Kyoto Encyclopedia Of Genes And Genomes Pathway Enrichment Analysis Research Articles

Exploring and bioinformatics analysis of differentially expressed genes in bronchial asthma

Objective: To screen core differentially expressed genes of bronchial asthma and conduct bioinformatics analysis. Methods: Macrophage microarray data GSE22528 from asthma patients were downloaded from gene expression database (GEO). The dataset included transcriptome information from 10 human alveolar lavage fluid samples, and five of them were from allergic asthmatic subjects and five from control subjects. Differential expression genes (DEGs) were screened by R 4.0.4 software. Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to select DEGs using DAVID 6.8 database. Protein interaction network (PPI) was constructed from DEGs encoded proteins using STRING online database. Cytoscape software was used to construct core modules and determine core DEGs. Results: Alveolar lavage fluid samples were all collected from Caucasian Canadians, with age range as (20, 37) and (18, 36) years, respectively, including 3 males for each group. In asthmatic patients, 449 genes were up-regulated and 47 down-regulated. GO analysis showed that the up-regulated genes in asthmatic patients were mainly involved in biological processes such as response to folded proteins, and the molecular function was focused on binding of folded proteins and growth factors. Down-regulated genes were mainly involved in biological processes such as histone deacetylation and ubiquitin-mediated protein degradation, and their molecular functions focused on histone deacetylation activity. KEGG pathway enrichment analysis showed that pathways were mainly enriched by up-regulation genes, involving Hippo signaling pathway, hypertrophic cardiomyopathy, estrogen signaling pathway, arrhythmogenic right ventricular cardiomyopathy, basal cell carcinoma, neuro-activated receptor ligand interaction, dilated cardiomyopathy and adhesion and connection signaling pathways. Two core modules were obtained by PPI analysis, and 14 core DEGs were screened out. They were pro-melanin concentrating hormone (PMCH), prepronociceptin (PNOC), Sphingosinol-1-phosphate receptor 2 (S1PR2), Sphingosinol-1-phosphate receptor 5 (S1PR5), CC-type chemokine ligand 21 (CCL21), Kelch-like protein 25 (KLHL25), ubiquitin binding enzyme E2V2 (UBE2V2), F-box protein 17 (FBXO17), taste receptor type 2 member 3 (TAS2R3), somatostatin receptor 2 (SSTR2), metabolic glutamate receptor 2 (GRM2), Lister E3 ubiquitin protein ligase 1 (LTN1), LIM domain specific protein 7 (LMO7) and ring finger protein 19A gene(RNF19A), in which LTN1 and UBE2V2 were down-regulated and the rest were up-regulated. Conclusion: DEGs was found in macrophages of asthmatic and control individuals. PMCH, PNOC, S1PR2, S1PR5 and CCL21 might be the core genes in the pathological process of asthma.

Identification of Potential Genetic Biomarkers and Target Genes of Peri-Implantitis Using Bioinformatics Tools.

Objectives To investigate potential genetic biomarkers of peri-implantitis and target genes for the therapy of peri-implantitis by bioinformatics analysis of publicly available data. Methods The GSE33774 microarray dataset was downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) between peri-implantitis and healthy gingival tissues were identified using the GEO2R tool. GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID database and the Metascape tool, and the results were expressed as a bubble diagram. The protein-protein interaction network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized using Cytoscape. The hub genes were screened by the cytoHubba plugin of Cytoscape. The potential target genes associated with peri-implantitis were obtained from the DisGeNET database and the Open Targets Platform. The intersecting genes were identified using the Venn diagram web tool. Results Between the peri-implantitis group and the healthy group, 205 DEGs were investigated including 140 upregulated genes and 65 downregulated genes. These DEGs were mainly enriched in functions such as the immune response, inflammatory response, cell adhesion, receptor activity, and protease binding. The results of KEGG pathway enrichment analysis revealed that DEGs were mainly involved in the cytokine-cytokine receptor interaction, pathways in cancer, and the PI3K-Akt signaling pathway. The intersecting genes, including IL6, TLR4, FN1, IL1β, CXCL8, MMP9, and SPP1, were revealed as potential genetic biomarkers and target genes of peri-implantitis. Conclusions This study provides supportive evidence that IL6, TLR4, FN1, IL1β, CXCL8, MMP9, and SPP1 might be used as potential target biomarkers for peri-implantitis which may provide further therapeutic potentials for peri-implantitis.

