Analyzing microarray data of Alzheimer's using cluster analysis to identify the biomarker genes

Area of Science:

• Neuroscience
• Genetics
• Molecular Biology

Background:

• Alzheimer's disease is pathologically defined by senile plaques and neurofibrillary tangles in the brain's cortex.
• Understanding gene expression patterns is crucial for elucidating the complex local environment in Alzheimer's.

Purpose of the Study:

• To identify genes with altered expression patterns in Alzheimer's disease.
• To explore potential novel genetic associations with Alzheimer's disease.

Main Methods:

• Utilized Gene Expression Omnibus data for experimental analysis.
• Applied hierarchical cluster analysis and TreeView for gene expression pattern grouping and visualization.
• Identified genes exhibiting high expression levels.

Main Results:

• A list of 24 genes with high expression levels was generated.
• Three key genes (SORL1, APP, APOE) are strongly suspected in causing Alzheimer's disease.
• An additional 21 genes (e.g., TMEM59, CCT4, IGF2R) were identified, potentially associated with Alzheimer's or other diseases.

Conclusions:

• Gene expression dynamics reveal a complex local milieu in Alzheimer's disease.
• The identified genes, particularly SORL1, APP, and APOE, warrant further investigation for their role in Alzheimer's pathogenesis.
• The study highlights potential novel genetic targets for Alzheimer's disease research.