scholarly journals Prediction of hub genes of Alzheimer’s disease using a protein interaction network and functional enrichment analysis

2020 ◽  
Vol 18 (4) ◽  
pp. e39
Author(s):  
Jia Jin Wee ◽  
Suresh Kumar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes

scholarly journals Identification of the Hub Genes in Alzheimer’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huiwen Gui ◽  
Qi Gong ◽  
Jun Jiang ◽  
Mei Liu ◽  
Huanyin Li
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Elderly ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Geo Database

Purpose. Alzheimer’s disease (AD) is considered to be the most common neurodegenerative disease and also one of the major fatal diseases affecting the elderly, thus bringing a huge burden to society. Therefore, identifying AD-related hub genes is extremely important for developing novel strategies against AD. Materials and Methods. Here, we extracted the gene expression profile GSE63061 from the National Center for Biotechnology Information (NCBI) GEO database. Once the unverified gene chip was removed, we standardized the microarray data after quality control. We utilized the Limma software package to screen the differentially expressed genes (DEGs). We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs. Subsequently, we constructed a protein-protein interaction (PPI) network using the STRING database. Result. We screened 2169 DEGs, comprising 1313 DEGs with upregulation and 856 DEGs with downregulation. Functional enrichment analysis showed that the response of immune, the degranulation of neutrophils, lysosome, and the differentiation of osteoclast were greatly enriched in DEGs with upregulation; peptide biosynthetic process, translation, ribosome, and oxidative phosphorylation were dramatically enriched in DEGs with downregulation. 379 nodes and 1149 PPI edges were demonstrated in the PPI network constructed by upregulated DEGs; 202 nodes and 1963 PPI edges were shown in the PPI network constructed by downregulated DEGs. Four hub genes, including GAPDH, RHOA, RPS29, and RPS27A, were identified to be the newly produced candidates involved in AD pathology. Conclusion. GAPDH, RHOA, RPS29, and RPS27A are expected to be key candidates for AD progression. The results of this study can provide comprehensive insight into understanding AD’s pathogenesis and potential new therapeutic targets.

scholarly journals Unearthing of Key Genes Driving the Pathogenesis of Alzheimer’s Disease via Bioinformatics

2021 ◽  
Vol 12 ◽  
Author(s):  
Xingxing Zhao ◽  
Hongmei Yao ◽  
Xinyi Li
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Temporal Cortex ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Machine Learning Algorithms ◽  
Functional Enrichment ◽  
Control Group ◽  
Hub Genes ◽  
Expression Levels

Alzheimer’s disease (AD) is a neurodegenerative disease with unelucidated molecular pathogenesis. Herein, we aimed to identify potential hub genes governing the pathogenesis of AD. The AD datasets of GSE118553 and GSE131617 were collected from the NCBI GEO database. The weighted gene coexpression network analysis (WGCNA), differential gene expression analysis, and functional enrichment analysis were performed to reveal the hub genes and verify their role in AD. Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). We obtained 33, 42, 42, and 41 hub genes in modules associated with AD in TC, FC, EC, and CE tissues, respectively. Significant differences were recorded in the expression levels of hub genes between AD and the control group in the TC and EC tissues (P < 0.05). The differences in the expressions of FCGRT, SLC1A3, PTN, PTPRZ1, and PON2 in the FC and CE tissues among the AD and control groups were significant (P < 0.05). The expression levels of PLXNB1, GRAMD3, and GJA1 were statistically significant between the Braak NFT stages of AD. Overall, our study uncovered genes that may be involved in AD pathogenesis and revealed their potential for the development of AD biomarkers and appropriate AD therapeutics targets.

