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 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.

Co-expression Network Analysis Revealing the Potential Regulatory Roles of lncRNAs in Alzheimer’s Disease

2019 ◽  
Vol 11 (4) ◽  
pp. 645-654 ◽  
Author(s):  
Jiong Wu ◽  
Linhui Chen ◽  
Chaobo Zheng ◽  
Shanhu Xu ◽  
Yuhai Gao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis

Associations Between Obesity and Alzheimer’s Disease: Multiple Bioinformatic Analyses

2021 ◽  
pp. 1-11
Author(s):  
Qi-Shuai Zhuang ◽  
Lei Meng ◽  
Zhe Wang ◽  
Liang Shen ◽  
Hong-Fang Ji
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Drug Targets ◽  
Protective Factor ◽  
Genetic Locus ◽  
Meta Analysis ◽  
Current Evidence ◽  
Causal Association ◽  
Ppi Network

Background: Identifying modifiable risk factors, such as obesity, to lower the prevalence of Alzheimer’s disease (AD) has gained much interest. However, whether the association is causal remains to be evaluated. Objective: The present study was designed: 1) to make a quantitative assessment of the association between obesity and AD; 2) to validate whether there was a causal association between them; and 3) to provide genetic clues for the association through a network-based analysis. Methods: Two-sample Mendelian randomization (2SMR) analysis, meta-analysis, and protein-protein interaction (PPI) network analysis, were employed. Results: Firstly, the meta-analysis based on 9 studies comprising 6,986,436 subjects indicated that midlife obesity had 33%higher AD odds than controls (OR = 1.33, 95%CI = [1.03, 1.62]), while late-life obesity were inversely associated with AD risk (OR = 0.57, 95%CI = [0.47, 0.68]). Secondly, 2SMR analysis indicated that there was no causal association between them. Thirdly, CARTPT was identified to be shared by the anti-obesity drug targets and AD susceptibility genes. Further PPI network analysis found that CARTPT interacted with CD33, a strong genetic locus linked to AD. Finally, 2SMR analysis showed that CNR1 could be a protective factor for AD. Conclusion: Multiple bioinformatic analyses indicated that midlife obesity might increase the risk of AD, while current evidence indicated that there was no causal association between them. Further, CARTPT might be an important factor linking the two disease conditions. It could help to better understand the mechanisms underlying the associations between obesity and AD.

scholarly journals Integrated proteomics and network analysis identifies protein hubs and network alterations in Alzheimer’s disease

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Qi Zhang ◽  
Cheng Ma ◽  
Marla Gearing ◽  
Peng George Wang ◽  
Lih-Shen Chin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis

scholarly journals Metabolic Network Analysis Reveals Altered Bile Acid Synthesis and Metabolism in Alzheimer’s Disease

2020 ◽  
Vol 1 (8) ◽  
pp. 100138
Author(s):  
Priyanka Baloni ◽  
Cory C. Funk ◽  
Jingwen Yan ◽  
James T. Yurkovich ◽  
Alexandra Kueider-Paisley ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Bile Acid ◽  
Network Analysis ◽  
Metabolic Network ◽  
Acid Synthesis ◽  
Bile Acid Synthesis ◽  
Metabolic Network Analysis

scholarly journals The human brainome: network analysis identifies HSPA2 as a novel Alzheimer’s disease target

Brain ◽  
2018 ◽  
Author(s):  
Vladislav A Petyuk ◽  
Rui Chang ◽  
Manuel Ramirez-Restrepo ◽  
Noam D Beckmann ◽  
Marc Y R Henrion ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis
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