Background: Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. Identification of AD-related genes from transcriptomics provided new direction to the mechanism for finding potential targets for drug therapy.
Methods: We mined gene co-expression network modules from differentially expressed genes (DEGs) of AD and normal samples in multiple datasets by weighted gene co-expression network analysis (WGCNA). A convergent functional genomic (CFG) method was used to prioritize potential driver genes.
Results: The 7567 DEGs were enriched significantly with 61 KEGG pathway and 242 GO terms. Then, the genes in 5 AD-specific modules obtained significantly from DEGs were interconnected with well-known AD risk genes in common PPI network. Remarkably, compared to the number of Tau production-related genes, Aβ play a more critical role. Lastly, the 23 potential driver genes was prioritized by CFG method from 5 AD-specific modules.
Conclusions: Identification of AD-related genes could be useful for understanding pathophysiology of AD and looking for candidates drug targets.