Exploring Gene Expression Patterns in Alzheimer's Disease Using a Human Microarray Data Meta-Analysis

Area of Science:

• Neuroscience and Genetics
• Molecular Biology

Background:

• Alzheimer's disease (AD) is a leading neurodegenerative disorder, with aging as the primary risk factor.
• The escalating global elderly population correlates with a rising prevalence of AD.
• AD pathology begins years before symptom onset, underscoring the need for early diagnosis and prognosis.

Purpose of the Study:

• To identify differentially expressed genes (DEGs) between Alzheimer's disease and healthy brains.
• To discover genes with potential as risk factors, diagnostic, prognostic, or pharmacological biomarkers for AD.

Main Methods:

• Systematic meta-analysis of microarray datasets from public repositories, adhering to PRISMA 2020 guidelines.
• Quality control, data normalization, and Mosteller-Bush meta-analysis to combine DEG lists from eight studies.
• Enrichment analysis of statistically significant DEGs using an adjusted p-value cut-off of 0.001.

Main Results:

• A combined list of 4218 DEGs was identified between AD and healthy brains.
• 1944 DEGs were up-regulated, enriched for immune response and inflammation-related processes.
• 2274 DEGs were down-regulated, enriched for synapse-related pathways and neuronal signaling.

Conclusions:

• Alzheimer's disease exhibits a distinct transcriptomic profile characterized by heightened immune activity.
• A significant collapse in synaptic and neuronal signaling pathways is evident in AD brains.
• Identified DEGs offer potential for early AD diagnosis, prognosis, and therapeutic target development.