As a result, a total of 12 lncRNAs and 711 mRNAs were found to be differentially expressed in the diabetic subdermal endothelial cells compared with normal controls. A ceRNA network was established, which was composed of seven lncRNA nodes, 49 miRNA nodes, 58 mRNA nodes and 183 edges, and MSC-AS1 and LINC01550 may serve as key nodes. GO function enrichment analysis showed enrichments of epithelial cell proliferation, intercellular junction, and cell adhesion molecule binding. KEGG pathway analysis revealed 33 enriched pathways. PPI protein interaction analysis identified 57 potential ceRNA-related proteins. Overall, this study suggests that multiple lncRNAs, specifically MSC-AS1 and LINC01550, may play an important role in DV development and they are like to be developed as the therapeutic targets for DV. However, further experiments in vitro and in vivo should be conducted to validate our results.  Diabetes mellitus is a worldwide disease with high incidence and morbidity, and it is characterized with chronic hyperglycemia caused by multiple causes. The pre-diabetes changes and diabetes mellitus (DM) have affected 425 million people worldwide in 2017, and it was predicted to rise to 629 million by 2045 ,4. DiabeThe damage of vascular endothelial cell is the most crucial step during DV development. Under physiological condition, insulin can interact with its receptor on endothelial cells to activate eNOS, upregulates NO release and reduces ET-1 secretion, consequently inducing the vasodilation of blood vessels . HoweverLong non-coding RNA (LncRNA) is a class of noncoding RNA molecules with longer than 200 nucleotides that lack protein-coding functions. Recently, increasing studies suggested that multiple lncRNAs are dysregulated in DM and involved in the pathogenesis of DM. For instance, Ruan et al. revealed that lncRNA-p3134 contributes to glucose metabolism and insulin signal transduction in pancreatic B cells . Feng etTo develop new strategies for preventing and treating DV, in this study, we tried to investigate the role of lncRNAs and their potential mechanisms in DV by the bioinformatics analysis using a high throughput sequencing dataset (GSE92724) and miRNA-predicted websites. As a result, we identified 12 differentially expressed lncRNAs in the diabetic subdermal endothelial cells. Then, the ceRNA regulatory network was established based on those lncRNAs and two key ceRNA sub-networks may be involved in the pathogenesis of DV. Overall, this study helps us to better understand the effects of lncRNAs in the dermal endothelial cells whose damage is involved in the pathogenesis of DV.https://www.ncbi.nlm.nih.gov/geo/). The GSE92724 dataset included 4 diabetic patients and 6 control individuals. The mRNAs and lncRNAs were distinguished by using the annotation information obtained from Genecode database. Totally, 18,206 mRNAs and 2025 lncRNAs were obtained from the high throughput sequencing dataset.The expression of dermal endothelial cells of type 2 diabetic patients processed by high throughput sequencing was downloaded from the Gene Expression Omnibus (GEO) database  [http://www.targetscan.org/vert_72/) [http://mirtarbase.cuhk.edu.cn/php/index.php) [http://mirdb.org/) [The subcellular localizations of lncRNAs were predicted in the lncLocator database by uploading the FASTA sequences downloaded from GenBank database (https://www.ncbi.nlm.nih.gov/) . The DEledu.cn/) . The preert_72/) , miRTarbdex.php)  and miRDdb.org/)  databasedb.org/) . In the https://string-db.org) with a confidence score > 0.40 [The PPI network of mRNAs involved in the ceRNA network was established through an online search tool, Search Tool for the Retrieval of Interacting Genes (STRING) database . A total of 12 differently expressed lncRNAs in subdermal endothelial cells were identified between 4 diabetic patients and 6 control individuals. Then, a ceRNA network was finally established with 183 edges and 114 nodes, including 7 lncRNAs, 49 miRNAs and 58 mRNAs. Additionally, KEGG pathway analysis further revealed that 33 signaling pathways were enriched, such as PI3K/Akt pathway, Rap1 pathway and MAPK pathway. Strikingly, previous studies showed that those pathways are closely associated with DV development. Lu et al. suggested that PI3K/AKT axis meditated the function of miR-21 in promoting retinal vascular endothelial cell viability and angiogenesis in diabetic retinopathy 24. A tot. EvidencAmong seven differentially expressed lncRNAs, MSC-AS1 and LINC01550 that were markedly downregulated in diabetic subdermal endothelial cells were found to serve as key nodes. To date, no studies were performed to explore the roles of MSC-AS1 and LINC01550 in DM. However, numerous recent studies suggested that MSC-AS1 is dysregulated in multiple types of cancers and performs an oncogenic function in tumor progression ,31, wherThere are some limitations in this study, and it should be seriously considered when interpreting our results. To begin with, the conclusion of this study was merely made based on the bioinformatic analysis. Therefore, in future, necessary experiments in vitro and in vivo should be performed to further validate our findings. Additionally, the publica data we used only included 4 diabetic patients and 6 control individuals, so the limitation of small sample size may lead to overestimated conclusion. Hence, large sample and multi-center clinical studies should be conducted to confirm our results.In summary, 12 lncRNAs and 711 mRNAs were identified to differentially express in the diabetic subdermal endothelial cells versus normal controls. Based on these lncRNAs and mRNAs, a lncRNA-miRNA-mRNA interaction network was established, which was composed of seven lncRNA nodes, 49 miRNA nodes, 58 mRNA nodes and 183 edges, and MSC-AS1 and LINC01550 were found to serve as key nodes in the network. Overall, this study suggested that multiple lncRNAs, specifically MSC-AS1 and LINC01550, may play an important role in DV development and they are like to be developed as the therapeutic targets for DV. However, further experiments in vitro and in vivo should be conducted to validate our results.Click here for additional data file.


