Identification of Key Genes and Pathways Asscociated with Alzheimer's Disease Based on Bioinformatics Databases

2021 ◽  
Author(s):  
Wei Liu ◽  
Yingming Yang ◽  
Yongxi Jin ◽  
Chengcheng Song ◽  
Xiaohong Ye ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Bioinformatics Databases ◽  
Key Genes

scholarly journals Analysis of Immune-related Key Genes in Alzheimer’s Disease

Bioengineered ◽  
2021 ◽  
Author(s):  
You Wu ◽  
Shunli Liang ◽  
Hong Zhu ◽  
Yaping Zhu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Key Genes

scholarly journals Application of Weighted Gene Co-Expression Network Analysis to Explore the Key Genes in Alzheimer’s Disease

2018 ◽  
Vol 65 (4) ◽  
pp. 1353-1364 ◽  
Author(s):  
Jia-Wei Liang ◽  
Zheng-Yu Fang ◽  
Yong Huang ◽  
Zhen-yu Liuyang ◽  
Xiao-Lin Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Key Genes

scholarly journals Identification of Immune Cells and Key Genes associated with Alzheimer's Disease

2022 ◽  
Vol 19 (1) ◽  
pp. 112-125
Author(s):  
Chenming Liu ◽  
Xi Zhang ◽  
Huazhen Chai ◽  
Sutong Xu ◽  
Qiulu Liu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Immune Cells ◽  
Key Genes

scholarly journals Preservation Analysis on Spatiotemporal Specific Co-expression Networks Suggests the Immunopathogenesis of Alzheimer’s Disease

2021 ◽  
Vol 13 ◽  
Author(s):  
Liyuan Guo ◽  
Yushan Liu ◽  
Jing Wang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Expression Profiling ◽  
Clinical Symptoms ◽  
Brain Regions ◽  
Postmortem Brain ◽  
Postmortem Brain Tissue ◽  
Key Genes ◽  
Disease Stages

The occurrence and development of Alzheimer’s disease (AD) is a continuous clinical and pathophysiological process, molecular biological, and brain functional change often appear before clinical symptoms, but the detailed underlying mechanism is still unclear. The expression profiling of postmortem brain tissue from AD patients and controls provides evidence about AD etiopathogenesis. In the current study, we used published AD expression profiling data to construct spatiotemporal specific coexpression networks in AD and analyzed the network preservation features of each brain region in different disease stages to identify the most dramatically changed coexpression modules and obtained AD-related biological pathways, brain regions and circuits, cell types and key genes based on these modules. As result, we constructed 57 spatiotemporal specific networks (19 brain regions by three disease stages) in AD and observed universal expression changes in all 19 brain regions. The eight most dramatically changed coexpression modules were identified in seven brain regions. Genes in these modules are mostly involved in immune response-related pathways and non-neuron cells, and this supports the immune pathology of AD and suggests the role of blood brain barrier (BBB) injuries. Differentially expressed genes (DEGs) meta-analysis and protein–protein interaction (PPI) network analysis suggested potential key genes involved in AD development that might be therapeutic targets. In conclusion, our systematical network analysis on published AD expression profiling data suggests the immunopathogenesis of AD and identifies key brain regions and genes.

scholarly journals MicroRNAs and Alzheimer's Disease Mouse Models: Current Insights and Future Research Avenues

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Charlotte Delay ◽  
Sébastien S. Hébert
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Animal Models ◽  
Mouse Models ◽  
Future Research ◽  
Neuronal Function ◽  
Key Genes

Evidence from clinical trials as well as from studies performed in animal models suggest that both amyloid and tau pathologies function in concert with other factors to cause the severe neurodegeneration and dementia in Alzheimer’s disease (AD) patients. Accumulating data in the literature suggest that microRNAs (miRNAs) could be such factors. These conserved, small nonprotein-coding RNAs are essential for neuronal function and survival and have been implicated in the regulation of key genes involved in genetic and sporadic AD. The study of miRNA changes in AD mouse models provides an appealing approach to address the cause-consequence relationship between miRNA dysfunction and AD pathology in humans. Mouse models also provide attractive tools to validate miRNA targetsin vivoand provide unique platforms to study the role of specific miRNA-dependent gene pathways in disease. Finally, mouse models may be exploited for miRNA diagnostics in the fight against AD.

