Systems biology and bioinformatics approach to identify gene signatures, pathways and therapeutic targets of Alzheimer’s disease

Abstract Alzheimer's disease (AD) develops relentlessly in affected individuals and its occurrence is increasing. A clinical test to diagnose early-stage AD could be an important means of enabling interventions to slow its progression. However, available neuroimaging and cerebrospinal fluid-based diagnoses are very costly. Therefore, detecting AD from blood transcripts that mirror the expression of brain transcripts in the AD could improve the diagnosis. To achieve this goal, we employed a transcriptional analysis of affected tissues and integrated them with cis-eQTL data. In this study, we analyzed microarray gene expression data of brain and blood cells from AD patients and control individuals. Differentially expressed genes (DEGs) common to both brain tissue and blood cells were identified. Potential common genes and molecular pathways were identified using overlapping DEGs through the pathway and gene ontology enrichment analysis. We identified 18 significantly dysregulated genes shared by both brain and blood cells in AD affected individuals. We validated these candidates as disease-associated genes using gold-standard benchmarking databases (gene SNP-disease linkage). Significant molecular pathway and gene ontology indicating AD progression were identified. This study also identified regulatory factors, including transcription factors (TFs), microRNAs and candidate drugs. In sum, we identified new putative links between pathological processes in brain tissue and blood cells in AD that may allow assessment of AD status using blood samples. Thus, our formulated methodologies demonstrate the power of gene and gene expression analysis for brain-related pathologies transcriptomics, cis-eQTL, and epigenetics data from brain and blood cells.