Identification of Potential Driver Genes and Pathways Based on Transcriptomics Data in Alzheimer's Disease

Area of Science:

• Neuroscience
• Genomics
• Pharmacology

Background:

• Alzheimer's disease (AD) is a prevalent neurodegenerative disorder.
• Identifying AD-related genes is crucial for developing effective treatments.

Purpose of the Study:

• To identify novel Alzheimer's disease (AD)-related genes using transcriptomic data.
• To explore potential therapeutic targets for AD drug development.

Main Methods:

• Weighted Gene Co-expression Network Analysis (WGCNA) to identify co-expression networks in AD.
• Convergent Genomic approach (CFG) to prioritize candidate genes.
• Network prediction using deepDTnet and SAveRUNNER to analyze gene-drug-disease interactions.

Main Results:

• The gene GJA1 demonstrated the highest CFG score, indicating its significance in AD.
• Network analysis revealed interactions between GJA1, drugs, and AD.
• GJA1 is identified as a highly probable therapeutic target for Alzheimer's disease.

Conclusions:

• The study successfully identified GJA1 as a key gene associated with Alzheimer's disease.
• This research contributes to understanding AD pathophysiology.
• Findings support the development of new therapeutic strategies and drugs for AD.