Protective effects of Fufang Ejiao Jiang against aplastic anemia assessed by network pharmacology and metabolomics strategy

ObjectiveTo elucidate the mechanisms underlying the therapeutic effects of Fufang Ejiao Jiang (复方阿胶浆, FFEJJ) on aplastic anemia (AA) using integrated network pharmacology and serum metabolomics. MethodsTraditional Chinese Medicine Systems Pharmacology (TCMSP), Pubmed, integrative pharmacology-based research platform of traditional Chinese medicine (TCMIP), and Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (BATMAN-TCM) were used to identify the constituents and putative targets of FFEJJ. GeneCards and DisGeNET databases were used to identify AA-associated targets. We constructed a herb-component-target network and analyzed the protein-protein interaction (PPI) network. Potential mechanisms were determined using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. In addition, an AA model was established using acetylphenylhydrazine (APH) and cetylphenylhydrazine (CTX). Ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS)-based serum metabolomics was applied to screen potential metabolites and the related pathways associated with AA and the potential anti-anemic effects of FFEJJ. ResultsA total of 30 active components of FFEJJ and 24 targets were related to AA. PPI network analysis showed that VEGFA, AKT1, IL-6, CASP3, and ICAM1 were key nodes overlapping with proteins known to be related to AA. KEGG pathway enrichment analysis revealed that the presumed targets of FFEJJ were mainly associated with pathways linked to the promotion of hematopoiesis and improvement of the hematopoietic microenvironment. A total of 423 metabolite biomarkers were identified between the control and AA models, which are involved in the development of AA. In contrast, FFEJJ reversed the 79 differential metabolites altered by AA. Pathway analysis suggested that the synergistic effects of FFEJJ were mainly enriched in 24 metabolic pathways. Among them, sphingolipid metabolism, glycerophospholipid metabolism, and arachidonic acid metabolism were related to promoting hematopoiesis and improving the hematopoietic microenvironment, which partially conforms with network pharmacology. The interaction network formed by three key differential metabolites, including hydroxy-eicosatetraenoic acid (HETE), sphingosine 1-phosphate (S1P), and lysophosphati-dylcholine (lysoPC), and three predicted network targets (VEGFA, CASP3, and ICAM1) may be the potential mechanism underlying the anti-AA action of the multi-component of FFEJJ. ConclusionFFEJJ could be an alternative treatment option for AA. It acts by promoting hematopoiesis and improving the hematopoietic microenvironment. Network pharmacology-integrated metabolomics makes it possible to analyze TCMs from a systems perspective and at the molecular level.

Study on differential gene expression profile of serum exosomes in patients with acute cerebral infarction

ObjectiveTo analyze the differential gene expression profile of serum exosomes in patients with acute cerebral infarction (ACI) and clarify the changes in gene expression related to cerebral infarction injury and the potential serum markers. MethodsFour patients with ACI and five healthy people were enrolled in the Phase Ⅰ study. After serum isolation from peripheral blood, exosomes were extracted with exosomes kits, high-throughput detection of mRNA was performed with gene chips, and differentially expressed mRNAs were screened. Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed simultaneously. Furthermore, real-time polymerase chain reaction (qRT-PCR) was used to verify the expression levels of the screened differential mRNAs in the serum exosomes collected in Phase Ⅱ from 32 patients each in the ACI case and normal control groups. ResultsIn the Phase Ⅰ study, there were 248 differentially expressed mRNAs (fold change ≥ 2.0, P < 0.05) among five patients in the normal control group and four patients in the case group, of which the expression of 242 was upregulated and that of six was downregulated. The results of GO functional enrichment analysis mainly included behavior regulation, cell connection, and antioxidant activity. The results of KEGG pathway enrichment analysis mainly included ribosomes, proteasomes, oxytocin signaling pathways, and oxidative phosphorylation. After researching and screening based on relevant literature, it was found that among the genes with significant differential expression, H3F3B mRNA may be associated with and might play an important role in ACI. The qRT-PCR method was used to detect the H3F3B mRNA expression in serum exosomes of 32 patients each in the normal control and case groups in Phase Ⅱ; the expression was significantly higher in serum exosomes of the case group than in those of the normal control group (P < 0.001). H3F3B mRNA expression in serum exosomes of the case group positively correlated with age, the National Institutes of Health Stroke Scale (NIHSS) score, and the maximum infarct size (P < 0.05). ConclusionACI can lead to changes in the serum exosomes mRNA expression profile, which may be closely related to the occurrence, development, and prognosis of this condition. These findings will provide direction for research on the molecular mechanism, diagnostic markers, and therapeutic targets of ACI.

Reconstruction and analysis of the aberrant lncRNA-miRNA-mRNA network based on competitive endogenous RNA in adenoid cystic carcinoma of the salivary gland.