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 Co-expression network analysis identifies specific hub genes in association with developmental neuronal remodeling in Drosophila melanogaster

2019 ◽  
Author(s):  
Yunze Liu ◽  
Xiaojie Sun ◽  
Aijun Qu
Keyword(s):  
Drosophila Melanogaster ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Neuronal Remodeling ◽  
Gene Modules

As an evolutionarily conserved mechanism, developmental neuronal remodeling is needed for the proper wiring of the nervous system and is critical for understanding the neurodevelopment mechanisms. Previous studies have shown that during metamorphosis lots of Drosophila melanogaster mushroom body neurons experience stereotypic remodeling. However, the related regulators and downstream executors of pathways are yet unclear, especially studies of transcriptional gene co-expression analysis of nervous systems remain insufficient. In this study, we develop a weighted gene co-expression network (WGCNA) to classify gene modules associated with neuronal remodeling. Moreover, functional and pathway enrichment analysis with protein-protein network construction is applied to detect high informative hub genes in the targeted gene module. Thus, we select a total of five hub genes that play critical roles in neuronal remodeling and identify them with functional enrichment analysis and protein-protein interaction network. Overall, this study provides insight into the underlying molecular mechanism of developmental neuronal remodeling in Drosophila melanogaster.

scholarly journals Co-expression network analysis identifies specific hub genes in association with developmental neuronal remodeling in Drosophila melanogaster

2019 ◽  
Author(s):  
Yunze Liu ◽  
Xiaojie Sun ◽  
Aijun Qu
Keyword(s):  
Drosophila Melanogaster ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Neuronal Remodeling ◽  
Gene Modules

As an evolutionarily conserved mechanism, developmental neuronal remodeling is needed for the proper wiring of the nervous system and is critical for understanding the neurodevelopment mechanisms. Previous studies have shown that during metamorphosis lots of Drosophila melanogaster mushroom body neurons experience stereotypic remodeling. However, the related regulators and downstream executors of pathways are yet unclear, especially studies of transcriptional gene co-expression analysis of nervous systems remain insufficient. In this study, we develop a weighted gene co-expression network (WGCNA) to classify gene modules associated with neuronal remodeling. Moreover, functional and pathway enrichment analysis with protein-protein network construction is applied to detect high informative hub genes in the targeted gene module. Thus, we select a total of five hub genes that play critical roles in neuronal remodeling and identify them with functional enrichment analysis and protein-protein interaction network. Overall, this study provides insight into the underlying molecular mechanism of developmental neuronal remodeling in Drosophila melanogaster.

scholarly journals Identification of Key Genes Related with Alzheimer’s Disease Treatment Through Bioinformatics Analysis

2018 ◽  
Vol 9 (1) ◽  
pp. 78
Author(s):  
Liqun Wang ◽  
Hongjia Qian ◽  
Liqun Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Interaction ◽  
Expression Profiles ◽  
Amyloid Β ◽  
Interaction Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Tg2576 Mice

T0901317, a live X receptor agonist, can reduce amyloid β generation in vitro and in a mouse Alzheimer’s disease (AD) model. To investigate the global molecular effects of T0901317 in mouse hippocampus, we downloaded public GSE31624 generated from the hippocampus of wild-type mice, Tg2576 mice and T0901317-treated Tg2576 mice. Differentially-expressed genes (DEGs) were identified on LIMMA of R software. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment were analyzed through DAVID. Protein- protein interaction and hub genes were obtained based on STRING and Cytoscape. Nine downregulated and 68 upregulated DEGs in T0901317-treated Tg2576 were identified in comparison with untreated Tg2576 mice. Annotation analyses showed these DEGs correlated with transport (BP), membrane (CC) and binding (MF) terms and the dopaminergic synapse pathway. Protein-protein interaction network was built to find out some hub genes by maximal clique centrality. Discs large homolog 4 (Dlg4), the most outstanding gene, was associated with cognition improvement in aged AD mice. T0901317 may impact the development by regulating the Dlg4 expression. In conclusion, we investigated effects of T0901317 therapy on gene expression profiles in the hippocampus of Tg2576 mice and found Dlg4 may serve as putative therapeutics target for AD treatment.

