scholarly journals Network pharmacology-based elucidation of the molecular mechanism underlying the anti-migraine effect of Asari Radix et Rhizoma

2021 ◽  
Vol 18 (10) ◽  
pp. 2067-2074
Author(s):  
Yun-Bin Jiang ◽  
Mei Zhong ◽  
Ting Huang ◽  
Zhong-Hua Dai ◽  
Xing-Bao Tao ◽  
...  
Keyword(s):  
Molecular Mechanism ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Scientific Basis ◽  
Network Pharmacology ◽  
Overlapping Genes ◽  
Hub Genes ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Tnf Gene

Purpose: To determine the molecular mechanism involved in the anti-migraine effect of Asari Radix et Rhizoma (ARR) using network pharmacology. Methods: The compounds present in ARR were identified through information retrieval from literature and public databases, and were screened based on absorption, distribution, metabolism, excretion and toxicity. Target genes related to the selected compounds and migraine were identified or predicted from public databases. Hub genes in ARR against migraine were identified through analysis of interactions in overlapping genes between compounds and migraine target genes, based on STRING database. Gene enrichment analysis of overlapping genes was performed using Database for Annotation, Visualization and Integrated Discovery. Results: A total of 138 compounds were selected as potential bioactive compounds in ARR. Target genes related to the selected compounds (611 genes) and migraine (278 genes) were obtained, including 71 overlapping genes. The hub genes in the anti-migraine effect of ARR were BDNF, IL6, COMT, APP and TNF. Gene enrichment analysis showed the top 10 biological processes or pathways involved in the mechanism of anti-migraine action of ARR. The tissue source of the overlapping genes was not limited to the brain. The results from gene enrichment analysis revealed that the effect of ARR on migraine was holistic, which is characteristic of traditional Chinese medicines. Conclusion: Network pharmacology has been used to decipher the molecular mechanism involved in the action of ARR against migraine. The results provide a scientific basis for the clinical effect of ARR on migraine.

Discussing the Mechanism of Dahuang Huanglian Xiexin Decoction in the Treatment of Type 2 Diabetes Mellitus via Network Pharmacology and Molecular Docking

2021 ◽  
Author(s):  
Xi Cen ◽  
Yan Wang ◽  
LeiLei Zhang ◽  
XiaoXiao Xue ◽  
Yan Wang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Molecular Docking ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Overlapping Genes ◽  
Potential Mechanism ◽  
Active Components ◽  
The Core ◽  
Core Genes

Abstract BackgroundType 2 diabetes mellitus (T2DM) is regarded as Pi Dan disease in traditional Chinese medicine (TCM). Dahuang Huanglian Xiexin Decoction (DHXD), a classical TCM formula, has been used for treating Pi Dan disease in clinic, its pharmacological mechanism has not been elucidated. MethodsThis study used network pharmacological analysis and molecular docking approach to explore the mechanism of DHXD on T2DM. Firstly, the compounds in DHXD were obtained from TCMSP and TCMID databases, the potential targets were determined based on TCMSP and UniProt databases. Next, Genecards, Digenet and UniProt databases were used to identify the targets of T2DM. Then, the protein-protein interaction (PPI) network was established with overlapping genes of T2DM and compounds, and the core targets in the network were identified and analyzed. Then, the David database was used for GO and KEGG enrichment analysis. Finally, the target genes were selected and the molecular docking was completed by Autodock software to observe the binding level of active components with target genes.ResultsA total of 397 related components and 128 overlapping genes were identified. After enrichment analysis, it was found that HIF-1, TNF, IL-17 and other signaling pathways, as well as DNA transcription, gene expression, apoptosis and other cellular biological processes had the strongest correlation with the treatment of T2DM by DHXD, and most of them occurred in the extracellular space, plasma membrane and other places, which were related to enzyme binding and protein binding. In addition, 42 core genes of DHXD, such as VEGFA, TP53 and MAPK1, were considered as potential therapeutic targets, indicating the potential mechanism of DHXD on T2DM. Finally, the results of molecular docking showed that HIF-1 pathway had strong correlation with the target genes INSR and GLUT4, quercetin and berberine had the strongest binding power with them respectively.ConclusionThis study summarized the main components of DHXD in the treatment of T2DM, identified the core genes and pathways, and systematically analyzed the interaction of related targets, trying to lay the foundation for clarifying the potential mechanism of DHXD on T2DM, so as to carry out further research in the future.

