Identification and experimental validation of Alzheimer's disease hub genes via bioinformatics and machine learning.

BACKGROUND

Alzheimer's disease (AD) is a complex neurodegenerative disorder.

OBJECTIVE

To identify diagnostic and predictive biomarkers for AD.

METHODS

Based on three GEO datasets of human brain tissue from AD patients and controls, weighted gene co-expression network analysis (WGCNA) and enrichment analysis were used to identify AD-related gene modules. Hub genes were screened via protein-protein interaction (PPI) analysis and three machine learning algorithms. Diagnostic efficacy was evaluated using receiver operating characteristic (ROC) curves. Immune cell infiltration and hub gene expression correlations were analyzed using xCell. In vivo validation was performed using an AD mouse model.

RESULTS

The magenta module was significantly correlated with AD. PPI network analysis identified 15 AD-related genes, mainly enriched in mitochondria and ribosomes. Two hub genes, DLAT and CCDC88b, were identified. DLAT was significantly downregulated in AD, and CCDC88b was upregulated (p < 0.01); both findings were validated via qPCR in AD model mice. ROC analysis showed good diagnostic performance. Immune infiltration analysis revealed macrophages as the dominant cell type, with hub gene expression associated with immune cell presence.

CONCLUSIONS

DLAT and CCDC88b are potential novel biomarkers for AD and may serve as targets for therapeutic intervention.