Title:Identification of Critical Genes Differentiating Stable and Unstable
Atherosclerotic Plaques: A Bioinformatic and Computational Analysis
Volume: 22
Issue: 4
Author(s): Maryam Mahjoubin-Tehran, Raul D. Santos, Wael Almahmeed, Khalid Al-Rasadi and Amirhossein Sahebkar*
Affiliation:
- Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
Keywords:
Atherosclerosis, stable plaque, unstable plaque, blood biomarkers, genes, Cytoscape.
Abstract:
Background: Identification of biomarkers to distinguish between stable and unstable
plaque formation would be very useful to predict plaque vulnerability.
Methods: We downloaded microarray profiles of gene set enrichment (GSE) accession numbers
including GSE71226 and GSE20680 (group A: containing healthy vs stable plaque samples) and
GSE62646 and GSE34822 (group B: containing stable vs unstable plaque samples) from Gene expression
omnibus (GEO) database. Differentially expressed genes were compared in both data sets
of each group.
Results: Ten and 12 key genes were screened in groups A and B, respectively. Gene Ontology
(GO) enrichment was applied by the plugin “BiNGO” (Biological networks gene ontology tool) of
the Cytoscape. The key genes were mostly enriched in the biological process of positive regulation
of the cellular process. The protein-protein interaction and co-expression network were analyzed
by the STRING (search tool for the retrieval of interacting genes/proteins) and GeneMANIA
(gene multiple association network integration algorithm) plugin of Cytoscape, respectively,
which showed that Epidermal growth factor (EGF), Heparin-binding EGF like growth factor
(HBEGF), and Matrix metalloproteinase 9 (MMP9) were at the core of the network. Further validation
of key genes using two datasets showed that Phosphodiesterase 5A (PDE5A) and Protein S
(PROS1) were decreased in unstable plaques, while Suppressor of cytokine signaling (SOCS3),
HBEGF, and Leukocyte immunoglobulin-like receptor B4 (LILRB4) were increased.
Conclusion: The present study used several datasets to identify key genes associated with stable
and unstable atherosclerotic plaque.
