Title of article :
Exploring Potential Biomarkers Underlying Pathogenesis of Alzheimer’s Disease by Differential Co-expression Analysis
Author/Authors :
Izadi, Fereshteh Department of Genetics - Evolution and Environment - Darwin Building - University College London (UCL), London, UK , Soheilifar, Mohammad Hasan Research Center for Molecular Medicine - Hamedan University of Medical Sciences, Hamedan, Iran
Abstract :
Background: Alzheimer's Disease (AD) is the most common form of dementia in the
elderly. Due to the facts that biological causes of AD are complex in addition to increasing
rates of AD worldwide, a deeper understanding of AD etiology is required for
AD treatment and diagnosis.
Methods: To identify molecular pathological alterations in AD brains, GSE36980 series
containing microarray data samples from temporal cortex, frontal cortex and hippocampus
were downloaded from Gene Expression Omnibus (GEO) database and valid
gene symbols were subjected to building a gene co-expression network by a bioinformatics
tool known as differential regulation from differential co-expression (DCGL)
software package. Then, a network-driven integrative analysis was performed to find
significant genes and underlying biological terms.
Results: A total of 17088 unique genes were parsed into three independent differential
co-expression networks. As a result, a small number of differentially co-regulated
genes mostly in frontal and hippocampus lobs were detected as potential biomarkers
related to AD brains. Ultimately differentially co-regulated genes were enriched in biological
terms including response to lipid and fatty acid and pathways mainly signaling
pathway such as G-protein signaling pathway and glutamate receptor groups II
and III. By conducting co-expression analysis, our study identified multiple genes that
may play an important role in the pathogenesis of AD.
Conclusion: The study aimed to provide a systematic understanding of the potential
relationships among these genes and it is hoped that it could aid in AD biomarker discovery.
Keywords :
Dementia , Computational biology , Alzheimer’s disease
Journal title :
Astroparticle Physics