Signaling Pathway Enrichment Analysis Research Articles

Effects of Sodium Chromate Exposure on Gene Expression Profiles of Primary Rat Hepatocytes (In Vitro).

Chromium exposure has adverse impacts on human health and the environment, whereas chromate-induced hepatotoxicity's detailed mechanism is still unclear. Therefore, the purpose of the current study was to reveal the crucial signaling pathways and genes linked to sodium chromate-induced hepatotoxicity. GSE19662, a gene expression microarray, was obtained from Gene Expression Omnibus (GEO). Six primary rat hepatocyte (PRH) samples from GSE19662 include sodium chromate-treated (n = 3) and the control PRH samples (n = 3). A total of 2,525 differentially expressed genes (DEGs) were obtained, especially 962, and 1,563 genes were up- and downregulated in sodium chromate-treated PRHs compared to the control. Gene ontology (GO) enrichment analysis suggested that those DEGs were involved in multiple biological processes, including the response to toxic substances, the positive regulation of apoptotic process, lipid and cholesterol metabolic process, and others. Signaling pathway enrichment analysis indicated that the DEGs were mainly enriched in MAPK, PI3K-Akt, PPAR, AMPK, cellular senescence, hepatitis B, fatty acid biosynthesis, etc. Moreover, many genes, including CYP2E1, CYP1A2, CYP2C13, CDK1, NDC80, and CCNB1, might contribute to sodium chromate-induced hepatotoxicity. Taken together, this study enhances our knowledge of the potential molecular mechanisms of sodium chromate-induced hepatotoxicity.

Exploring the Action Mechanism of Yadanzi (Brucea javanica) in the Treatment of Glioblastoma Based on Bioinformatics and Network Pharmacology

Abstract Objective The aim of the study is to explore the molecular mechanism of Yadanzi (Brucea javanica) in the treatment of glioblastoma (GBM) by using the methods of bioinformatics and network pharmacology. Methods The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and literature retrieval method were applied to obtain the active ingredients of Yadanzi (Brucea javanica), and to predict the relevant targets of the active ingredients. The GBM-related targets were retrieved and screened through the Gene Expression Profiling Interactive Analysis (GEPIA) database, and mapped to each other with the targets of the components of Yadanzi (Brucea javanica) to obtain the intersection targets. The GBM differentially expressed gene targets were imported into the String database to obtain the protein interaction relationship, the Cytoscape software was used to draw the protein interaction network, the Cytobba and MCODE plug-ins were used to screen the core genes and important protein interaction modules, and the GEPIA database was applied to make survival analysis of the core genes. The network map of “active ingredients-targets” was constructed through the Cytoscape 3.6.1 software. Gene Ontology (GO) biological function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis for GBM differentially expressed genes were performed through the DAVID database. Results Through TCMSP and literature retrieval, 23 potential active ingredients and 129 related targets were obtained from Yadanzi (Brucea javanica). In the GEPIA database, 247 GBM differentially expressed genes were screened, including 113 up-regulated genes and 134 downregulated genes. After mapping with the targets related to the active ingredients of Yadanzi (Brucea javanica), six intersection targets were obtained, that is, the potential action targets of Yadanzi (Brucea javanica) in treating GBM, including MMP2, HMOX1, BIRC5, EGFR, CCNB2, and TOP2A. Cytoscape software was applied to build an “active ingredient-action target” network. Two active ingredients and five action targets of β-sitosterol (BS) and luteolin were found, and the targets were mainly concentrated in BS. It was found by KEGG pathway enrichment analysis that GBM differentially expressed genes were mainly involved in signaling pathways related to Staphylococcus aureus infection, phagosome formation, tuberculosis and systemic lupus erythematosus and other infectious and autoimmune diseases. It was found by GO enrichment analysis that the GBM differentially expressed genes mainly involved such biological processes (BP) as the processing and presentation of exogenous antigenic peptides and polysaccharide antigens through MHC II molecules, γ-interferon-mediated signaling pathways, extracellular matrix composition, and chemical synapses transmission; it involved cellular components such as cell junctions, axon terminal buttons, extracellular space, vesicle membranes for endocytosis, and MHC II protein complexes; molecular functions such as calcium-mediated ionic protein binding, MHC II molecular receptor activity, immunoglobulin binding, and phospholipase inhibitor activity were also involved. Survival analysis was conducted by GEPIA on the top 37 core targets in degree value, and a total of five genes related to GBM prognosis were obtained. Among them, FN1 and MMP2 were highly expressed while GABRD (γ-aminobutyric acid A receptor delta subunit), RBFOX1, and SLC6A7 were expressed at a low level in cancer patients. Conclusion The pathogenesis of GBM is closely related to the human immune system, and BS and luteolin may be the main material basis of Yadanzi (Brucea javanica) for the treatment of GBM and the improvement of prognosis. The molecular mechanism may be related to the physical barrier formed by destroying the tumor cell stromal molecules and its involvement in tumor immune response.

