An integrative bioinformatics approach in microRNA data analytics of alzheimer’s disease

• Main Author: Chang, Siow Wee
• Format: Conference or Workshop Item
• Language: English
• Published: 2023
• Subjects: Q Science (General); QH301 Biology; RA Public aspects of medicine
• Online Access: http://eprints.um.edu.my/38236/1/AJCC2023_paper_6608.pdf; http://eprints.um.edu.my/38236/; https://www.ajcc-conf.net/

Alzheimer’s Disease (AD) is the most common type of dementia clinically recognised by cognitive function impairment. Lately, the blood-based biomarkers relating to AD are intensively investigated due to the minimum invasiveness and relatively low cost in the collection of blood samples compared to the cerebrospinal fluid in the brain. With regard to that, the study of the deregulation of microRNA (miRNA) levels in the blood of AD patients is on the rise too. In this study, data analysis was performed on the miRNA expression profiling dataset using an integrative bioinformatics approach. kNN imputation and quantile normalization were carried out as the data pre-processing step to remove outliers and reduce bias in the dataset. Differential expression analysis was performed to identify 10 significant dysregulated miRNAs using a cut-off at adjusted-pvalue <0.05 and an absolute fold change of 1.6. Subsequently, 16 pathways were determined to be involved by the selected 10 miRNA signatures, and 7 genes were predicted as the common target genes, which are Cdc42, VEGFA, NTRK3, ESR1, SH3GL2, COX-2 and E2F1. The roles of these target genes in AD were proven by literature studies. Expansion of the current work on a bigger scale of data analysis is needed to further validate and understand the mechanism of miRNAs in AD development.