A meta-analysis of microarray datasets to identify biological regulatory networks in Alzheimer’s disease

Frontiers in Genetics

Genetics

Ali Akbar Hemmati¹  Elham Talebi²  Kimia Sadat Hashemi²  Behnaz Dayeri³  Maryam Motealleh⁴  Shayan Khalili Alashti⁵  Radin Dabbagh Rezaeiye⁶  Mohadese Koohi Aliabadi⁷  Mohammad Javad Ghanbary⁸  Arian Mehrara⁹ 

[1] Department of Biology and Biotechnology, Molecular Biology, and Genetics, Pavia University, Lombardi, Italy;Department of Genetics, Faculty of Advanced Science and Technology, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran;Department of Pharmaceutical Sciences, Faculty of Pharmaceutical Biotechnology, University of Milan, Milan, Italy;Department of System Biology Lab, University of Vrije Universiteit Amsterdam, Amsterdam, Netherlands;Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran;Faculty of Basic Sciences, Gonbad Kavous University, Gonbad Kavous, Iran;Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran;Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran;School of Pharmacy, Ramsar International Campus, Mazandaran University of Medical Sciences, Ramsar, Iran;

关键词: Alzheimer’s disease;    gene expression profiling;    microarray analysis;    bioinformatics;    biology;   

DOI  :  10.3389/fgene.2023.1225196

 received in 2023-05-19, accepted in 2023-08-14,  发布年份 2023

来源: Frontiers

【 摘 要 】

Background: Alzheimer’s Disease (AD) is an age-related progressive neurodegenerative disorder characterized by mental deterioration, memory deficit, and multiple cognitive abnormalities, with an overall prevalence of ∼2% among industrialized countries. Although a proper diagnosis is not yet available, identification of miRNAs and mRNAs could offer valuable insights into the molecular pathways underlying AD’s prognosis.Method: This study aims to utilize microarray bioinformatic analysis to identify potential biomarkers of AD, by analyzing six microarray datasets (GSE4757, GSE5281, GSE16759, GSE28146, GSE12685, and GSE1297) of AD patients, and control groups. Furthermore, this study conducted gene ontology, pathways analysis, and protein-protein interaction network to reveal major pathways linked to probable biological events. The datasets were meta-analyzed using bioinformatics tools, to identify significant differentially expressed genes (DEGs) and hub genes and their targeted miRNAs’.Results: According to the findings, CXCR4, TGFB1, ITGB1, MYH11, and SELE genes were identified as hub genes in this study. The analysis of DEGs using GO (gene ontology) revealed that these genes were significantly enriched in actin cytoskeleton regulation, ECM-receptor interaction, and hypertrophic cardiomyopathy. Eventually, hsa-mir-122-5p, hsa-mir-106a-5p, hsa-mir-27a-3p, hsa-mir16-5p, hsa-mir-145-5p, hsa-mir-12-5p, hsa-mir-128-3p, hsa-mir 3200-3p, hsa-mir-103a-3p, and hsa-mir-9-3p exhibited significant interactions with most of the hub genes.Conclusion: Overall, these genes can be considered as pivotal biomarkers for diagnosing the pathogenesis and molecular functions of AD.

【 授权许可】

Unknown   
Copyright © 2023 Hashemi, Aliabadi, Mehrara, Talebi, Hemmati, Rezaeiye, Ghanbary, Motealleh, Dayeri and Alashti.