Research on the effect and underlying molecular mechanism of Cangzhu in the treatment of gouty arthritis

ObjectiveWe aimed to identify the active ingredients and elucidate the underlying mechanism of action of Atractylodes lancea (Thunb.) DC (namely, Cangzhu) for the treatment of gouty arthritis (GA) based on network pharmacology methods. These findings are expected to provide a theoretical basis for the clinical treatment of GA. MethodsWe used monosodium urate (MSU)-induced GA rats as a model to test the overall efficacy of Cangzhu in vivo. Then, the components of the Cangzhu decoction were analyzed and identified, and we screened the active ingredients and their targets. The GA disease targets were predicted by GeneCards and Disgenet databases and found to overlap in both databases. The STRING database was used to construct a protein–protein interaction network, followed by identification of the hub genes using Network Analyzer. Thereafter, Cytoscape software (version 3.8.2) was applied to construct a network for drug-active ingredient-key targets. Next, we applied cluego, a plug-in of Cytoscape, to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway enrichment analyses. Additionally, molecular docking was used to verify the characteristics of the key candidate components interacting with the hub therapeutic targets. Finally, we established an inflammatory injury model of LPS using RAW264.7 macrophages and used it to experimentally validate the critical active ingredients. ResultsCangzhu effectively protected against gouty arthritis in vivo, and network pharmacology results revealed various active ingredients in Cangzhu, such as wogonin, atractylenolide I and atractylenolide II. These compounds were found to act on 16 hub targets, including tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6), interleukin-1β (IL-1β), prostaglandin-endoperoxide synthase 2 (PTGS2), recombinant mitogen-activated protein kinase 14 (MAPK14) and transcription factor p65 (RELA), which have significant effects on regulating inflammatory factors and apoptosis-related pathways to improve the proinflammatory or anti-inflammatory imbalance in the body, and this may be one of the underlying mechanisms of Cangzhu in anti-GA. ConclusionOur findings revealed that Cangzhu comprises multiple active components that exert various targeted effects during GA treatment. These findings provide relevant insights to illuminate the mechanism of Cangzhu in the treatment of GA and provide a reference for further experimental research.

Identification of a five genes prognosis signature for triple-negative breast cancer using multi-omics methods and bioinformatics analysis.

Triple-negative breast cancer (TNBC) has a high degree of malignancy, lack of effective diagnosis and treatment, and poor prognosis. Bioinformatics methods are used to screen the hub genes and signal pathways involved in the progress of TNBC to provide reliable biomarkers for the diagnosis and treatment of TNBC. Download the raw data of four TNBC-related datasets from the Gene Expression Omnibus (GEO) database and use them for bioinformatics analysis. GEO2R tool was used to analyze and identify differentially expressed (DE) mRNAs. DAVID database was used to carry out gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genome Pathways (KEGG) signal pathway enrichment analysis for DE mRNAs. STRING database and Cytoscape were used to build DE mRNAs protein-protein interaction (PPI) network diagram and visualize PPI network, respectively. Through cytoHubba, cBioPortal database, Kaplan-Meier mapper database, Gene Expression Profiling Interactive Analysis (GEPIA) Database, UALCAN Database, The Cancer Genome Atlas (TCGA) database, Tumor Immunity Estimation Resource identify hub genes. Perform qRT-PCR, Human Protein Atlas analysis, mutation analysis, survival analysis, clinical-pathological characteristics, and infiltrating immune cell analysis. 22 DE mRNAs were identified from the four datasets, including 16 upregulated DE mRNAs and six downregulated DE mRNAs. Enrichment analysis of the KEGG showed that DE mRNAs were principally enriched in pathways in cancer, mismatch repair, cell cycle, platinum drug resistance, breast cancer. Six hub genes were screened based on the PPI network diagram of DE mRNAs. Survival analysis found that TOP2A, CCNA2, PCNA, MSH2, CDK6 are related to the prognosis of TNBC. In addition, mutations, clinical indicators, and immune infiltration analysis show that these five hub genes play an important role in the progress of TNBC and immune monitoring. Compared with MCF-10A, MCF-7, and SKBR-3 cells, TOP2A, PCNA, MSH2, and CDK6 were significantly upregulated in MDA-MB-321 cells. Compared with normal, luminal, and Her-2 positive tissues, CCNA2, MSH2, and CDK6 were significantly upregulated in TNBC. Through comparative analysis of GEO datasets related to colorectal cancer and lung adenocarcinoma, it was determined that these five hub genes were unique differentially expressed genes of TNBC. At last, the hub genes related to the progression, prognosis, and immunity of TNBC have been successfully screened. They are indeed specific to TNBC as prognostic features. They can be used as potential markers for the prognosis of TNBC and provide potential therapeutic targets.

Mechanism of Abelmoschus manihot L. in the Treatment of Contrast-Induced Nephropathy on the Basis of Network Pharmacology Analysis.

