Current therapeutic strategies for Alzheimer's disease (AD) demonstrate limited efficacy in decelerating disease progression, underscoring an exigent need for the development of more potent disease-modifying therapeutics.
Objective
The primary aim of this research was to identify novel therapeutic targets to improve AD prognosis.
Methods
First, we conducted a meta-analysis of brain tissue transcriptome datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) associated with AD. Next, Mendelian randomization (MR) and summary-based MR (SMR) analyses were utilized to screen for potential AD drug targets. Colocalization analyses were employed to examine whether DNA methylation, gene expression, and AD risk are driven by shared single nucleotide polymorphisms (SNPs). Finally, single-gene gene set enrichment analysis (GSEA), protein–protein interaction (PPI) networks, drug prediction, and molecular docking were employed to infer potential biological mechanisms.
Results
A meta-analysis of twelve brain tissue datasets revealed 262 druggable AD-related DEGs. According to MR analysis, VEGFB, GIPR, and CD3E were significantly associated with AD. However, only VEGFB met the criteria for all three-step SMR methods and colocalization analysis, with genetically predicted lower DNA methylation levels (OR 0.86, 95% CI 0.79–0.94) and higher gene expression (OR 1.34, 95% CI 1.14–1.58) positively correlated with increased AD risk. The therapeutic potential of VEGFB for AD was further corroborated by additional analyses.
Conclusions
This study suggests that the modulation of VEGFB and its related pathways could be a promising therapeutic target for AD, offering a new direction for future drug development and targeted therapies.
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