Radix Rhei Et Rhizome , a classical Chinese herb, has been widely used in gastric cancer (GC) for many years in China. However, inadequate systematic studies have focused on the anti-GC effect of RRER in TME. This study intended to uncover the mechanism of it by network pharmacology.  Tumor microenvironment (TME) takes a vital effect on the occurrence and development of cancer.  We collected compounds and targets of RRER from traditional Chinese medicine system pharmacology database and analysis platform (TCMSP) and SwissTargetPrediction. GC targets were obtained from GeneCards. Protein-protein interaction (PPI) network and RRER-GC-target network were built by STRING and Cytoscape 3.2.1. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed using Database for Annotation, Visualization, and Integrated Discovery (DAVID).  We obtained 92 compounds of RRER. A total of 10 key compounds and 20 key targets were selected by “RRER-GC-target network” topological analysis. GO analysis showed that the biological process mainly involved in response to the tumor necrosis factor, positive regulation of fibroblast proliferation, and DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest. Molecular functions included cyclin-dependent protein serine/threonine kinase activity, RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding, and transmembrane receptor protein tyrosine kinase activity. Cellular components mainly were centrosome, cell surface, and membrane. KEGG pathway enrichment results mainly involved in the p53 signaling pathway, estrogen signaling pathway, and regulation of lipolysis in adipocytes.  This study explored the anti-GC mechanism of RRER from the perspective of TME based on network pharmacology, which contributed to the development and application of RRER. Gastric cancer (GC) remains the world's second most common cause of cancer-related death . There aRadix Rhei Et Rhizome , a classic Chinese herb, has been widely used in GC. For example, RRER-Zhechong pill and RRER-Renshen decoction with chemotherapy are used in advanced GC [In recent years, anced GC , 4, and anced GC . Moreoveanced GC . Besidesanced GC . Its actanced GC .Notably, GC is not a simple mass of tumor cells. It is located in a very complex system named tumor microenvironment (TME), which includes the tumor vasculature, composition of the extracellular matrix, and stroma cells. A compelling body of evidence has demonstrated that TME takes a vital effect on the occurrence and development of GC –11. HoweNetwork pharmacology, first proposed by Hopkins , is a nehttp://lsp.nwu.edu.cn/tcmsp.php, version: 2.3) and SwissTargetPrediction [http://www.swisstargetprediction.ch/, updated in 2019) were used to collect RRER compounds and targets. GeneCards [https://www.genecards.org/, version: 5.0) was applied to get GC targets. UniProt [https://www.uniprot.org/) was selected to supplement the targets' UniProt ID. STRING [http://string-db.org, version 11.0) was used to establish the protein-protein interaction (PPI) network. Cytoscape 3.2.1 [http://www.cytoscape.org/) was applied to construct the RRER-GC-target network and do network topology analysis. Database for Annotation, Visualization, and Integrated Discovery [http://david.ncifcrf.Gov, version: 6.8) was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. OmicShare (https://www.Omicshare.com/) was selected to visualize the GO and KEGG results.The traditional Chinese medicine system pharmacology database and analysis platform  , drug-likeness (DL), intestinal epithelial permeability, blood-brain barrier, and aqueous solubility. Among them, OB  is the rFirst, to predict RRER's targets, we uploaded canonical SMILES to the SwissTargetPrediction database, saved targets' full name, abbreviation, and UniProt ID and then eliminated the repeated targets. When the results of the predicted targets were 0, we used TCMSP to supplement them. Second, we used “gastric cancer” as the keyword to search disease targets in the GeneCards database. After removing targets with relevance score less than 20 and supplementing targets' UniProt ID in the UniProt database, we finally obtained GC-related targets' full names, abbreviations, and UniProt IDs.By mapping RRER targets and GC targets, we obtained RRER and GC common targets and then the constructed PPI network (combined score ≥0.9) through the STRING database. Simultaneously, the RRER-GC-common target network was built by Cytoscape 3.2.1. After that, network topological analysis was carried out to find the pivotal nodes in the network.Go is a gene function classification that describes the properties of genes and gene products. There are 3 ontologies in GO, which include the molecular function, cellular component, and biological process. In this study, we used DAVID to carry out GO and KEGG analysis by importing the key targets into “Functional Annotation.” And then, the OmicShare tool was used to visualize the analysis results.We obtained 92 active compounds of RRER after searching in the TCMSP database, among which 16 active compounds met the screening criteria of “OB ≥ 30%, DL ≥ 0.18” .We obtained 583 targets of RRER by SwissTargetPrediction. Because results of the 2 active compounds -catechin) were 0 in SwissTargetPrediction, we used TCMSP to supplement them and then added 34 targets. Finally, we totally collected 617 RRER's targets . A totalAfter mapping RRER targets and GC targets, a total of 99 common targets were obtained. To better understand the interactions between common targets, a PPI network (combined score ≥0.9) was built by STRING . Based oGO analysis showed that the biological process mainly involved in response to the tumor necrosis factor, positive regulation of fibroblast proliferation, and DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest. Molecular functions included the cyclin-dependent protein serine/threonine kinase activity, RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding, and transmembrane receptor protein tyrosine kinase activity. Cellular components mainly were centrosome, cell surface, and membrane .P values were less than 0.05  PI3nfection  and causnfection . It is rnfection . (3) HIFnfection . Emodin er cells .In conclusion, increasing evidence supports the reliability of the network pharmacology method, which may be an effective way to study the pharmacological mechanisms of Chinese herbs. This study explored the anti-GC mechanism of RRER from the perspective of TME based on network pharmacology, which might contribute to the development and application of RRER. However, some results of our study have not been fully confirmed at present. Future in vitro and in vivo studies will help to give more insight into unveiling the molecular mechanisms of RRER in TME.