Identification of alzheimer diseaseassociated pathways and network using transcriptome analysis / Lau Ching Yee
Alzheimer's disease (AD) is a progressive neurodegenerative disease and the most common form of dementia. The disease mainly affects people aged 65 and older. The mechanisms underlying AD aetiology is still not clearly understood due to its complex nature. In this study, we integratively reanal...
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| Format: | Thesis |
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2018
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| Online Access: | http://studentsrepo.um.edu.my/12296/ http://studentsrepo.um.edu.my/12296/2/Lau_Ching_Yee.pdf http://studentsrepo.um.edu.my/12296/1/Lau_Ching_Yee.pdf |
| Summary: | Alzheimer's disease (AD) is a progressive neurodegenerative disease and the most common form of dementia. The disease mainly affects people aged 65 and older. The mechanisms underlying AD aetiology is still not clearly understood due to its complex nature. In this study, we integratively reanalyzed the publicly available transcriptome data sets of AD studies using human post-mortem brains. By using this method, the capability of detecting weak signals could be improved and novel biological insights which could not be obtained from the individual studies could be gained. In order to get reliable biological inference from the data, we compared and evaluated existing bioinformatic methods in transcriptomic analysis and selected the superior ones to be included in our data analysis pipeline. Since complex diseases like AD can be better understood from the perspective of network biology than at the individual gene level, we used NetDecoder, a state-of-the-art network-based transcriptomic analysis algorithm to capture genes that are associated with the differentially expressed genes in a network context. The networks established based on protein-protein interactions included key genes such as UBC, ABL1, YWHAZ, APP, TP53 and CTNNB1, which have also been reported by other AD studies. The networks potentially provide mechanistic insights to better understand how these genes interact and drive AD pathogenesis. Thus, the present study provides a workflow for mining promising target genes for further confirmatory experiments which can lead to more effective treatment of AD or better diagnostics for AD.
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