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Identification of five potential predictive biomarkers for Alzheimer's disease by integrating the unified test for molecular signatures and weighted gene co-expression network analysis.

BACKGROUND
Previous transcriptome-wide association study (TWAS) has documented 21 genes associated with Alzheimer's disease (AD) risk, but the predictive biomarkers remain unexplored.


METHODS
TWAS leveraging the unified test for molecular signatures (UTMOST) was performed in 75,000 cases and 420,000 controls with ten brain tissue gene expression references. Weighted gene co-expression network analysis (WGCNA) was conducted in GSE5281 and GSE48350 datasets containing 167 AD samples and 247 controls. Random forests (RF) analysis was applied to screen the potential predictive biomarkers based on overlapping genes identified by TWAS and WGCNA, followed by comprehensive bioinformatic analyses with differential gene expression, functional enrichment, and correlation with immune cells. A nomogram was established to verify the predictive power of the identified biomarkers.


RESULTS
TWAS revealed 78 candidate genes (p<2.89×10 -6). In WGCNA turquoise module, 3718 AD related genes were screened. RF identified five predictive biomarkers (FAM71E1, DDB2, AP4M1, GPR4, DOC2A), which are enriched in the global genome nucleotide excision repair pathway and associated with immune cell designations "Natural.killer.T.cell", "Memory.B.cell", "T.follicular.helper.cell", "Neutrophil" and "MDSC". The nomogram based on the five markers showed a high predictive power.


CONCLUSION
Five potential predictive biomarkers for AD were identified, providing new insights into the pathogenesis and etiology of AD.