scholarly journals A Network Pharmacology Approach to Uncover the Multiple Mechanisms of Hedyotis diffusa Willd. on Colorectal Cancer

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xinkui Liu ◽  
Jiarui Wu ◽  
Dan Zhang ◽  
Kaihuan Wang ◽  
Xiaojiao Duan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Network Analysis ◽  
Chinese Herb ◽  
Endocrine System ◽  
Target Prediction ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Hedyotis Diffusa

Background. As one of the most frequently diagnosed cancer diseases globally, colorectal cancer (CRC) remains an important cause of cancer-related death. Although the traditional Chinese herb Hedyotis diffusa Willd. (HDW) has been proven to be effective for treating CRC in clinical practice, its definite mechanisms have not been completely deciphered. Objective. The aim of our research is to systematically explore the multiple mechanisms of HDW on CRC. Methods. This study adopted the network pharmacology approach, which was mainly composed of active component gathering, target prediction, CRC gene collection, network analysis, and gene enrichment analysis. Results. The network analysis showed that 10 targets might be the therapeutic targets of HDW on CRC, namely, HRAS, PIK3CA, KRAS, TP53, APC, BRAF, GSK3B, CDK2, AKT1, and RAF1. The gene enrichment analysis implied that HDW probably benefits patients with CRC by modulating pathways related to cancers, infectious diseases, endocrine system, immune system, nervous system, signal transduction, cellular community, and cell motility. Conclusions. This study partially verified and predicted the pharmacological and molecular mechanism of HDW against CRC from a holistic perspective, which will also lay a foundation for the further experimental research and clinical rational application of HDW.

scholarly journals A Comprehensive Network Pharmacology-Based Strategy to Investigate Multiple Mechanisms of HeChan Tablet on Lung Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Zhenjie Zhuang ◽  
Qianying Chen ◽  
Cihui Huang ◽  
Junmao Wen ◽  
Haifu Huang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Therapeutic Target ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Network Pharmacology ◽  
Hub Genes ◽  
Lung Cancer Patients ◽  
Ppi Networks

Background. HeChan tablet (HCT) is a traditional Chinese medicine preparation extensively prescribed to treat lung cancer in China. However, the pharmacological mechanisms of HCT on lung cancer remain to be elucidated. Methods. A comprehensive network pharmacology-based strategy was conducted to explore underlying mechanisms of HCT on lung cancer. Putative targets and compounds of HCT were retrieved from TCMSP and BATMAN-TCM databases; related genes of lung cancer were retrieved from OMIM and DisGeNET databases; known therapeutic target genes of lung cancer were retrieved from TTD and DrugBank databases; PPI networks among target genes were constructed to filter hub genes by STRING. Furthermore, the pathway and GO enrichment analysis of hub genes was performed by clusterProfiler, and the clinical significance of hub genes was identified by The Cancer Genome Atlas. Result. A total of 206 compounds and 2,433 target genes of HCT were obtained. 5,317 related genes of lung cancer and 77 known therapeutic target genes of lung cancer were identified. 507 unique target genes were identified among HCT-related genes of lung cancer and 34 unique target genes were identified among HCT-known therapeutic target genes of lung cancer. By PPI networks, 11 target genes AKT1, TP53, MAPK8, JUN, EGFR, TNF, INS, IL-6, MYC, VEGFA, and MAPK1 were identified as major hub genes. IL-6, JUN, EGFR, and MYC were shown to associate with the survival of lung cancer patients. Five compounds of HCT， quercetin, luteolin, kaempferol, beta-sitosterol, and baicalein were recognized as key compounds of HCT on lung cancer. The gene enrichment analysis implied that HCT probably benefitted patients with lung cancer by modulating the MAPK and PI3K-Akt pathways. Conclusion. This study predicted pharmacological and molecular mechanisms of HCT against lung cancer and could pave the way for further experimental research and clinical application of HCT.

scholarly journals Joint analysis of lncRNA m6A methylome and lncRNA/mRNA expression profiles in gastric cancer