BackgroundThe aim of this work was to investigate the competing endogenous RNA (ceRNA) network in adenoid cystic carcinoma of the salivary gland (SACC).MethodsDifferentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) between cancer tissues and normal salivary gland (NSG) in ACC were identified using data from the Gene Expression Omnibus (GEO) database. Functional annotation and pathway enrichment analysis of DEmRNAs were performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The miRNAs that are targeted by lncRNAs were predicted using miRanda and PITA, while the target mRNAs of miRNAs were retrieved from miRanda, miRWalk, and TargetScan. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and then we constructed the lncRNA-miRNA-mRNA networks of ACC.ResultsDifferentially expressed RNAs were identified in SACC. Upon comparing cancer tissues and NSG tissues, 103 upregulated and 52 downregulated lncRNAs and 745 upregulated and 866 downregulated mRNAs were identified in GSE88804; in addition, 39 upregulated and 43 downregulated miRNAs were identified in GSE117275. GO enrichment analyses revealed that the most relevant GO terms were regulation of transcription DNA-templated, transcription DNA-templated, and cell division. KEGG pathway enrichment analysis showed that differentially expressed genes (DEGs) were mainly enriched in the cell cycle, pathways in cancer, PI3K-Akt signaling pathway, breast cancer, and microRNAs in cancer. The PPI network consisted of 27 upregulated and 54 downregulated mRNAs. By constructing ceRNA network, NONHSAT251752.1-hsa-miR-6817-5p-NOTCH1, NONHSAT251752.1-hsa-miR-204-5p/hsa-miR-138-5p-CDK6 regulatory axises were identified and all genes in the network were verified by qRT-PCR.ConclusionsThe present study constructed ceRNA networks in SACC and provided a novel perspective of the molecular mechanisms for SACC.

DMAG, a novel countermeasure for the treatment of thrombocytopenia

BackgroundThrombocytopenia is one of the most common hematological disease that can be life-threatening caused by bleeding complications. However, the treatment options for thrombocytopenia remain limited.MethodsIn this study, giemsa staining, phalloidin staining, immunofluorescence and flow cytometry were used to identify the effects of 3,3ʹ-di-O-methylellagic acid 4ʹ-glucoside (DMAG), a natural ellagic acid derived from Sanguisorba officinalis L. (SOL) on megakaryocyte differentiation in HEL cells. Then, thrombocytopenia mice model was constructed by X-ray irradiation to evaluate the therapeutic action of DMAG on thrombocytopenia. Furthermore, the effects of DMAG on platelet function were evaluated by tail bleeding time, platelet aggregation and platelet adhesion assays. Next, network pharmacology approaches were carried out to identify the targets of DMAG. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to elucidate the underling mechanism of DMAG against thrombocytopenia. Finally, molecular docking simulation, molecular dynamics simulation and western blot analysis were used to explore the relationship between DAMG with its targets.ResultsDMAG significantly promoted megakaryocyte differentiation of HEL cells. DMAG administration accelerated platelet recovery and megakaryopoiesis, shortened tail bleeding time, strengthened platelet aggregation and adhesion in thrombocytopenia mice. Network pharmacology revealed that ITGA2B, ITGB3, VWF, PLEK, TLR2, BCL2, BCL2L1 and TNF were the core targets of DMAG. GO and KEGG pathway enrichment analyses suggested that the core targets of DMAG were enriched in PI3K–Akt signaling pathway, hematopoietic cell lineage, ECM-receptor interaction and platelet activation. Molecular docking simulation and molecular dynamics simulation further indicated that ITGA2B, ITGB3, PLEK and TLR2 displayed strong binding ability with DMAG. Finally, western blot analysis evidenced that DMAG up-regulated the expression of ITGA2B, ITGB3, VWF, p-Akt and PLEK.ConclusionDMAG plays a critical role in promoting megakaryocytes differentiation and platelets production and might be a promising medicine for the treatment of thrombocytopenia.Graphical

Mechanism of quercetin therapeutic targets for Alzheimer disease and type 2 diabetes mellitus

Quercetin has demonstrated antioxidant, anti-inflammatory, hypoglycemic, and hypolipidemic activities, suggesting therapeutic potential against type 2 diabetes mellitus (T2DM) and Alzheimer’s disease (AD). In this study, potential molecular targets of quercetin were first identified using the Swiss Target Prediction platform and pathogenic targets of T2DM and AD were identified using online Mendelian inheritance in man (OMIM), DisGeNET, TTD, DrugBank, and GeneCards databases. The 95 targets shared among quercetin, T2DM, and AD were used to establish a protein–protein interaction (PPI) network, top 25 core genes, and protein functional modules using MCODE. Metascape was then used for gene ontology and kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. A protein functional module with best score was obtained from the PPI network using CytoHubba, and 6 high-probability quercetin targets (AKT1, JUN, MAPK, TNF, VEGFA, and EGFR) were confirmed by docking simulations. Molecular dynamics simulation was carried out according to the molecular docking results. KEGG pathway enrichment analysis suggested that the major shared mechanisms for T2DM and AD include “AGE-RAGE signaling pathway in diabetic complications,” “pathways in cancer,” and “MAPK signaling pathway” (the key pathway). We speculate that quercetin may have therapeutic applications in T2DM and AD by targeting MAPK signaling, providing a theoretical foundation for future clinical research.

