Molecular Subgroup Classification in Alzheimer's Disease by Transcriptomic Profiles.

Area of Science:

• Neuroscience
• Genetics
• Biochemistry

Background:

• Alzheimer's disease (AD) is a global neurodegenerative disorder with limited disease-modifying treatments.
• Identifying novel therapeutic targets and pathways is crucial for effective AD intervention.

Purpose of the Study:

• To classify Alzheimer's disease into molecular subgroups.
• To identify subgroup-specific genes, pathways, and potential therapeutic targets.

Main Methods:

• Utilized consensus clustering on 1068 AD samples from two datasets.
• Performed weighted gene coexpression analysis (WGCNA), Gene Ontology (GO), KEGG, and gene set enrichment analysis (GSEA).
• Constructed protein-protein interaction networks and correlated hub genes with clinical traits, including neurofibrillary tangle (Nft) density.

Main Results:

• Classified AD samples into three distinct molecular subgroups.
• Subgroup III patients were younger with more severe pathology.
• Identified enriched pathways including dopaminergic synapse (Subgroup I), neurotrophin signaling, and TGF-beta signaling (Subgroup III).
• Hub genes in Subgroup I negatively correlated with Nft density, while those in Subgroups II and III showed positive correlations.

Conclusions:

• Molecular subgrouping provides new insights into Alzheimer's disease heterogeneity.
• Specific pathways and hub genes identified offer potential therapeutic targets for distinct AD subgroups.
• This classification aids in developing targeted treatment strategies for Alzheimer's disease.