Developing a blood‐derived gene expression biomarker specific for Alzheimer's disease. (31st December 2021) - Korukonda, Anuradha; Dutkowski, Janusz; Ronen, Roy; Sachdev, Pallavi
Abstract: Background: Early detection, monitoring, and treatment of Alzheimer's disease (AD) will require measurement of easily accessible and reliable fluid biomarkers. We compared brain and blood gene and pathway expression analyses to identify core features for discriminating AD from controls. The brain gene expression profile was generated using a dataset (GSE33000) from post‐mortem dorsolateral prefrontal cortex isolated from 624 AD patients and genotype‐matched control samples, while blood gene expression profiles were generated using GSE63060 (n=329) and GSE63061 (n=388) datasets derived from peripheral blood samples of two case‐controlled studies of the AddNeuroMed consortium. Method: The gene expression datasets were retrieved from the Gene Expression Omnibus (GEO). Limma linear models were used to compute moderated T‐statistics and identify differentially expressed genes (DEGs) in the AD condition. Gene Set Enrichment Analysis (GSEA, applying the Piano R package to moderated T‐statistics from DEG analysis) was employed to identify significantly differentially regulated REACTOME and KEGG pathways. Comparison of the brain and blood‐derived analyses provided a common set of pathways modulated in AD patients. Result: DEG analysis revealed 680, 116, and 21 upregulated genes and 136, 160, and 45 downregulated genes in GSE33000 (Brain), GSE63060, and GSE63061 (Blood) datasets, respectively (FDR < 0.05; upregulated: FC > 1.15, downregulated: FC < 0.8). Differential pathway expression analysis using GSEA (FDR < 0.05) yielded 218, 196, and 202 REACTOME and 151, 69, and 125 KEGG pathways in GSE33000, GSE63060, and GSE63061 datasets, respectively. Notably, 21/115 and 56/103 up‐ and downregulated REACTOME pathways respectively, in the brain‐derived dataset were shared with the blood‐derived dataset (FDR < 0.05). Strikingly, 52.4% and 23.8% of the upregulated pathways were dominated by immune system and hemostasis, respectively. Meanwhile, 42.9% and 10.7% of the downregulated pathways included cell cycle and signal transduction pathways, respectively. Conclusion: Systematic analysis of genes and pathways differentially expressed in AD samples across brain and blood identified common signatures reproducible across studies and showed potential for distinguishing between AD and control patients. Our results suggest the potential of employing a blood‐based gene expression panel for early diagnosis and disease monitoring of AD.