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhi Lv ◽  
Liping Sun ◽  
Qian Xu ◽  
Chengzhong Xing ◽  
Yuan Yuan
Keyword(s):  
Gastric Cancer ◽  
Mrna Expression ◽  
Expression Analysis ◽  
Target Genes ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Joint Analysis ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment

Abstract Background N6-methyladenosine (m6A) modification might be closely associated with the genesis and development of gastric cancer (GC). Currently, the evidence established by high-throughput assay for GC-related m6A patterns based on long non-coding RNAs (lncRNAs) remains limited. Here, a joint analysis of lncRNA m6A methylome and lncRNA/mRNA expression profiles in GC was performed to explore the regulatory roles of m6A modification in lncRNAs. Methods Three subjects with primary GC were enrolled in our study and paired sample was randomly selected from GC tissue and adjacent normal tissue for each case. Methylated RNA Immunoprecipitation NextGeneration Sequencing (MeRIP-Seq) and Microarray Gene Expression Profiling was subsequently performed. Then co-expression analysis and gene enrichment analysis were successively conducted. Results After data analysis, we identified 191 differentially m6A-methylated lncRNAs, 240 differentially expressed lncRNAs and 229 differentially expressed mRNAs in GC. Furthermore, four differentially m6A-methylated and expressed lncRNAs (dme-lncRNAs) were discovered including RASAL2-AS1, LINC00910, SNHG7 and LINC01105. Their potential target genes were explored by co-expression analysis. And gene enrichment analysis suggested that they might influence the cellular processes and biological behaviors involved in mitosis and cell cycle. The potential impacts of these targets on GC cells were further validated by CCLE database and literature review. Conclusions Four novel dme-lncRNAs were identified in GC, which might exert regulatory roles on GC cell proliferation. The present study would provide clues for the lncRNA m6A methylation-based research on GC epigenetic etiology and pathogenesis.

Uncovering Active Ingredients and Mechanisms of Spica Prunellae in the Treatment of Colon Adenocarcinoma: A Study Based on Network Pharmacology and Bioinformatics

2020 ◽  
Vol 23 ◽  
Author(s):  
Yan Lei ◽  
Hao Yuan ◽  
Liyue Gai ◽  
Xuelian Wu ◽  
Zhixiao Luo
Keyword(s):  
Target Genes ◽  
Experimental Studies ◽  
Colon Adenocarcinoma ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Ppi Network ◽  
Active Ingredients ◽  
Hub Genes

Background: As a well-known herb used in the treatment of colon adenocarcinoma (COAD), Spica Prunellae (SP) shows favorable clinical effect and safety in China for many years, but its active ingredients and therapeutic mechanisms against COAD remain poorly understood. Therefore, this study aims to uncover active ingredients and mechanism of SP in the treatment of COAD using a combined approach of network pharmacology and bioinformatics. Methods: A comprehensive approach mainly comprised of target prediction, network construction, pathway and functional enrichment analysis, and hub genes verification was adopted in the current study. Results: We collected 102 compounds-related genes and 3549 differently expressed genes (DEGs) following treatment with SP, and 64 disease-drug target genes between them were recognized. In addition, a total of 25 active ingredients in SP were identified.Pathway and functional enrichment analyses suggested that the mechanisms of SP against COAD might be to induce apoptosis of colon cancer cells by regulating PI3K-Akt and TNF signaling pathway. Recognition of hub genes and core functional modules was performed by constructing protein-protein interaction (PPI) network, from whichTP53, MYC, MAPK8 and CASP3 were found as the hub target genes that might play an important part in therapy for COAD. Subsequently we further compared differential expression level and assessed prognostic value of these four hub genes. These result of verification suggested that SP exerted therapeutic effects against COAD via a PPI network involving TP53, MYC, MAPK8 and CASP3. Conclusion: In this study, active ingredients and mechanism of SPin the treatment of COAD were systematically dis-cussed, which providedthe foundation for further experimental studies and mightact to promote its appropriate clinical application.

scholarly journals Co-expression Network Analysis Reveals Novel Genes Underlying Alzheimer’s Disease Pathogenesis

2020 ◽  
Vol 12 ◽  
Author(s):  
Rui-ting Hu ◽  
Qian Yu ◽  
Shao-dan Zhou ◽  
Yi-xin Yin ◽  
Rui-guang Hu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Novel Genes ◽  
Independent Dataset ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Functional Pathways ◽  
Independent Cohort

