scholarly journals Systematic Analysis of Endometrial Cancer-Associated Hub Proteins Based on Text Mining

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Huiqiao Gao ◽  
Zhenyu Zhang
Keyword(s):  
Endometrial Cancer ◽  
Targeted Therapy ◽  
Text Mining ◽  
Molecular Mechanisms ◽  
Ppi Network ◽  
Systematic Analysis ◽  
Ppi Networks ◽  
Related Proteins ◽  
Gene Data ◽  
Hub Proteins

Objective. The aim of this study was to systematically characterize the expression of endometrial cancer- (EC-) associated genes and to analysis the functions, pathways, and networks of EC-associated hub proteins.Methods. Gene data for EC were extracted from the PubMed (MEDLINE) database using text mining based on NLP. PPI networks and pathways were integrated and obtained from the KEGG and other databases. Proteins that interacted with at least 10 other proteins were identified as the hub proteins of the EC-related genes network.Results. A total of 489 genes were identified as EC-related withP<0.05, and 32 pathways were identified as significant (P<0.05,FDR<0.05). A network of EC-related proteins that included 271 interactions was constructed. The 17 proteins that interact with 10 or more other proteins (P<0.05,FDR<0.05) were identified as the hub proteins of this PPI network of EC-related genes. These 17 proteins are EGFR, MET, PDGFRB, CCND1, JUN, FGFR2, MYC, PIK3CA, PIK3R1, PIK3R2, KRAS, MAPK3, CTNNB1, RELA, JAK2, AKT1, and AKT2.Conclusion. Our data may help to reveal the molecular mechanisms of EC development and provide implications for targeted therapy for EC. However, corrections between certain proteins and EC continue to require additional exploration.

scholarly journals PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1969
Author(s):  
Dongmin Jung ◽  
Xijin Ge
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Bioconductor Package ◽  
Biological Functions ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available STRING database, we use network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

scholarly journals PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 1969 ◽  
Author(s):  
Dongmin Jung ◽  
Xijin Ge
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Bioconductor Package ◽  
Biological Functions ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available in the STRING database, we use a network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

scholarly journals Proteomic Profiling Reveals the Molecular Changes of Insomnia Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Guanying Wang ◽  
Xiaojuan Ren ◽  
Xingping Zhang ◽  
Qingquan Wang ◽  
Tao Liu ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Cholesterol Metabolism ◽  
Sleep Onset ◽  
Public Health Problem ◽  
Sleep Time ◽  
Control Group ◽  
Proteomic Profiling ◽  
Ppi Network ◽  
Metabolic Systems ◽  
Hub Proteins

Background. Insomnia is an economic burden and public health problem. This study is aimed at exploring potential biological pathways and protein networks for insomnia characterized by wakefulness after sleep. Method. Proteomics analysis was performed in the insomnia group with wakefulness and the control group. The differentially expressed proteins (DEPs) were enriched; then, hub proteins were identified by protein-protein interaction (PPI) network and verified by parallel reaction monitoring (PRM). Results. Compared with the control group, the sleep time and efficiency of insomnia patients were decreased, and awakening time and numbers after sleep onset were significantly increased ( P < 0.001 ). The results of proteomic sequencing found 68 DEPs in serum under 1.2-fold changed standard. These DEPs were significantly enriched in humoral immune response, complement and coagulation cascades, and cholesterol metabolism. Through the PPI network, we identified 10 proteins with the highest connectivity as hub proteins. Among them, the differential expression of 9 proteins was verified by PRM. Conclusion. We identified the hub proteins and molecular mechanisms of insomnia patients characterized by wakefulness after sleep. It provided potential molecular targets for the clinical diagnosis and treatment of these patients and indicated that the immune and metabolic systems may be closely related to insomnia characterized by wakefulness after sleep.

scholarly journals Network Pharmacology-Based Systematic Analysis of Molecular Mechanisms of Dingji Fumai Decoction for Ventricular Arrhythmia

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yi Liang ◽  
Bo Liang ◽  
Xin-Rui Wu ◽  
Wen Chen ◽  
Li-Zhi Zhao
Keyword(s):  
Molecular Docking ◽  
Ventricular Arrhythmia ◽  
Molecular Mechanisms ◽  
Mendelian Inheritance ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Systematic Analysis ◽  
Chemical Structures ◽  
Function Modules ◽  
Underlying Mechanisms

Background. Dingji Fumai Decoction (DFD), a traditional herbal mixture, has been widely used to ventricular arrhythmia (VA) in clinical practice in China. However, research on the bioactive components and underlying mechanisms of DFD in VA is still scarce. Methods. Components of DFD were collected from TCMSP, ETCM, and literature. The chemical structures of each component were obtained from PubChem. Next, SwissADME and SwissTargetPrediction were applied for compounds screening and targets prediction of DFD; meanwhile, targets of VA were collected from DrugBank and Online Mendelian Inheritance in Man (OMIM). Then, the H-C-T-D network and the protein-protein interaction (PPI) network were constructed based on the data obtained above. CytoNCA was utilized to filter hub genes and VarElect was used to analyze the relationship between genes and diseases. At last, Metascape was employed for systematic analysis on the potential targets of herbals against VA, and AutoDock was applied for molecular docking to verify the results. Results. A total of 434 components were collected, 168 of which were qualified, and there were 28 shared targets between DFD and VA. Three function modules of DFD were found from the PPI network. Further systematic analysis of shared genes and function modules explained the potential mechanism of DFD in the treatment of VA; molecular docking has verified the interactions. Conclusions. DFD could be employed for VA through mechanisms, including complex interactions between related components and targets, as predicted by network pharmacology and molecular docking. This work confirmed that DFD could apply to the treatment of VA and promoted the explanation of DFD for VA in the molecular mechanisms.