scholarly journals Identification of potential biomarkers for pathogenesis of Alzheimer’s disease

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Huimin Wang ◽  
Xiujiang Han ◽  
Sheng Gao
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Maximal Clique ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Kegg Pathways ◽  
Functional Connections ◽  
Key Genes ◽  
Potential Biomarkers

Abstract Background Alzheimer’s disease (AD) is an extremely complicated neurodegenerative disorder, which accounts for almost 80 % of all dementia diagnoses. Due to the limited treatment efficacy, it is imperative for AD patients to take reliable prevention and diagnosis measures. This study aimed to explore potential biomarkers for AD. Methods GSE63060 and GSE140829 datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEG) between AD and control groups in GSE63060 were analyzed using the limma software package. The mRNA expression data in GSE140829 was analyzed using weighted gene co-expression network analysis (WGCNA) function package. Protein functional connections and interactions were analyzed using STRING and key genes were screened based on the degree and Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the key genes. Results There were 65 DEGs in GSE63060 dataset between AD patients and healthy controls. In GSE140829 dataset, the turquoise module was related to the pathogenesis of AD, among which, 42 genes were also differentially expressed in GSE63060 dataset. Then 8 genes, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were finally screened. Additionally, these 42 genes were significantly enriched in 12 KEGG pathways and 119 GO terms. Conclusions In conclusion, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were potential biomarkers for pathogenesis of AD, which should be further explored in AD in the future.

scholarly journals Exploring the Key Genes and Identification of Potential Diagnosis Biomarkers in Alzheimer’s Disease Using Bioinformatics Analysis

2021 ◽  
Vol 13 ◽  
Author(s):  
Wuhan Yu ◽  
Weihua Yu ◽  
Yan Yang ◽  
Yang Lü
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Validation Dataset ◽  
Braak Stage ◽  
Hub Genes ◽  
Diagnostic Biomarkers ◽  
Functional Features ◽  
Key Genes

BackgroundAlzheimer’s disease (AD) is one of the major threats of the twenty-first century and lacks available therapy. Identification of novel molecular markers for diagnosis and treatment of AD is urgently demanded, and genetic biomarkers show potential prospects.MethodWe identify and intersected differentially expressed genes (DEGs) from five microarray datasets to detect consensus DEGs. Based on these DEGs, we conducted Gene Ontology (GO), performed the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, constructed a protein—protein interaction (PPI) network, and utilized Cytoscape to identify hub genes. The least absolute shrinkage and selection operator (LASSO) logistic regression was applied to identify potential diagnostic biomarkers. Gene set enrichment analysis (GSEA) was performed to investigate the biological functions of the key genes.ResultWe identified 608 consensus DEGs, several dysregulated pathways, and 18 hub genes. Sixteen hub genes dysregulated as AD progressed. The diagnostic model of 35 genes was constructed, which has a high area under the curve (AUC) value in both the validation dataset and combined dataset (AUC = 0.992 and AUC = 0.985, respectively). The model can also differentiate mild cognitive impairment and AD patients from controls in two blood datasets. Brain-derived neurotrophic factor (BDNF) and WW domain-containing transcription regulator protein 1 (WWTR1), which are associated with the Braak stage, Aβ 42 levels, and β-secretase activity, were identified as critical genes of AD.ConclusionOur study identified 16 hub genes correlated to the neuropathological stage and 35 potential biomarkers for the diagnosis of AD. WWTR1 were identified as candidate genes for future studies. This study deepens our understanding of the transcriptomic and functional features and provides new potential diagnostic biomarkers and therapeutic targets for AD.

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