Contrast-induced nephropathy (CIN) is increasingly seen in patients receiving contrast medium. Abelmoschus manihot (L.) Medik. (Malvaceae) and its preparations are widely used and effective in the treatment of various chronic kidney diseases and CIN in China. It is supposed to be an important adjuvant therapy for CIN. PubMed and CNKI were searched for the main compounds of A. manihot L. The Swiss target prediction platform, OMIM, GeneCards, DisGeNET, and DrugBank databases were mined for information relevant to the prediction of targetsthat A. manihot L. in the treatment of CIN. Subsequently, STRING database was applied for the construction of the PPI protein interaction network, meanwhile, the core targets were screened. DAVID database was used to perform the GO function and Kegg signal pathway enrichment analysis. AutoDockTools and PYMOAL were used for molecular docking. Vitro experiments were used to verify the effect of TFA, the main active component of A. manihot L., in the intervention of iopromide-induced cells injury. A total of 17 chemical components and 133 potential targets in A. manihot L. were obtained. The top 15 proteins with higher degree value were selected from the PPI network model, AKT1, PIK3R1, EGFR, SRC,AR, APP, TNF, GAPDH, MMP9, and PTPN1, etc. may be core targets. The enrichment analysis indicated that A. manihot L. was involved in the regulation of PI3K/AKT signaling pathway, FoxO signaling pathway, VEGF signaling pathway, HIF-1, TNF signaling pathway, melanoma, hepatitis B, and other signaling pathways which were mainly associated with the regulation of transcription and apoptosis, protein phosphorylation, inflammatory response, aging, and cell proliferation. Molecular docking indicated that the key components and core targets had a good binding ability. The vitro experiments illustrated that TFA reduces iopromide induced renal tubular cell injury and apoptosis, which may be related to regulating the phosphorylation ofAKT. The study preliminarily revealed the multi-component, multi-target, and multi-pathway synergistic effects of A. manihot L. on CIN, which provide theoretical reference and basis for the study of the pharmacological mechanism of A. manihot L. in the treatment of CIN.

Gandi Capsule Improved Podocyte Lipid Metabolism of Diabetic Nephropathy Mice through SIRT1/AMPK/HNF4A Pathway.

Podocyte lipid accumulation is a potential therapeutic target for diabetic nephropathy (DN). This study was aimed at clarifying the mechanism of Gandi capsule (GDC) ameliorating DN by regulating the lipid metabolism of podocytes. Network pharmacology methods were performed to screen the key molecules and potential targets of GDC for constructing the molecular-protein interaction network of GDC and conducting signal pathway enrichment analysis. GDC was predicted to ameliorate DN through SIRT1/AMPK/HNF4A pathway. Our results showed that GDC improved renal function in db/db mice. Besides, GDC exhibited effectiveness in relieving kidney tissue damage and renal lipid accumulation in db/db mice, and same effects were present in GDC-active ingredient baicalin. We further proved the new role of HNF4A in the lipid metabolism of DN mediated by SIRT1 and AMPK signaling pathways. The results suggested decreased expression of SIRT1 and p-AMPKα in the kidney tissue and increased expression of HNF4A of db/db mice compared with the control group. GDC and baicalin could reverse these expression changes. Furthermore, similar expression changes were observed in the murine podocyte cell line (MPC-5) treated with different concentrations of GDC and baicalin. Our research suggested that GDC and its active ingredient baicalin could alleviate the abnormal lipid metabolism in the kidney of db/db mice and might exert renal protection through the SIRT1/AMPK/HNF4A pathway.

Identification of key candidate genes and biological pathways in the synovial tissue of patients with rheumatoid arthritis.

The aim of the present study was to identify potential key candidate genes and mechanisms associated with rheumatoid arthritis (RA). Gene expression data from GSE55235, GSE55457 and GSE1919 datasets were downloaded from the Gene Expression Omnibus database. These datasets comprised 78 tissue samples collectively, including 25 healthy synovial membrane samples and 28 RA synovial membrane samples, whilst the 25 osteoarthritis (OA) samples were not included in the analysis. The differentially expressed genes (DEGs) between the two types of samples were identified with the Linear Models for Microarray Analysis package in R. Gene Ontology (GO) functional term and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analyses were also performed. In addition, Protein-Protein Interaction (PPI) network and module analyses were visualized using Cytoscape, and subsequent hub gene identification as well as GO and KEGG enrichment analyses of the modules was performed. Finally, reverse transcription-quantitative PCR (RT-qPCR) was used to validate the expression of the DEGs identified by GO and KEGG analysis in vitro. The analysis identified 491 DEGs, including 289 upregulated and 202 downregulated genes, which were mainly enriched in the following pathways: ‘Cytokine-cytokine receptor interaction’, ‘Rheumatoid arthritis’, ‘Chemokine signaling pathway’, ‘Intestinal immune network for IgA production’ and ‘Primary immunodeficiency’. The top 10 hub genes identified from the PPI network were IL-6, protein tyrosine phosphatase receptor type C, VEGFA, CD86, EGFR, C-X-C chemokine receptor type 4, matrix metalloproteinase 9, CC-chemokine receptor type (CCR)7, CCR5 and selectin L. KEGG signaling pathway enrichment analysis of the top two modules identified from the PPI network revealed that the genes in Module 1 were mainly enriched in the ‘Cytokine-cytokine receptor interaction’ and ‘Chemokine signaling pathway’, whereas analysis of Module 2 revealed that the genes were mainly enriched in ‘Primary immunodeficiency’ and ‘Cytokine-cytokine receptor interaction’. Finally, the results of the RT-qPCR and western blot analysis demonstrated that the expression levels of inflammation and NF-κB signaling pathway-related mRNAs were significantly upregulated following lipopolysaccharide stimulation. In conclusion, the findings of the present study identified key genes and signaling pathways associated with RA, which may improve the current understanding of the molecular mechanisms underlying its development and progression. The identified hub genes may also be used as potential targets for RA diagnosis and treatment.