scholarly journals In silico Identification of 10 Hub Genes and an miRNA–mRNA Regulatory Network in Acute Kawasaki Disease

2021 ◽  
Vol 12 ◽  
Author(s):  
Jin Ma ◽  
Huan Gui ◽  
Yunjia Tang ◽  
Yueyue Ding ◽  
Guanghui Qian ◽  
...  
Keyword(s):  
Kawasaki Disease ◽  
Signaling Pathway ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Unknown Etiology ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Ppi Networks

Kawasaki disease (KD) causes acute systemic vasculitis and has unknown etiology. Since the acute stage of KD is the most relevant, the aim of the present study was to identify hub genes in acute KD by bioinformatics analysis. We also aimed at constructing microRNA (miRNA)–messenger RNA (mRNA) regulatory networks associated with acute KD based on previously identified differentially expressed miRNAs (DE-miRNAs). DE-mRNAs in acute KD patients were screened using the mRNA expression profile data of GSE18606 from the Gene Expression Omnibus. The functional and pathway enrichment analysis of DE-mRNAs were performed with the DAVID database. Target genes of DE-miRNAs were predicted using the miRWalk database and their intersection with DE-mRNAs was obtained. From a protein–protein interaction (PPI) network established by the STRING database, Cytoscape software identified hub genes with the two topological analysis methods maximal clique centrality and Degree algorithm to construct a miRNA-hub gene network. A total of 1,063 DE-mRNAs were identified between acute KD and healthy individuals, 472 upregulated and 591 downregulated. The constructed PPI network with these DE-mRNAs identified 38 hub genes mostly enriched in pathways related to systemic lupus erythematosus, alcoholism, viral carcinogenesis, osteoclast differentiation, adipocytokine signaling pathway and tumor necrosis factor signaling pathway. Target genes were predicted for the up-regulated and down-regulated DE-miRNAs, 10,203, and 5,310, respectively. Subsequently, 355, and 130 overlapping target DE-mRNAs were obtained for upregulated and downregulated DE-miRNAs, respectively. PPI networks with these target DE-mRNAs produced 15 hub genes, six down-regulated and nine upregulated hub genes. Among these, ten genes (ATM, MDC1, CD59, CD177, TRPM2, FCAR, TSPAN14, LILRB2, SIRPA, and STAT3) were identified as hub genes in the PPI network of DE-mRNAs. Finally, we constructed the regulatory network of DE-miRNAs and hub genes, which suggested potential modulation of most hub genes by hsa-miR-4443 and hsa-miR-6510-5p. SP1 was predicted to potentially regulate most of DE-miRNAs. In conclusion, several hub genes are associated with acute KD. An miRNA–mRNA regulatory network potentially relevant for acute KD pathogenesis provides new insights into the underlying molecular mechanisms of acute KD. The latter may contribute to the diagnosis and treatment of acute KD.

scholarly journals Identification of Essential Proteins Based on Improved HITS Algorithm

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 177 ◽  
Author(s):  
Xiujuan Lei ◽  
Siguo Wang ◽  
Fang-Xiang Wu
Keyword(s):  
Molecular Mechanisms ◽  
Clustering Coefficient ◽  
New Drugs ◽  
Biological Information ◽  
Ppi Network ◽  
Go Annotation ◽  
Detection Techniques ◽  
Essential Proteins ◽  
Ppi Networks ◽  
Topological Characteristics

Essential proteins are critical to the development and survival of cells. Identifying and analyzing essential proteins is vital to understand the molecular mechanisms of living cells and design new drugs. With the development of high-throughput technologies, many protein–protein interaction (PPI) data are available, which facilitates the studies of essential proteins at the network level. Up to now, although various computational methods have been proposed, the prediction precision still needs to be improved. In this paper, we propose a novel method by applying Hyperlink-Induced Topic Search (HITS) on weighted PPI networks to detect essential proteins, named HSEP. First, an original undirected PPI network is transformed into a bidirectional PPI network. Then, both biological information and network topological characteristics are taken into account to weighted PPI networks. Pieces of biological information include gene expression data, Gene Ontology (GO) annotation and subcellular localization. The edge clustering coefficient is represented as network topological characteristics to measure the closeness of two connected nodes. We conducted experiments on two species, namely Saccharomyces cerevisiae and Drosophila melanogaster, and the experimental results show that HSEP outperformed some state-of-the-art essential proteins detection techniques.