scholarly journals Bioinformatic Analysis of Crosstalk Between circRNA, miRNA, and Target Gene Network in NAFLD

2021 ◽  
Vol 12 ◽  
Author(s):  
Cen Du ◽  
Lan Shen ◽  
Zhuoqi Ma ◽  
Jian Du ◽  
Shi Jin
Keyword(s):  
Liver Disease ◽  
Protein Interaction ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Alcoholic Fatty Liver ◽  
Protein Protein Interaction ◽  
Differential Genes

Background: The majority of chronic liver disease is caused by non-alcoholic fatty liver disease (NAFLD), which is one of the highly prevalent diseases worldwide. The current studies have found that non-coding RNA (ncRNA) plays an important role in the NAFLD, but few studies on circRNA. In this study, genes, microRNA (miRNA), and circular RNA (circRNA) associated with NAFLD were found by bioinformatic methods, bringing a novel perspective for the prevention and treatment of NAFLD.Methods: Expression data of GSE63067 was acquired from Gene Expression Omnibus (GEO) database. The liver samples were collected from the people diagnosed with NAFLD or not. Differentially expressed genes (DEGs) were obtained from the steatosis vs. the control group and non-alcoholic steatohepatitis (NASH) vs. the control group using the GEO2R online tool. The overlapping genes remained for further functional enrichment analysis and protein-protein interaction network analysis. MiRNAs and circRNAs targeting these overlapping DEGs were predicted from the databases. Finally, the GSE134146 dataset was used to verify the expression of circRNA.Results: In summary, 228 upregulated and 63 downregulated differential genes were selected. The top 10 biological processes and relative signaling pathways of the upregulated differential genes were obtained. Also, ten hub genes were performed in the Protein-protein interaction (PPI) network. One hundred thirty-nine miRNAs and 902 circRNAs were forecast for the differential genes by the database. Ultimately, the crosstalk between hsa_circ_0000313, miR-6512-3p, and PEG10 was constructed.Conclusion: The crosstalk of hsa_circ_0000313-hsa-miR-6512-3p-PEG10 and some related non-coding RNAs may take part in NAFLD’s pathogenesis, which could be the potential biomarkers of NAFLD in the future.

scholarly journals Exploring The Molecular Mechanisms of Classical Hodgkin Lymphoma Through Bioinformatics Analysis

2021 ◽  
Author(s):  
Zhu Lili ◽  
Zhu YuKun ◽  
Zhuangzhuang Tian ◽  
Yongsheng Li ◽  
Liyu Cao
Keyword(s):  
Hodgkin Lymphoma ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Background Classic Hodgkin lymphoma (CHL) is the most common HL in the modern society. Although the treatment of cHL has made great progress, its molecular mechanisms have yet to be deciphered. Objectives The purpose of this study is to find out the crucial potential genes and pathways associated with cHL. Methods We downloaded the cHL microarray dataset (GSE12453) from Gene Expression Omnibus (GEO) database and to identify the differentially expressed genes (DEGs) between cHL samples and normal samples through the limma package in R. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out. Finally, we constructed the protein-protein interaction network to screen out the hub genes using Search Tool for the Retrieval of Interacting Genes (STRING) database. Results We screened out 788 DEGs in the cHL dataset, such as BATF3, IER3, RAB13 and FCRL2. GO functional enrichment analysis indicated that the DEGs were related with regulation of lymphocyte activation, secretory granule lumen and chemokine activity. KEGG pathway analysis showed that the genes enriched in Prion disease, Complement and coagulation cascades and Parkinson disease Coronavirus disease-COVID-19 pathway. Protein-protein interaction network construction identified 10 hub genes (IL6, ITGAM, CD86, FN1, MMP9, CXCL10, CCL5, CD19, IFNG, SELL, UBB) in the network. Conclusions In the present investigation, we identified several pathways and hub genes related to the occurrence and development of cHL, which may provide an important basis for further research and novel therapeutic targets and prognostic indicators for cHL.

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