Bioinformatics screening and analysis of key differentially expressed genes characteristics in nonalcoholic fatty liver disease

Objective: To screen and analyze the key differentially expressed genes characteristics in nonalcoholic fatty liver disease (NAFLD) with bioinformatics method. Methods: NAFLD-related expression matrix GSE89632 was downloaded from the GEO database. Limma package was used to screen differentially expressed genes (DEGs) in healthy, steatosis (SS), and nonalcoholic steatohepatitis (NASH) samples. WGCNA was used to analyze the output gene module. The intersection of module genes and differential genes was used to determine the differential genes characteristic, and then GO function and KEGG signaling pathway enrichment analysis were performed. The protein-protein interaction network (PPI) was constructed using the online website STRING and Cytoscape software, and the key (Hub) genes were screened. Finally, R software was used to analyze the receiver operating characteristic curve (ROC) of the Hub gene. Results: 92 differentially expressed genes characteristic were obtained through screening, which were mainly enriched in inflammatory response-related functions of "lipopolysaccharide response and molecular response of bacterial origin", as well as cancer signaling pathways of "proteoglycan in cancer" and "T-cell leukemia virus infection-related". 10 hub genes (FOS, CXCL8, SERPINE1, CYR61, THBS1, FOSL1, CCL2, MYC, SOCS3 and ATF3) had good diagnostic value. Conclusion: The differentially expressed hub genes among the 10 NAFLD disease-related characteristics obtained with bioinformatics analysis may become a diagnostic and prognostic marker and potential therapeutic target for NAFLD. However, further basic and clinical studies are needed to validate.

Use of Network Pharmacology and Molecular Docking Methods to Elucidate the Curative Effect of Epimedium–Anemarrhen on Osteoporosis

Objective: Using network pharmacology and molecular docking methods, this study aimed to elucidate the key targets and molecular mechanisms of the Epimedium–Anemarrhen (EA) combination as a therapeutic approach for osteoporosis (OP). Methods: The TCMSP database was used to identify effective active ingredients and information on targets of EA according to the ADME threshold. An OP target database was established using the GeneCards, OMIM, DrugBank, and DisGeNET databases. The intersection of drug targets and disease targets was used to identify common genes, and protein interaction network, GO function enrichment, and KEGG signal pathway enrichment analyses were performed. A molecular docking analysis of active pharmaceutical ingredients with core genes was also conducted. Results: The analysis identified 30 effective active ingredients in EA and 149 common genes. A total of 20 core genes were identified by constructing a protein–protein interaction network and calculating the network topology parameters. An enrichment analysis revealed that these targets were primarily involved in biological functions and processes such as cell proliferation, apoptosis, inflammation, oxidative stress, and immunity. The signaling pathways associated with OP include the AGE-RAGE signaling pathway in relation to diabetes complications, the IL-17 signaling pathway, and the TNF signaling pathway. Molecular docking showed stable affinity between the core genes and most of the key components. Conclusion: These data provide mechanistic insights into the pharmacological activity of EA in the treatment of OP and provide an essential theoretical basis for clinical practice.

LncRNA-AC068228.1 Is a Novel Prognostic Biomarker That Promotes Malignant Phenotypes in Lung Adenocarcinoma.