Background: The pathogenesis of Alzheimer’s disease (AD) remains to be elucidated. This study aimed to identify the hub genes in AD pathogenesis and determine their functions and pathways.Methods: A co-expression network for an AD gene dataset with 401 samples was constructed, and the AD status-related genes were screened. The hub genes of the network were identified and validated by an independent cohort. The functional pathways of hub genes were analyzed.Results: The co-expression network revealed a module that related to the AD status, and 101 status-related genes were screened from the trait-related module. Gene enrichment analysis indicated that these status-related genes are involved in synaptic processes and pathways. Four hub genes (ENO2, ELAVL4, SNAP91, and NEFM) were identified from the module, and these hub genes all participated in AD-related pathways, but the associations of each gene with clinical features were variable. An independent dataset confirmed the different expression of hub genes between AD and controls.Conclusions: Four novel genes associated with AD pathogenesis were identified and validated, which provided novel therapeutic targets for AD.

scholarly journals SARS-CoV-2 intervened by NSAIDs: A network pharmacology approach to decipher signaling pathway and interactive genes

2020 ◽  
Author(s):  
Ki Kwang Oh ◽  
Md. Adnan ◽  
Dong Ha Cho
Keyword(s):  
Signaling Pathway ◽  
Binding Affinity ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Venn Diagram ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Ras Signaling ◽  
Overlapping Genes ◽  
Anti Inflammatory

Abstract Background: Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) showed promising clinical efficacy toward COVID-19 patients as painkillers and anti-inflammatory agents. However, the prospective anti-COVID-19 mechanisms of NSAIDs are not evidently exposed. Therefore, we intended to decipher the most potent NSAIDs candidate(s) and its novel mechanism(s) against COVID-19 by network pharmacology.Method: FDA (U.S. Food & Drug Administration) approved twenty NSAIDs were used for this study. Genes related to selected NSAIDs and COVID-19 related genes were identified by the Similarity Ensemble Approach, Swiss Target Prediction, and PubChem databases. Venn diagram identified overlapping genes between NSAIDs and COVID-19 related genes. The interactive networking between NSAIDs and overlapping genes was analyzed by STRING. RStudio plotted the bubble chart of KEGG pathway enrichment analysis of overlapping genes. Finally, the binding affinity of NSAIDs against target genes was determined through molecular docking analysis.Results: Geneset enrichment analysis exhibited 26 signaling pathways against COVID-19. Inhibition of proinflammatory stimuli of tissues and/or cells by inactivating RAS signaling pathway was identified as the key anti-COVID-19 mechanism of NSAIDs. Besides, MAPK8, MAPK10, and BAD genes were explored as the associated genes of the RAS. Among twenty NSAIDs, 6MNA, rofecoxib, and indomethacin revealed promising binding affinity with the highest docking score against three identified genes, respectively.Conclusions: Overall, our proposed three NSAIDs (6MNA, rofecoxib, and indomethacin) might block the RAS by inactivating its associated genes, thus may alleviate excessive inflammation induced by SARS-CoV-2.

scholarly journals ATRT-03. IDENTIFICATION OF microRNA-BASED PROGNOSTIC BIOMARKERS AND CANDIDATE THERAPEUTIC AGENTS FOR ATYPICAL TERATOID/RHABDOID TUMOR

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii275-iii276
Author(s):  
Yang Zhang ◽  
Jianguo Xu
Keyword(s):  
Target Genes ◽  
Kinase Inhibitor ◽  
Therapeutic Agents ◽  
Rhabdoid Tumor ◽  
Janus Kinase ◽  
Prognostic Biomarkers ◽  
Atypical Teratoid Rhabdoid Tumor ◽  
Overlapping Genes ◽  
Hub Genes ◽  
Atypical Teratoid