Bioinformatics Analysis to Screen Key Targets of Curcumin against Colorectal Cancer and the Correlation with Tumor-Infiltrating Immune Cells

Purpose Curcumin is a potential drug for the treatment of colorectal cancer (CRC). Its mechanism of action has not been elucidated. This study aims to investigate the mechanism of action of curcumin in the treatment of CRC via bioinformatics methods such as network pharmacology and molecular docking. Methods The targets of curcumin and CRC were obtained from the public databases. The component-targets network of curcumin in the treatment of CRC was constructed by Cytoscape v3.7.2. Through protein-protein interaction (PPI), the Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG), important targets and signaling pathways related to CRC treatment were identified. Finally, the results were verified by molecular docking, and the correlation between the key targets and tumor-infiltrating immune cells (TICs) was analyzed. Results A total of 30 potential targets of curcumin for CRC treatment were collected. The GO function enrichment analysis showed 140 items, and the KEGG pathway enrichment analysis showed 61 signaling pathways related to the regulation of protein kinase activity, negative regulation of apoptosis process, cancer signaling pathway, and PI3K-Akt signaling pathway. The molecular docking results showed that curcumin could be combined with AKT1, EGFR, and STAT3 more stably, and AKT1 has the strongest binding to curcumin. Bioinformatics analysis discovered that the expression of core targets AKT1, EGFR, and STAT3 in CRC was related to TICs. Conclusion This study explored the targets and pathways of curcumin in the treatment of CRC. The core targets are AKT1, EGFR, and STAT3. The study indicated that curcumin has preventive and treatment effects on CRC through multitarget and multipathway, which laid the foundation for follow-up research.

The screening of potential biological markers of seizure onset zone in focal cortical dysplasia based on bioinformatics analysis

Three patients diagnosed with focal cortical dysplasia (FCD) in the First Hospital of Peking University from September to November 2020 were recruited in the study. Based on stereotactic electroencephalogram (SEEG) or electrocorticogram (ECoG) analysis to localize the seizure onset zone (SOZ), RNA sequencing (RNA-seq) analysis was performed for the SOZ and para-SOZ tissue obtained from surgery. The differentially expressed genes between SOZ and para-SOZ samples were analyzed by performing Go (Gene ontology) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis."Extracellular matrix"was significantly enriched, which included collagen synthesis genes (e.g., COL1A1)."Ether lipid metabolism"was enriched in the KEGG pathway enrichment analysis. These differences could be the potential biological markers for SOZ localization.

Significant correlation between HSPA4 and prognosis and immune regulation in hepatocellular carcinoma.

BackgroundHepatocellular carcinoma (HCC) is an inflammation-associated tumor involved in immune tolerance and evasion in the immune microenvironment. Heat shock proteins (HSPs) are involved in the occurrence, progression, and immune regulation of tumors. Therefore, HSPs have been considered potential therapeutic targets. Here, we aimed to elucidate the value of HSP family A (Hsp70) member 4 (HSPA4) in the diagnosis and predicting prognosis of HCC, and its relationship with immune cell infiltration, immune cell biomarkers, and immune checkpoints. Gene mutation, DNA methylation, and the pathway involved in HCC were also analyzed.MethodsThe gene expression omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to compare HSPA4 expression, and the results were confirmed by immunohistochemical staining of clinical samples. R package was used to analyze the correlation between HSPA4 and cancer stage, and to establish receiver operating characteristic (ROC) curve of diagnosis, time-dependent survival ROC curve, and a nomogram model. cBioPortal and MethSurv were used to identify genetic alterations and DNA methylation, and their effect on prognosis. The Tumor Immune Estimation Resource (TIMER) was used to analyze immune cell infiltration, immune cell biomarkers, and immune checkpoints. The STRING database was used to analyze protein–protein interaction network information. Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the functions of HSPA4 and its functional partner genes.ResultsOverexpression of HSPA4 was identified in 25 cancers. Overexpression of HSPA4 considerably correlated with cancer stage and alpha-fetoprotein (AFP) level in HCC. Patients with higher HSPA4 expression showed poorer prognosis. HSPA4 expression can accurately identify tumor from normal tissue (AUC = 0.957). The area under 1-, 3-, and 5-year survival ROCs were above 0.6. The HSPA4 genetic alteration rate was 1.3%. Among the 14 DNA methylation CpG sites, seven were related to the prognosis of HCC. HSPA4 was positively related to immune cell infiltration and immune checkpoints (PD-1 and CTLA-4) in HCC. The KEGG pathway enrichment analysis revealed HSPA4 enrichment in antigen processing and presentation together with HSPA8 and HSP90AA1. We verified the value of HSPA4 in the diagnosis and predicting prognosis of HCC. HSPA4 may not only participate in the occurrence and progression but also the immune regulation of HCC. Therefore, HSPA4 can be a potential diagnostic and prognostic biomarker and a therapeutic target for HCC.

Systematic Analysis and Identification of Dysregulated Panel lncRNAs Contributing to Poor Prognosis in Head-Neck Cancer.