BackgroundThe crucial roles played by lncRNA-AC068228.1 in primary malignant cancer remain poorly understood. This study aimed at examining the clinical significance and evaluating the biological function of AC068228.1 in lung adenocarcinoma (LUAD).MethodsWe used data obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and the Gene Expression Omnibus (GEO) database to examine the expression of AC068228.1 in LUAD patients, and the prognostic and diagnostic value of those levels. Functional experiments were conducted to determine the function of AC068228.1 on LUAD cells. Signaling pathway enrichment analysis of AC068228.1 was conducted using the clusterProfiler and Gene Set Enrichment Analysis (GSEA) software. We analyzed the correlation between AC068228.1 expression and immune infiltration level in LUAD using the single-sample gene set enrichment analysis (ssGSEA) method by the R package GSVA.ResultsAC068228.1 expression was significantly elevated in LUAD tissues compared with normal tissues. Higher expression of AC068228.1 was strongly correlated with adverse clinical outcomes and was identified as an independent prognostic marker for LUAD patients. GSEA and infiltration analysis confirmed that AC068228.1 expression was significantly correlated with immune cells infiltrating in LUAD. Knockdown of AC068228.1 inhibited the cell proliferation and cell migration of LUAD.ConclusionsAC068228.1 was upregulated in LUAD and was significantly correlated with adverse clinical outcomes. Meanwhile, it was associated with immune cell infiltration and could be used as a promising diagnostic and prognostic biomarker for LUAD patients.

Exploration of the Mechanism of Lianhua Qingwen in Treating Influenza Virus Pneumonia and New Coronavirus Pneumonia with the Concept of "Different Diseases with the Same Treatment" Based on Network Pharmacology.

The 31 main components of Lianhua Qingwen (LHQW) were obtained through a literature and database search; the components included glycyrrhizic acid, emodin, chlorogenic acid, isophoroside A, forsythia, menthol, luteolin, quercetin, and rutin. Sixty-eight common targets for the treatment of novel coronavirus pneumonia (NCP) and influenza virus pneumonia (IVP) were also obtained. A “component-target-disease” network was constructed with Cytoscape 3.2.1 software, and 20 key targets, such as cyclooxygenase2 (COX2), interleukin-6 (IL-6), mitogen-activated protein kinase14 (Mapk14), and tumor necrosis factor (TNF), were screened from the network. The David database was used to perform a Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway enrichment analysis and gene ontology (GO) biological process enrichment. Results showed that the key targets of LHQW in the treatment of NCP and IVP mainly involved biological processes, such as immune system process intervention, cell proliferation, apoptosis and invasion, toxic metabolism, cytokine activity, and regulation of the synthesis process. KEGG enrichment analysis revealed that 115 signalling pathways were related to the treatment of LHQW. Amongst them, IL-17, T cell receptor, Th17 cell differentiation, TNF, toll-like receptor, MAPK, apoptosis, and seven other signalling pathways were closely related to the occurrence and development of NCP and IVP. Molecular docking showed that each component had different degrees of binding with six targets, namely, 3C-like protease (3CL), angiotensin-converting enzyme 2 (ACE2), COX2, hemagglutinin (HA), IL-6, and neuraminidase (NA). Rutin, isoforsythiaside A, hesperidin and isochlorogenic acid B were the best components for docking with the six core targets. The first five components with the best docking results were isoforsythiaside, hesperidin, isochlorogenic acid B, forsythin E, and quercetin. In conclusion, LHQW has many components, targets, and pathways. The findings of this work can provide an important theoretical basis for determining the mechanism of LHQW in treating NCP and IVP.

Mechanism of Carthami Flos and Lepidii Semen drug pair in inhibition of myocardial fibrosis by improving cardiac microenvironment based on network pharmacology and animal experiment

Previously, Carthami Flos and Lepidii Semen(CF-LS) drug pair has been proved effective in inhibiting myocardial fibrosis(MF) by blunting the activity of cardiac fibroblasts. The present study explored the underlying mechanism of CF-LS in inhibiting MF by improving the cardiac microenvironment based on network pharmacology combined with experimental verification. Active compounds and potential targets of CF-LS were retrieved from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP), and the potential targets of MF were obtained from GeneCards, Online Mendelian Inheritance in Man(OMIM), and Pharmacogenetics and Pharmacogenomics Knowledge Base(PharmGKB). The "active component-target-MF" network was constructed and analyzed by Cytoscape 3.8.1. The protein-protein interaction(PPI) network was constructed by STRING. The Gene Ontology(GO) biological process enrichment analysis was performed by CluoGO plug-in. Kyoto Encyclopedia of Genes and Genomes(KEGG) signaling pathway enrichment analysis was performed by R 4.0.2 and Funrich. Subsequently, the inhibitory effect of CF-LS on MF was investigated based on angiotensin Ⅱ(Ang Ⅱ)-induced MF rats. RT-PCR and ELISA were used to verify the effect of CF-LS on the targets of signaling pathways related to vascular endothelial cells predicted by the network pharmacology. Thirty-one active components and 204 potential targets of CF-LS, 4 671 MF-related targets, and 174 common targets were obtained. The network analysis showed that the key targets of CF-LS against MF included RAC-alpha serine/threonine-protein kinase(AKT1), transcription factor AP-1(JUN), mitogen-activated protein kinase 1(MAPK1), cellular tumor antigen p53(TP53), transcription factor p65(RELA), and mitogen-activated protein kinase 8(MAPK8). Biological processes mainly involved regulation of blood vessel diameter, regulation of blood vessel endothelial cell migration, cell death in response to oxidative stress, etc. Advanced glycation end products(AGE)-receptor for advanced glycation end products(RAGE) signaling pathway, phosphoinositide 3-kinase(PI3 K)-serine/threonine protein kinase(AKT) signaling pathway, hypoxia-inducible factor-1(HIF-1) signaling pathway, integrin signaling pathway, transforming growth factor-β(TGF-β) signaling pathway, etc. were involved in signaling pathway enrichment. Literature retrieval confirmed that some of these signaling pathways were closely related to vascular endothelial cells, including AGE-RAGE, PI3 K-AKT, HIF-1α, p53, the transcription factor activator protein-1(AP-1), integrin, p38 MAPK, and TGF-β. Animal experiments showed that CF-LS inhibited MF induced by Ang Ⅱ in rats by suppressing the expression of RAGE, HIF-1α, integrin β6, and TGF-β1. The inhibitory effect of CF-LS on MF has the characteristics of multiple components, multiple targets, and multiple pathways. CF-LS can inhibit MF by regulating the activity of vascular endothelial cells in the cardiac microenvironment.

