Identification of vascular dementia and Alzheimer's disease hub genes expressed in the frontal lobe and temporal cortex by weighted co-expression network analysis and construction of a protein-protein interaction

2021 ◽  
pp. 1-18
Author(s):  
Xiaodou Tian ◽  
Yao Qin ◽  
Yuling Tian ◽  
Xiaoyan Ge ◽  
Jing Cui ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Vascular Dementia ◽  
Frontal Lobe ◽  
Protein Interaction ◽  
Temporal Cortex ◽  
Hub Genes ◽  
Protein Protein Interaction

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.

O2-13-06: The protein-protein interaction network analysis of neurobiology of Alzheimer's disease: A blood-based mirna systematic revision study

2015 ◽  
Vol 11 (7S_Part_4) ◽  
pp. P206-P207
Author(s):  
Ana Paula Mendes Silva ◽  
Kenia Kelly Fiaux do Nascimento ◽  
Kelly Silva Pereira ◽  
Eduardo de Souza Nicolau ◽  
Lucélia SilvaS Barroso ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Systematic Revision ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

scholarly journals Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weishuang Xue ◽  
Jinwei Li ◽  
Kailei Fu ◽  
Weiyu Teng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

Putative Factors Interfering Cell Cycle Re-Entry in Alzheimer’s Disease: An Omics Study with Differential Expression Meta-Analytics and Co-Expression Profiling

2021 ◽  
pp. 1-26
Author(s):  
Sze Chung Yuen ◽  
Simon Ming-Yuen Lee ◽  
Siu-wai Leung
Keyword(s):  
Alzheimer’S Disease ◽  
Cell Cycle ◽  
Alzheimer's Disease ◽  
Expression Analysis ◽  
Protein Interaction ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
Tau Proteins ◽  
Related Factors ◽  
Protein Protein Interaction

Background: Neuronal cell cycle re-entry (CCR) is a mechanism, along with amyloid-β (Aβ) oligomers and hyperphosphorylated tau proteins, contributing to toxicity in Alzheimer’s disease (AD). Objective: This study aimed to examine the putative factors in CCR based on evidence corroboration by combining meta-analysis and co-expression analysis of omic data. Methods: The differentially expressed genes (DEGs) and CCR-related modules were obtained through the differential analysis and co-expression of transcriptomic data, respectively. Differentially expressed microRNAs (DEmiRNAs) were extracted from the differential miRNA expression studies. The dysregulations of DEGs and DEmiRNAs as binary outcomes were independently analyzed by meta-analysis based on a random-effects model. The CCR-related modules were mapped to human protein-protein interaction databases to construct a network. The importance score of each node within the network was determined by the PageRank algorithm, and nodes that fit the pre-defined criteria were treated as putative CCR-related factors. Results: The meta-analysis identified 18,261 DEGs and 36 DEmiRNAs, including genes in the ubiquitination proteasome system, mitochondrial homeostasis, and CCR, and miRNAs associated with AD pathologies. The co-expression analysis identified 156 CCR-related modules to construct a protein-protein interaction network. Five genes, UBC, ESR1, EGFR, CUL3, and KRAS, were selected as putative CCR-related factors. Their functions suggested that the combined effects of cellular dyshomeostasis and receptors mediating Aβ toxicity from impaired ubiquitination proteasome system are involved in CCR. Conclusion: This study identified five genes as putative factors and revealed the significance of cellular dyshomeostasis in the CCR of AD.

Immune-Related Hub Genes and the Competitive Endogenous RNA Network in Alzheimer’s Disease

2020 ◽  
Vol 77 (3) ◽  
pp. 1255-1265
Author(s):  
Hui Xu ◽  
Jianping Jia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Data Set ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Immune Phenomena ◽  
Competitive Endogenous Rna

Background: The pathogenesis of Alzheimer’s disease (AD) involves various immune-related phenomena; however, the mechanisms underlying these immune phenomena and the potential hub genes involved therein are unclear. An understanding of AD-related immune hub genes and regulatory mechanisms would help develop new immunotherapeutic targets. Objective: The aim of this study was to explore the hub genes and the mechanisms underlying the regulation of competitive endogenous RNA (ceRNA) in immune-related phenomena in AD pathogenesis. Methods: We used the GSE48350 data set from the Gene Expression Omnibus database and identified AD immune-related differentially expressed RNAs (DERNAs). We constructed protein–protein interaction (PPI) networks for differentially expressed mRNAs and determined the degree for screening hub genes. By determining Pearson’s correlation coefficient and using StarBase, DIANA-LncBase, and Human MicroRNA Disease Database (HMDD), the AD immune-related ceRNA network was generated. Furthermore, we assessed the upregulated and downregulated ceRNA subnetworks to identify key lncRNAs. Results: In total, 552 AD immune-related DERNAs were obtained. Twenty hub genes, including PIK3R1, B2M, HLA-DPB1, HLA-DQB1, PIK3CA, APP, CDC42, PPBP, C3AR1, HRAS, PTAFR, RAB37, FYN, PSMD1, ACTR10, HLA-E, ARRB2, GGH, ALDOA, and VAMP2 were identified on PPI network analysis. Furthermore, upon microRNAs (miRNAs) inhibition, we identified LINC00836 and DCTN1-AS1 as key lncRNAs regulating the aforementioned hub genes. Conclusion: AD-related immune hub genes include B2M, FYN, PIK3R1, and PIK3CA, and lncRNAs LINC00836 and DCTN1-AS1 potentially contribute to AD immune-related phenomena by regulating AD-related hub genes.

scholarly journals Key Gene Network Related to Primary Ciliary Dyskinesia in Hippocampus of Patients With Alzheimer’s Disease Revealed by Weighted Gene Co-expression Network Analysis

2021 ◽  
Author(s):  
Pengcheng Xia ◽  
Jing Chen ◽  
Xiaohui Bai ◽  
Ming Li ◽  
Le Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Cell Biology ◽  
Primary Ciliary Dyskinesia ◽  
Gene Network ◽  
Pathophysiological Mechanism ◽  
Hub Genes ◽  
Ciliary Movement ◽  
Ciliary Dyskinesia

Abstract Background. Alzheimer's disease (AD) is closely related to aging, showing an increasing incidence rate for years. As one of the main organs involved in AD, hippocampus has been extensively studied due to its association with many human diseases. However, little knowledge is known on its association with primary ciliary dyskinesia (PCD).Material and Methods. The microarray data of hippocampus on AD were retrieved from the Gene Expression Omnibus (GEO) database to construct the co-expression network by weighted gene co-expression network analysis (WGCNA). The gene network modules associated with AD screened with the common genes were further annotated based on Gene Ontology (GO) database and enriched based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The protein-protein interaction (PPI) network was constructed based on STRING database to identify the hub genes in the network.Results. Genes involved in PCD were identified in the hippocampus of AD patients. Functional analysis revealed that these genes were mainly enriched in ciliary tissue, ciliary assembly, axoneme assembly, ciliary movement, microtubule based process, microtubule based movement, organelle assembly, axoneme dynamin complex, cell projection tissue, and microtubule cytoskeleton tissue. A total of 20 central genes, e.g.,DYNLRB2, ZMYND10, DRC1, DNAH5, WDR16, TTC25, and ARMC4 were identified as hub genes related to PCD in hippocampus of AD patients.Conclusion. Our study demonstrated that AD and PCD have shared metabolic pathways. These common pathways provide novel evidence for further investigation of the pathophysiological mechanism and the hub genes suggest new therapeutic targets for the diagnosis and treatment of AD and PCD.Subjects Bioinformatics, Cell Biology, Molecular Biology, Neurology

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.

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