Head and neck cancer (HNC) is one of the most prevalent cancers worldwide, accounting for approximately 5% of all cancers. While the underlying molecules and their pathogenetic mechanisms in HNC have yet to be well elucidated, recent studies have shown that dysregulation of lncRNAs may disrupt the homeostasis of various biological pathways. However, the understanding of lncRNAs in HNC is still limited by the lack of expression profiling. In the present study, we employed a systematic strategy to identify a panel of lncRNA associated with HNC. A cancer-related lncRNA profile PCR array was screened to explore potential molecules specific for HNC. A total of 55 lncRNAs were found to be dysregulated in HNC cells when compared to normal keratinocytes. Further analysis of the prognostic significance using The Cancer Genome Atlas (TCGA) database revealed 15 lncRNAs highly correlated with overall survival in HNC patients. Additionally, clinical sample expression analysis of the TCGA-HNSC cohort revealed 16 highly dysregulated lncRNAs in HNC, resulting in a combined 31-lncRNA signature panel that could predict prognosis. Validation of these molecules confirmed the considerable level of altered expressions in HNC cells, with XIST, HOXA11-AS, TSIX, MALAT1, WT1-AS, and IPW being the most prominently dysregulated. We further selected a molecule from our panel (XIST) to confirm the validity of these lncRNAs in the regulation of cancer aggressiveness. Gene ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses demonstrated that XIST participated in various cancer-related functions, including cell proliferation and metastasis. XIST silencing with the RNAi technique substantially reduced invasion and migration in several HNC cell lines. Thus, our study defined a 31-lncRNA panel as prognostic signatures in HNC. These perspective results provide a knowledge foundation for further application of these molecules in precision medicine.

Material Basis and Mechanism of Chansu Injection for COVID-19 Treatment Based on Network Pharmacology and Molecular Docking Technology.

Purpose The clinical efficacy of Chansu injection for COVID-19 treatment has been confirmed. Its mechanism of action remains unclear. We used network pharmacology and molecular docking technology to explore the potential material basis and mechanism of action of Chansu injection for COVID-19. Methods The main components of Chansu injection were determined using HPLC. The PharmMapper, SwissTargetPrediction, SEA, and TCMID databases were used to screen for the active ingredients and therapeutic targets of Chansu injection, while the OMIM and GeneCards Suite databases were used to search for COVID-19-related targets. The STRING database was used for protein-protein interaction (PPI) network construction and topological analysis, while DAVID was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the core targets. The main active compounds of Chansu injection were docked with 3CL protease, ACE2, RdRp, and spike protein. Results The three Chansu injection compounds were identified using HPLC. A total of 236 drug-related targets and 16,611 disease-related targets were identified, and 77 common targets were determined through mapping. The PPI mapping results revealed that 16 core targets were obtained through topological analysis and screening. Furthermore, GO and KEGG pathway enrichment analyses revealed that the PI3K and JAK-STAT signaling pathways are the major pathways. The molecular docking results suggest that the three Chansu injection components have high binding energies to the S protein. Conclusions The potential mechanism of Chansu injection for COVID-19 involves multiple targets and pathways, thereby providing a scientific basis for its clinical application and further research.

Mechanism of Pingyang Jiangya Formula in treating hypertension based on network pharmacology and in vivo study

ObjectiveThis study aimed to analyze the mechanism of action of the Pingyang Jiangya Formula (平阳降压方, PYJYF) in treating hypertension, based on network pharmacology, and to verify the subsequent predictions through animal experiments. MethodsThe active components and related target genes of PYJYF were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM), Encyclopedia of Traditional Chinese Medicine (ETCM), and DrugBank databases and available literature. The hypertension target genes were screened based on Therapeutic Target Database (TTD), GeneCards, Online Mendelian Inheritance in Man (OMIM), UniProt, and relevant literature. The component-disease-target network intersection target genes were inputted into the STRING database, and the key target genes were selected according to the degree algorithm. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to explore the multitarget mechanism of action and molecular regulatory network of PYJYF in the treatment of hypertension. To verify this prediction, we used PYJYF to intervene in spontaneously hypertensive rats (SHRs) and Wistar–Kyoto rats (WKY) as normal control, and the noninvasive tail artery manometry method was used to measure systolic blood pressure (SBP) in the rat tail before PYJYF intervention. After drug intervention, the SBP of each group rats were measured and compared every week. Enzyme-linked immunosorbent assay (ELISA) was used to test plasma renin, angiotensin II (Ang II), and aldosterone (Ald) levels, and hematoxylin-eosin (HE) staining was used to observe pathological damage to the renal vessels in each group of rats. Western blot and reverse transcription real-time quantitative PCR (RT-PCR) were used to detect the protein and mRNA expression levels of PI3K, AKT1, BAX, and Bcl-2, respectively. ResultsA total of 4 123 hypertension targets were obtained from related databases. From the TCMSP and chemical databases, 78 active components of PYJYF and the corresponding 401 drug targets were retrieved. Data analysis revealed that 208 drug targets directly interacted with the hypertension targets in PYJYF. The 10 targets most closely related to hypertension target proteins in PYJYF were directly retrieved from relevant databases. GO analysis revealed that 10 direct target proteins were involved in all aspects of the antihypertensive effects of PYJYF, as well as molecular biological processes, such as the regulation of blood pressure, renin-angiotensin-aldosterone system (RAAS), angiotensin-mediated ligand reactions, and biological stimulation of cardiomyocyte apoptosis. KEGG pathway enrichment analysis revealed that PYJYF directly affected 20 signaling pathways associated with hypertension. In animal experiments, PYJYF reduced the protein and mRNA levels of PI3K, Akt, and Bax and upregulated the expression of the protein and mRNA levels of Bcl-2, reduced plasma renin, Ang II, and Ald levels, improved the hyperactivity of RAAS, and significantly reduced SBP in SHRs. ConclusionPYJYF is effective for hypertension therapy that acts through multiple compounds and targets. The possible underlying molecular mechanism includes regulating the PI3K/Akt signaling pathway to suppress RAAS, increasing the ratio of Bcl-2/Bax proteins, and inhibiting apoptosis, thereby mediating the repair of renal and renal vascular damage caused by hypertension. These findings warrant further research for use in clinical settings.