Identification of traditional Chinese drugs containing active ingredients for treating myocardial infarction and analysis of their therapeutic mechanisms by network pharmacology and molecular docking

To identify traditional Chinese drugs that contain active ingredients for treatment of myocardial infarction (MI) and explore their therapeutic mechanisms using network pharmacology and molecular docking technology. The TCMSP database was used for screening the traditional Chinese drugs containing active ingredients for treating MI, and the related targets of MI and the candidate drugs were obtained from Genecards, OMIM, PharmGkb and PharmMapper databases. The common target network of the drug targets and disease targets was established using Venny2.1.0 software. GO and KEGG signal pathway enrichment analysis of the common targets was performed, and the protein-protein interaction (PPI) network was constructed for the targets. The targets in the PPI network were analyzed to identify the key targets, for which GO and KEGG pathway enrichment analyses were performed. Molecular docking was performed for the candidate ingredients and the key targets, and a total score ≥6 was used as the criteria for screening the therapeutic ingredients and their docking binding with key targets was verified. A human umbilical vein endothelial cell (HUVEC) model of oxygen-glucose deprivation (OGD) was used to validate the candidate ingredients and the key therapeutic targets for MI by Western blotting. Our analysis identified Salvia miltiorrhiza and Dalbergiae odoriferae as the candidate drugs rich in active ingredients for treatment of MI. These ingredients involved 16 key therapeutic targets for MI, which participated in such biological processes as inflammatory response, angiogenesis, energy metabolism and oxidative stress and the pathways including HIF-1, VEGF, and TNF pathways. Sclareol and PTGS2 in Salvia miltiorrhiza and formononetin and KDR in Dalbergiae odoriferae all had high docking total scores. Western blotting showed that at medium and high doses, sclareol significantly inhibited PTGS2 expression and formononetin promoted KDR expressions in the cell models in a dose-dependent manner (P < 0.05). Both Salvia miltiorrhiza and Dalbergiae odoriferae have good therapeutic effects on MI. Sclareol in Salvia miltiorrhiza and formononetin in Dalbergiae odoriferae regulate the expressions of KDR and PTGS2, respectively, to modulate the inflammatory response, angiogenesis, oxidative stress and energy metabolism and thus produce myocardial protective effects.

Network Pharmacology Study on the Mechanism of Qiang Huo-Yinchen Treating Diabetic Peripheral Neuropathy

Objective: To explore the mechanism of Qiang huo-Yin chen in the treatment of diabetic peripheral neuropathy (DPN) based on network pharmacology. Methods Using traditional Chinese medicine system pharmacology analysis platform (TCMSP), oral bioavailability (OB) ≥30 %, drug-like (DL)≥0.18 as screening conditions, notopterygium-Yinchen compounds and related targets were obtained. DNP-related genes were obtained from the human gene database (GeneCards) and the online human Mendel genetic database (OMIM). The intersection genes between candidate compounds and DNP were obtained by online Wayne diagram analysis. The interaction network of intersection proteins was constructed by STRING 10.0 database. Gene ontology (GO) analysis and signal pathway enrichment analysis based on Kyoto Encyclopedia of Genes and Genes (KEGG) were performed on the intersection proteins through DAVID database. Cytoscape 3.8.2 software was used to construct the component-target-signaling pathway network of Radix Astragali- Radix Angelicae Sinensis in the treatment of diabetic nephropathy. Results: The constructed component-target network contained 23 active components, including cnidilin, quercetin, beta-sitosterol, isorhamnetin, and areapillin. HSP90AA1, BLC2, CASP3 among 63 targets, KEGG involves 28 pathways such as lipid and atherosclerosis, AGE-RAGE in diabetic complications, apoptosis, pathway signaling pathways of neurodegenerative diseases. Conclusion: Qianghuo-yinchen pair may act on HSP90AA1, BLC2, CASP3 and other targets through multiple components such as quercetin and β-sitosterol, and regulate multiple signaling pathways such as lipid, apoptosis, and neurodegenerative diseases, thereby playing a role in the treatment of DNP.