Abstract BACKGROUND MicroRNA (miRNA) has been found to be involved in development of many malignant pediatric brain tumors, including atypical teratoid/rhabdoid tumor (AT/RT) that is highly aggressive and carries a dismal prognosis. The current study investigated the potential value of miRNAs and pivotal genes associated with AT/RT using bioinformatics analysis, aiming to identify new prognostic biomarkers and candidate drugs for AT/RT patients. METHODS Differentially expressed miRNAs (DEMs) and genes (DEGs) between AT/RT and normal control samples were obtained from GEO database. The target genes of DEMs were predicted via TargetScanHuman7.2 and miRDB, and then intersected with DEGs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of overlapping genes were conducted, followed by construction of protein-protein interaction network. Hub genes were determined by Cytoscape software, and their prognostic values were evaluated using Kaplan-Meier analysis. Connectivity Map database was used to identify latent therapeutic agents. RESULTS A total of 11 DEMs (hsa-miR-1224-5p, hsa-miR-128-3p, hsa-miR-17-5p, hsa-miR-18b-5p, hsa-miR-29c-5p, hsa-miR-329-3p, hsa-miR-379-5p, hsa-miR-433-3p, hsa-miR-488-5p, hsa-miR-656-3p and hsa-miR-885-5p) were screened. By intersecting 3275 predicted target genes and 925 DEGs, we finally identified 226 overlapping genes that were enriched in pathways in cancer and MAPK signaling pathway. Four hub genes (GRIA2, NRXN1, SLC6A1 and SYT1) were significantly associated with the overall survival of AT/RT patients. Candidate drugs included histone deacetylase inhibitor (givinostat), DNA synthesis inhibitor (floxuridine), cyclin-dependent kinase inhibitor (purvalanol) and janus kinase inhibitor (lestaurtinib). CONCLUSION In summary, this study systematically analyzed AT/RT-related miRNAs and pivotal genes to provide novel prognostic biomarkers and potential therapeutic agents.

scholarly journals Studies on the Mechanism of Gegen Qinlian Decoction in Treating Diabetes Mellitus Based on Network Pharmacology

2021 ◽  
Vol 16 (1) ◽  
pp. 1934578X2098213
Author(s):  
Xiaodong Deng ◽  
Yuhua Liang ◽  
Jianmei Hu ◽  
Yuhui Yang
Keyword(s):  
Diabetes Mellitus ◽  
Molecular Docking ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Network Pharmacology ◽  
Hub Genes ◽  
Topological Parameters

Diabetes mellitus (DM) is a chronic disease that is very common and seriously threatens patient health. Gegen Qinlian decoction (GQD) has long been applied clinically, but its mechanism in pharmacology has not been extensively and systematically studied. A GQD protein interaction network and diabetes protein interaction network were constructed based on the methods of system biology. Functional module analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Ontology (GO) enrichment analysis were carried out on the 2 networks. The hub nodes were filtered by comparative analysis. The topological parameters, interactions, and biological functions of the 2 networks were analyzed in multiple ways. By applying GEO-based external datasets to verify the results of our analysis that the Gene Set Enrichment Analysis (GSEA) displayed metabolic pathways in which hub genes played roles in regulating different expression states. Molecular docking is used to verify the effective components that can be combined with hub nodes. By comparing the 2 networks, 24 hub targets were filtered. There were 7 complex relationships between the networks. The results showed 4 topological parameters of the 24 selected hub targets that were much higher than the median values, suggesting that these hub targets show specific involvement in the network. The hub genes were verified in the GEO database, and these genes were closely related to the biological processes involved in glucose metabolism. Molecular docking results showed that 5,7,2', 6'-tetrahydroxyflavone, magnograndiolide, gancaonin I, isoglycyrol, gancaonin A, worenine, and glyzaglabrin produced the strongest binding effect with 10 hub nodes. This compound–target mode of interaction may be the main mechanism of action of GQD. This study reflected the synergistic characteristics of multiple targets and multiple pathways of traditional Chinese medicine and discussed the mechanism of GQD in the treatment of DM at the molecular pharmacological level.

scholarly journals HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhixin Wu ◽  
Yinxian Wen ◽  
Guanlan Fan ◽  
Hangyuan He ◽  
Siqi Zhou ◽  
...  
Keyword(s):  
Femoral Head ◽  
Roc Curve ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Overlapping Genes ◽  
Hub Genes ◽  
Diagnostic Biomarkers ◽  
Key Genes

Abstract Background Steroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH. Methods The GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve. Results Nine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14. Conclusions Our study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

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