Network Pharmacology Integrated with Molecular Docking Explores the Mechanisms of Naringin against Osteoporotic Fracture by Regulating Oxidative Stress.

Naringin (NG), as the most abundant component of Drynariae Rhizoma (Chinese name: Gusuibu), has been proved to be an antioxidant flavonoid on promoting osteoporotic fracture (OF) healing, but relevant research is scanty on the underlying mechanisms. We adopted target prediction, protein-protein interaction (PPI) analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and molecular docking to establish a system pharmacology database of NG against OF. Totally 105 targets of naringin were obtained, including 26 common targets with OF. A total of 415 entries were obtained through GO Biological Process enrichment analysis (P < 0.05), and 37 entries were obtained through KEGG pathway enrichment analysis with seven signaling pathways included (P < 0.05), which were primarily concerned with p53, IL-17, TNF, estrogen, and PPAR signaling pathways. According to the results of molecular docking, naringin is all bound in the active pockets of the core targets with 3–9 hydrogen bonds through some connections such as hydrophobic interactions, Pi-Pi stacked interactions, and salt bridge, demonstrating that naringin binds tightly to the core targets. In general, naringin may treat OF through multiple targets and multiple pathways via regulating oxidative stress, etc. Notably, it is first reported that NG may regulate osteoclast differentiation and oxidative stress through the expression of the core targets so as to treat OF.

DNA methylation and gene expression profiles to identify childhood atopic asthma associated genes

BackgroundAsthma is a chronic inflammatory disorder of the airways involving many different factors. This study aimed to screen for the critical genes using DNA methylation/CpGs and miRNAs involved in childhood atopic asthma.MethodsDNA methylation and gene expression data (Access Numbers GSE40732 and GSE40576) were downloaded from the Gene Expression Omnibus database. Each set contains 194 peripheral blood mononuclear cell (PBMC) samples of 97 children with atopic asthma and 97 control children. Differentially expressed genes (DEGs) with DNA methylation changes were identified. Pearson correlation analysis was used to select genes with an opposite direction of expression and differences in methylation levels, and then Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Protein–protein interaction network and miRNA–target gene regulatory networks were then constructed. Finally, important genes related to asthma were screened.ResultsA total of 130 critical DEGs with DNA methylation changes were screened from children with atopic asthma and compared with control samples from healthy children. GO and KEGG pathway enrichment analysis found that critical genes were primarily related to 24 GO terms and 10 KEGG pathways. In the miRNA–target gene regulatory networks, 9 KEGG pathways were identified. Analysis of the miRNA–target gene network noted an overlapping KEGG signaling pathway, hsa04060: cytokine-cytokine receptor interaction, in which the gene CCL2, directly related to asthma, was involved. This gene is targeted by eight asthma related miRNAs (hsa-miR-206, hsa-miR-19a, hsa-miR-9,hsa-miR-22, hsa-miR-33b, hsa-miR-122, hsa-miR-1, and hsa-miR-23b). The genes IL2RG and CCl4 were also involved in this pathway.ConclusionsThe present study provides a novel insight into the underlying molecular mechanism of childhood atopic asthma.

Bioinformatics analysis to screen DNA methylation-driven genes for prognosis of patients with bladder cancer.