Molecular Mechanism of Chrysin in Hepatocellular Carcinoma Treatment Based on Network Pharmacology and in Vitro Experiments

This study elucidated the potential molecular mechanism of chrysin in hepatocellular carcinoma (HCC) treatment using network pharmacology and in vitro experiments. Chrysin and candidate targets of HCC were obtained from the TCMSP and DrugBank databases, followed by mapping and screening of chrysin and HCC targets to identify the core targets of chrysin in HCC treatment. The interaction of chrysin and its targets, including CDK1, CDK5, as well as MMP9, were evaluated by molecular docking. The STRING database and Cytoscape (version 3.8.2) software were used to construct protein interactions and component-target networks of the core targets. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of the core target genes were performed using the DAVID database. Network pharmacology results showed that chrysin treatment of HCC was mainly related to cell proliferation and cell cycle. Accordingly, the cell counting kit-8 method and flow cytometry were used to detect the cell viability and cell cycle of hepatocarcinoma cells HCCLM3 and BEL-7402 in vitro. A total of 142 compound targets of chrysin, 12,179 HCC-related targets, and 116 intersecting targets were screened. The first 20 GO biological annotations of 17 core targets and the first 20 KEGG pathways mainly involved cell proliferation and cell cycle. In vitro experiments showed that chrysin inhibits the proliferation of human hepatocarcinoma cells (HCCLM3 and BEL-7402) in a dose-dependent manner. Moreover, chrysin induced cell cycle arrest in HCCLM3 and BEL-7402 cells in the G2 phase, and the expression was downregulated of cyclin-dependent kinases (CDKs), CDK2 and CDK4. Chrysin can offset HCC mainly by regulating the cell cycle and inhibiting cell proliferation. The network pharmacology results were verified, providing the basis for further study on the mechanism of chrysin intervention in HCC.

Characteristics of genomic mutations and signaling pathway alterations in thymic epithelial tumors.

BackgroundTo elucidate the mechanisms of thymic epithelial tumor (TET) canceration by characterizing genomic mutations and signaling pathway alterations.MethodsPrimary tumor and blood samples were collected from 21 patients diagnosed with TETs (thymoma and thymic cancer), 15 of whom were screened by nucleic acid extraction and whole exon sequencing. Bioinformatics was used to comprehensively analyze the sequencing data for these samples, including gene mutation information and the difference of tumor mutation burden (TMB) between thymoma and thymic carcinoma groups. We performed signaling pathway and functional enrichment analysis using the WebGestalt 2017 toolkit.ResultsZNF429 (36%) was the gene with the highest mutation frequency in thymic carcinoma. Mutations in BAP1 (14%), ABI1 (7%), BCL9L (7%), and CHEK2 (7%) were exclusively detected in thymic carcinoma, whereas ZNF721 mutations (14%) and PABPC1 (14%) were found exclusively in thymoma. The mean TMB values for thymic carcinoma and thymoma were 0.722 and 0.663 mutations per megabase (Mb), respectively, and these differences were not statistically significant. The ErbB signaling pathway was enriched in the thymoma and intersection groups, and pathways of central carbon metabolism in cancer, longevity regulating and MAPK signaling were only found in the thymoma group, while pathways in cancer (hsa05200) was found in the thymoma and thymic carcinoma groups.ConclusionsMultiple differences in somatic genes and pathways have been identified. Our findings provide insights into differences between thymoma and thymic carcinoma that could aid in designing personalized clinical therapeutic strategies.

Network pharmacology analysis of the mechanism of our hospital's experiential prescription in the treatment of Guillain Barré syndrome.

This study aimed to explore the active ingredients and potential mechanism of our hospital's Guillain-Barré syndrome (GBS) experiential prescription in the treatment of GBS based on network pharmacology. The traditional Chinese medicine system pharmacology (TCMSP) database was used to screen the active ingredients of the eight traditional Chinese medicines (TCMs) of the GBS-experiential prescription, and the Online Mendelian Inheritance in Man (OMIM), GeneCards, and MalaCards databases were used to obtain GBS-related gene targets. The common targets of the experiential prescriptions and GBS-related gene targets were acquired and imported into the STRING database to obtain the protein interaction relationship. Gene oncology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to predict the major mechanism of this prescription. The formula contained at least 154 potential active ingredients and a total of 4,270 unique targets, among which a total of 158 GBS-related disease targets and 70 common targets were found. The key targets included EGFR (Epidermal Growth Factor Receptor), TNF (Tumor Necrosis Factor), ITGAL (Integrin Subunit Alpha L), and CEBPA (CCAAT/Enhancer-Binding Protein Alpha), CPT2 (Carnitine Palmitoyltransferase 2), CRP (C-reactive protein), ICAM1 (Intercellular Adhesion Molecule 1), IL6 (interleukin 6), and PECAM1 (Platelet and Endothelial Cell Adhesion Molecule 1), CREBBP (CREB Binding Protein), etc. The GO enrichment analysis results revealed 116 terms, and the KEGG signaling pathway enrichment analysis results yielded 61 pathways, including influenza A, hepatitis B, malaria, etc. The development of GBS and the mechanism underlying the effects of the GBS-experiential prescription have common and complex targets, which are worthy of in-depth exploration.

