Investigating gene expression datasets of hippocampus tissue to discover Alzheimer's disease-associated molecular markers

Sneh Prabha; Mohd Sajad; Farah Anjum; Md Imtaiyaz Hassan; Anas Shamsi; Sonu Chand Thakur

doi:10.1177/13872877241297335

Investigating gene expression datasets of hippocampus tissue to discover Alzheimer's disease-associated molecular markers

J Alzheimers Dis. 2024 Nov 27:13872877241297335. doi: 10.1177/13872877241297335. Online ahead of print.

Authors

Sneh Prabha¹, Mohd Sajad¹, Farah Anjum², Md Imtaiyaz Hassan¹, Anas Shamsi³, Sonu Chand Thakur¹

Affiliations

¹ Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India.
² Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia.
³ Center of Medical and Bio-Allied Health Sciences Research (CMBHSR), Ajman University, Ajman, United Arab Emirates.

PMID: 39604273
DOI: 10.1177/13872877241297335

Abstract

Background: Alzheimer's disease (AD) is an advancing neurodegenerative disorder distinguished by the formation of amyloid plaques and neurofibrillary tangles in the human brain. Nevertheless, the lack of peripheral biomarkers that can detect the development of AD remains a significant limitation.

Objective: The main aim of this work was to discover the molecular markers associated with AD.

Methods: We conducted a comprehensive microarray analysis of gene expression data from hippocampus tissue in AD patients and control samples using three microarray datasets (GSE1297, GSE28146, and GSE29378) collected from Gene Expression Omnibus (GEO). The datasets were pre-processed and normalized, revealing 346 significant genes, 103 of which were upregulated and 243 downregulated. The PPI network of significant genes was constructed to detect the top 50 hub genes, which were then further analyzed using Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes pathway (KEGG), and GSEA, revealing 47 key genes involved in AD-related pathways. These key genes were then subjected to feed forward loop (FFL) motif analysis for the prediction of transcriptional factors (TFs) and microRNAs (miRNAs) mediated gene regulatory networks.

Results: The interaction of AD-associated TFs HNF4A, SPI1, EGR1, STAT3, and MYC and miRNAs hsa-miR-155-5p and hsa-miR-16-5p in the transcriptional and post-transcriptional events of 3 upregulated and 10 downregulated genes: H2AFZ, MCM3, MYO1C, AXIN1, CCND1, ETS2, MYH9, RELA, RHEB, SOCS3, TBL1X, TBP, TXNIP, and YWHAZ, respectively, has been identified. The miRNA/TF-mediated three types of the FFL motifs, i.e., miRNA-FFL, TF-FFL, and composite-FFL, were constructed, and seven common genes among these FFL were identified: CCND1, MYH9, SOCS3, RHEB, MYO1C, TXNIP, AXIN1, and TXNIP.

Conclusions: These findings may provide insights into the development of potential molecular markers for therapeutic management of AD.

Keywords: Alzheimer's disease; GSEA; feed forward loop; gene expression; microarray analysis; protein-protein interaction.