BackgroundBladder cancer (BLCA) is the most prevalent tumor affecting the urinary system, and has contributed to a rise in morbidity and mortality rates. Herein, we sought to identify the methylation-driven genes (MDGs)of BLCA in an effort to develop prognostic biomarkers suitable for the individualized assessment of patients with this particular cancer.MethodsThe Cancer Genome Atlas (TCGA) dataset was distributed into training set (n=272) and testing set(n=117). The ConsensusClusterPluspackage was used to identify BLCA subtypes. The ChAMP package was used to analyze differential methylation probe (DMP) and differential methylation region (DMR). The differentially expressed genes (DEGs) were detected using DESeq2. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were utilized to identify the pathways enriched of DEGs. Correlation analysis between 5’-C-phosphate-G-3’s (CpGs) and DEGs was employed to identify the MDGs. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) was used to build the protein-protein interaction (PPI) network of MDGs. Screening for BLCA prognosis-related MDGs and clinical features was conducted via the Cox regression model. A prognosis-related nomogram was developed and validated for prediction of the BLCA patients’ survival.ResultsWe identified 2 BLCA clusters. Differential methylations at CpGs sites (dm-CpGs) were observed between cluster2 and cluster1, with 14,189 of them hypermethylated and 878 hypomethylated, predominantly in the CpG islands. In addition, a total 4,234 DEGs were identified between cluster2 and cluster1. The KEGG pathway and GO term enrichment analyses found that some DEGs were significantly enriched in multiple cancer-related pathways. A total of 33 MDGs were detected from correlation analysis between CpGs and DEGs. We selected BLCA-specific prognostic DMGs signatures for risk model development. The nomogram comprised a risk model to predict survival for BLCA patients. The efficiency of the prognostic prediction model was validated in the training and testing set.ConclusionsThis study discovered differential methylation patterns and MDGs in BLCA patients, which provided a bioinformatics basis for guiding BLCA early diagnosis and prognosis analyses.

Uncovering the active components, prospective targets, and molecular mechanism of Baihe Zhimu decoction for treating depression using network pharmacology-based analysis

Ethnopharmacological relevanceBaihe Zhimu decoction (BZD) is a classical traditional Chinese medicinal herbal formula. It consists of two herbal medicines, Rhizoma Anemarrhenae (Zhimu), the rhizomes of Anemarrhena asphodeloides Bge. (Liliaceae), and Bulbus Lilii (Baihe), the bulbs of Lilium brownii var. Viridulum Baker (Liliaceae). BZD has been widely used in China to treat depression and verified to be effective without evident side effects. Aim of the studyThe aim of this study was to elucidate the active components, potential targets, and molecular mechanism of Baihe Zhimu decoction for treating depression. Materials and methodsIn this research, a chronic unpredictable mild stress (CUMS) mice was first established to evaluate the pharmacological effects of BZD for treating depression. A component database was then constructed for BZD. High-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS) technique was used to identify the components in BZD and blood-absorbed components. Further screening and validation of protein targets were performed by molecule docking. The component-target binding affinity was validated by surface plasmon resonance analysis (SPR) assay. The related pathways were predicted by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Relative proteins in the predicted pathways were finally assessed by Western blot. ResultsThe pharmacology evaluation experiment demonstrated that BZD could improve depressive-like behavior, inhibit the hippocampal secretion of pro-inflammatory cytokines and reduce neuronal apoptosis in CUMS mice model. A component database containing 163 components and a target database covering 1286 proteins were constructed. HPLC-QTOF-MS assay identified twenty-six components from BZD and ten components absorbed into rat plasma after an intragastric treatment with BZD. Next, 56 underlying targets were screened out by a virtual high-throughput screening approach. Twenty-seven of them were further screened out and confirmed by molecular docking. Afterward, a component-target network was established, and the component-protein binding affinities were validated by SPR assays. By KEGG pathway enrichment analysis, two signaling pathways PI3K/Akt and MAPK were predicted as the potential signaling cascades. Finally, Western blot showed that BZD dramatically reversed the suppression of PI3K/Akt/GSK-3β pathway and the activation of MAPK pathway in CUMS mice model. ConclusionsBZD demonstrated a substantial pharmacological effect on CUMS mice model. Network pharmacology-based analysis predicted that ten blood-absorbed components can act on 27 target proteins. KEGG and Western blotting analysis suggested that BZD could exert antidepressant effects by regulating the PI3K/Akt and MAPK signaling pathways.

Identification of latent biomarkers in connection with progression and prognosis in oral cancer by comprehensive bioinformatics analysis