Bioinformatic and experimental analyses of key biomarkers in pancreatic cancer.

The present study aimed to screen the key genes in pancreatic cancer and to explore the pathogenesis of pancreatic cancer. A total of three expression profiling datasets (GSE28735, GSE16515 and GSE15471) associated with pancreatic cancer were retrieved from the public gene chip database. The differentially expressed genes (DEGs) were screened by GEO2R and subjected to Gene Ontology (GO) and signaling pathway enrichment analysis. Furthermore, a protein interaction network was constructed. The GEPIA online database was used to screen for genes that affect the prognosis of pancreatic cancer. Finally, cell functional experiments were performed on the selected key genes. A total of 72 DEGs were identified, including 52 upregulated and 20 downregulated genes. Enrichment analysis revealed roles of the DEGs in endodermal cell differentiation, cell adhesion, extracellular matrix-receptor interaction and PI3K-Akt signaling pathway. In total, 10 key nodal genes were identified, including integrin subunit α 2 (ITGA2), ITGB6 and collagen α 1 chain 1. Through survival analysis, two genes with an impact on the prognosis of pancreatic cancer were identified, namely ITGA2 and ITGB6. Silencing of ITGB6 in a pancreatic cancer cell line significantly suppressed cell proliferation and induced cell cycle arrest at G2/M phase. The identified key genes and signaling pathways may help to deepen the understanding of the molecular mechanisms involved in pancreatic cancer and provide a theoretical basis to develop novel therapies.

ISLR affects colon cancer progression by regulating the epithelial-mesenchymal transition signaling pathway.

This study aims to determine the mechanism of ISLR on the progression of colon cancer. TCGA database was used to analyze ISLR expression in colon cancer tumor tissues. QRT-PCR and western blotting were used to detect ISLR expression in colon cancer cells. CCK-8, colony formation, EDU, wound healing and transwell assays were used to measure cell viability, proliferation, migration and invasion of colon cancer cells, respectively. The signaling pathway enrichment analysis of ISLR was analyzed on the basis of the KEGG database. The protein expression of genes related to signaling pathway was measured by western blotting. Results of TCGA analysis, qRT-PC and western blotting showed that ISLR was upregulated in colon cancer tumor tissues and cells. High level of ISLR was related to low overall survival of patients with colon cancer. ISLR silence significantly inhibited cell viability, proliferation, migration and invasion of colon cancer cells. ISLR overexpression markedly enhanced the cell viability, proliferation, migration and invasion of colon cancer cells. KEGG database analyzed showed that ISLR can activate the EMT signaling pathway. Inhibition of the EMT signaling pathway can suppress the growth, migration, and invasion of colon cancer cells and eliminate the promoted effect of ISLR overexpression on colon cancer progression. ISLR promotes the progression of colon cancer by activating the EMT signaling pathway.

An Exploration on Mechanisms of Andrographolide Nanoemulsifying in Treating Gastric Cancer Based on Network Pharmacology

This article preliminarily revealed the mechanism of andrographolide (AD) nanoemulsifying inhibiting gastric cancer(GC). The targets of AD was searched by TCMSP and SwissTargetPrediction database, we entered the keyword “Gastric Cancer” in GeneCards database to find all targets related to GC. The intersection of AD and GC is taken as an important targets for AD to act on GC, Cytoscape 3.7.1 was used for topological analysis of the target, so as to obtain all important targets for AD to inhibit GC. DAVID database was used to perform biological process analysis on all potential targets, mainly through its analysis function. It involves Cell composition, molecular function and signal pathway enrichment analysis. Finally, we obtained a total of 53 potential targets related to GC, among which IL6, MAPK8, CREBBP, CDK2, MAPK14, AR and MAP2K1 may be important targets for inhibiting GC. The effect of AD on GC is mainly through Influenza A, Pathways in cancer, Sphingolipid signaling pathway, Progesterone-mediated oocyte maturation, Herpes simplex infection and other important pathways which are closely related to 10 biological processes such as protein phosphorylation, signal transduction, peptidyl-serine phosphorylation, positive regulation of gene expression, and positive regulation of transcription from RNA polymerase II promoter Sex. This study can provide guidance for the development of anti-GC drugs.