scholarly journals PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1969 ◽  
Author(s):  
Dongmin Jung ◽  
Xijin Ge
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Bioconductor Package ◽  
Biological Functions ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available STRING database, we use network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

scholarly journals Identification Of Both Shared And Specific Potential Molecular Mechanisms Of Arvc And Dcm Based On A Genome-Wide Microarray Dataset

2020 ◽  
Author(s):  
Tang Zhang ◽  
Yao-Zong Guan ◽  
Hao Liu
Keyword(s):  
Molecular Mechanisms ◽  
Arrhythmogenic Right Ventricular Cardiomyopathy ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Go Annotation ◽  
Kegg Pathways ◽  
Ppi Networks ◽  
A Genome ◽  
Specific Potential

Abstract Background: The study aimed to detect the shared differentially expressed genes (DEGs) and specific DEGs of arrhythmogenic right ventricular cardiomyopathy (ARVC) and dilated cardiomyopathy (DCM) as well as their pathways.Methods: The GSE29819 dataset was examined for the DEGs of ARVC vs. non-failing transplant donor hearts (NF), DCM vs. NF, and ARVC vs. DCM based on 6 patients with ARVC, 7 patients with DCM, and 6 non-failing transplant donor hearts that were never actually transplanted. The shared DEGs and specific DEGs were screened out using a Venn diagram. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Gene Ontology (GO) annotation, and protein-protein interaction (PPI) of the DEGs were determined using online analytical tools. Then, the modules and hub genes were identified using Cytoscape software.Results: A total of 684 shared DEGs of ARVC vs. NF and DCM vs. NF, 1371 specific DEGs of ARVC vs. NF, and 1075 specific DEGs of DCM vs. NF were identified. The shared DEGs were enriched in 63 biological processes (BP), 11 molecular functions (MF), 10 cellular components (CC), and 25 KEGG pathways. The DEGs of ARVC vs. DCM were enriched in 71 BPs, 19 MFs, 14 CCs, and 26 KEGG pathways. A PPI network with 187 nodes, 700 edges, and 2 modules, and another PPI network with 575 nodes, 2834 edges, and 7 modules were constructed based on the shared and specific DEGs, respectively. The top ten hub genes CCR3, CCR5, CXCL2, CXCL10, CXCR4, FPR1, APLNR, PENK, BDKRB2, GRM8, and RPS8, RPS3A, RPS12, RPS14, RPS21, RPL14, RPL18A, RPL21, RPL31 were identified for the shared and specific PPI networks, respectively.Conclusions: Our findings may help further the understanding of both shared and specific potential molecular mechanisms of ARVC and DCM.

scholarly journals Network Pharmacology-based Systematic Analysis of Molecular Mechanisms of Dingji Fumai Decoction for Ventricular Arrhythmia

2020 ◽  
Author(s):  
Yi Liang ◽  
Bo Liang ◽  
Rui Xin Wu ◽  
Wen Chen ◽  
Li-zhi ZHAO
Keyword(s):  
Molecular Docking ◽  
Ventricular Arrhythmia ◽  
Molecular Mechanisms ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Systematic Analysis ◽  
Chemical Structures ◽  
Function Modules ◽  
Underlying Mechanisms ◽  
And Function

Abstract Background: Dingji Fumai decoction (DFD), a traditional herbal mixture, has been widely used to ventricular arrhythmia (VA) in clinical practice in China. However, research on the bioactive components and underlying mechanisms of DFD in VA is still scarce. Methods: Components of DFD were collected from TCMSP, ETCM, and literature. Then, the chemical structures of each component were obtained from PubChem. Next, SwissADME and SwissTargetPrediction were applied for compounds screening and targets prediction of DFD, meanwhile, targets of VA were collected from DrugBank and OMIM. Then, the H-C-T-D network as well as the PPI network were constructed based on the data obtained above. CytoNCA was utilized to filter hub genes and VarElect was used to analyze the relationship between genes and diseases. At last, Metascape was employed for systematic analysis on the potential targets of herbals against VA, and AutoDock was applied for molecular docking to verify the results.Results: A total of 434 components were collected, 168 of which were qualified, and there were 28 shared targets between DFD and VA. Three function modules of DFD were found from the PPI network. Further systematic analysis of shared genes and function modules explained the potential mechanism of DFD in the treatment of VA, molecular docking has verified the interactions. Conclusions: DFD could be employed for VA through mechanisms, including complex interactions between related components and targets, as predicted by network pharmacology and molecular docking. This work confirmed DFD could apply for the treatment of VA and promoted the explaining of DFD for VA in the molecular mechanisms.

scholarly journals Systematic analysis of molecular mechanisms for HCC metastasis via text mining approach

Oncotarget ◽  
2017 ◽  
Vol 8 (8) ◽  
pp. 13909-13916 ◽  
Author(s):  
Cheng Zhen ◽  
Caizhong Zhu ◽  
Haoyang Chen ◽  
Yiru Xiong ◽  
Junyuan Tan ◽  
...  
Keyword(s):  
Text Mining ◽  
Molecular Mechanisms ◽  
Systematic Analysis
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