BackgroundOral cancer (OC) is a common and dangerous malignant tumor with a low survival rate. However, the micro level mechanism has not been explained in detail.MethodsGene and miRNA expression micro array data were extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and miRNAs (DE miRNAs) were identified by R software. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of genes and genomes (KEGG) pathway analysis were used to assess the potential molecular mechanisms of DEGs. Cytoscape software was utilized to construct protein–protein interaction (PPI) network and miRNA-gene network. Central genes were screened out with the participation of gene degree, molecular complex detection (MCODE) plugin, and miRNA-gene network. Then, the identified genes were checked by The Cancer Genome Atlas (TCGA) gene expression profile, Kaplan-Meier data, Oncomine, and the Human Protein Atlas database. Receiver operating characteristic (ROC) curve was drawn to predict the diagnostic efficiency of crucial gene level in normal and tumor tissues. Univariate and multivariate Cox regression were used to analyze the effect of dominant genes and clinical characteristics on the overall survival rate of OC patients.ResultsGene expression data of gene expression profiling chip(GSE9844, GSE30784, and GSE74530) were obtained from GEO database, including 199 tumor and 63 non-tumor samples. We identified 298 gene mutations, including 200 upregulated and 98 downregulated genes. GO functional annotation analysis showed that DEGs were enriched in extracellular structure and extracellular matrix containing collagen. In addition, KEGG pathway enrichment analysis demonstrated that the DEGs were significantly enriched in IL-17 signaling pathway and PI3K-Akt signaling pathway. Then, we detected three most relevant modules in PPI network. Central genes (CXCL8, DDX60, EIF2AK2, GBP1, IFI44, IFI44L, IFIT1, IL6, MMP9,CXCL1, CCL20, RSAD2, and RTP4) were screened out with the participation of MCODE plugin, gene degree, and miRNA-gene network. TCGA gene expression profile and Kaplan-Meier analysis showed that high expression of CXCL8, DDX60, IL6, and RTP4 was associated with poor prognosis in OC patients, while patients with high expression of IFI44L and RSAD2 had a better prognosis. The elevated expression of CXCL8, DDX60, IFI44L, RSAD2, and RTP44 in OC was verified by using Oncomine database. ROC curve showed that the mRNA levels of these five genes had a helpful diagnostic effect on tumor tissue. The Human Protein Atlas database showed that the protein expressions of DDX60, IFI44L, RSAD2, and RTP44 in tumor tissues were higher than those in normal tissues. Finally, univariate and multivariate Cox regression showed that DDX60, IFI44L, RSAD2, and RTP44 were independent prognostic indicators of OC.ConclusionThis study revealed the potential biomarkers and relevant pathways of OC from publicly available GEO database, and provided a theoretical basis for elucidating the diagnosis, treatment, and prognosis of OC.

Integrated Bioinformatics Analysis to Identify Abnormal Methylated Differentially Expressed Genes for Predicting Prognosis of Human Colon Cancer.

ObjectiveTo identify the value of key differentially expressed genes (DEGs) regulated by differentially methylated regions (DMRs) in predicting the prognosis of human colon cancer.Materials and MethodsRNA sequencing data and DNA methylation data of 455 colon adenocarcinoma (COAD) cases and 41 normal controls were downloaded from The Cancer Genome Atlas (TCGA). Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by the DAVID database. To identify the hub genes regulated by methylation, univariate Cox and multivariate Cox regression analyses were carried out. A nomogram based on the risk score was built to identify the power of the hub genes to predict prognosis in patients with colon cancer.ResultsA total of 133 DEGs regulated by DMRs were identified through analyzing RNA sequencing data and DNA methylation data from TCGA. GO functional enrichment and KEGG pathway enrichment analysis showed the genes involved in the initiation and progression of colon cancer. Univariate Cox regression analysis and multivariate Cox regression analysis focused on the seven hub genes (CDH4, CR2, KRT85, LGI4, NPAS4, RUVBL1 and SP140) associated with overall survival, the expression of which negatively correlated with their methylation level. The risk score and nomogram model showed that the hub genes served as potential biomarkers for the prognosis prediction of patients with colon cancer.ConclusionOur findings suggest that the DEGs regulated by DMRs are involved in the carcinogenesis and development of colon cancer, and the aberrantly methylated DEGs associated with overall survival of patients may be potential diagnostic and therapeutic targets for colon cancer.

Identification of significant potential signaling pathways and differentially expressed proteins in patients with wheat intolerance based on quantitative proteomics

Wheat intolerance has various systemic manifestations that can affect people's quality of life, and few studies have focused on the mechanism of wheat intolerance and the signaling pathways involved in wheat intolerance have not been fully identified. We compared the protein profiles of patients with wheat intolerance with those of healthy controls using LASSO (least absolute shrinkage and selection operator) and PLS (partial least squares regression) to obtain DEPs (differentially expressed proteins) for GO (Gene Ontology) analysis, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis, and PPI (protein-protein interaction) network analysis. Internal validation and external validation were conducted for target proteomics testing. The correlation between differently expressed protein and the wheat-specific IgG antibody concentration was analyzed. Then ROC curve (receiver operating characteristic curve) was generated to validate the differentially expressed proteins. We identified 33 DEPs as significant candidate proteins of wheat intolerance. These proteins were mainly enriched in complement and coagulation cascade pathways, immune activation, and immune response-related pathways. After internal and external target proteomics validation, CFHR3 (complement factor H-related protein 3) was identified as a key protein that may have an important role in wheat intolerance. We found CFHR3 protein expression abundance and the wheat-specific IgG antibody concentration were significantly negatively correlated (P = 0.035; Spearman correlation coefficient r = −0.565). The AUC (median area under the ROC curve) of CFHR3 is 0.857 in external verification data. This study provides insights into wheat intolerance that can be used to further explore the pathogenesis of this condition. SignificanceProteomics has performed important potential in food allergy research and is conducive to improving our comprehension on molecular mechanisms of food allergy. The present study identified significant signaling pathways and differentially expressed proteins in patients with wheat intolerance by means of bioinformatics from the viewpoint of mass spectrometry-based proteomics, which provided insights into further research on the pathogenesis and timely diagnosis of wheat intolerance.