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Systematic Analysis and Biomarker Study for Alzheimer's Disease
Xinzhong Li1, Haiyan Wang2, Jintao Long3
1Plymouth University Faculty of Medicine and Dentistry, Drake Circus, Plymouth, PL4 8AA, UK. xinzhong.li@plymouth.ac.uk.
Scientific Reports
|November 28, 2018
Summary
This study analyzed gene expression in Alzheimer's Disease (AD) blood and brain tissues, identifying shared dysfunctional genes and pathways. Novel risk genes and a machine learning model for AD detection were discovered.
Area of Science:
- Genomics
- Neuroscience
- Biomarkers
Background:
- Alzheimer's Disease (AD) pathology is complex, involving genetic and molecular dysfunctions.
- Understanding shared molecular changes in blood and brain is crucial for AD diagnosis and treatment.
Purpose of the Study:
- To identify differentially expressed genes (DEGs) common to blood and brain in Alzheimer's Disease (AD) and mild cognitive impairment (MCI) patients.
- To discover novel AD risk genes and develop a predictive classification model.
Main Methods:
- Systematic analysis of DEGs in blood (245 AD, 143 MCI, 182 controls) and comparison with brain tissue data.
- Evaluation using independent AD blood datasets and gene-based genome-wide association study.
- Development of a machine learning classification model using identified DEGs.
Main Results:
- Identified 789 (AD) and 998 (MCI) common DEGs in blood and brain, with over 77% showing consistent regulation.
- Validated known gene ABCA7 and identified novel risk genes TYK2 and TCIRG1.
- Developed a machine learning model (NDUFA1, MRPL51, RPL36AL) distinguishing AD patients with 78.1% accuracy.
Conclusions:
- Mitochondrial dysfunction, NF-κB, and iNOS signaling pathways are significantly dysregulated in AD pathogenesis.
- Shared blood-brain gene expression patterns offer potential biomarkers for AD.
- Novel genes and a predictive model advance understanding and detection of Alzheimer's Disease.