Differential Expression Analysis of Blood MicroRNA in Identifying Potential Genes Relevant to Alzheimer’s Disease Pathogenesis, Using an Integrated Bioinformatics and Machine Learning Approach

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by cognitive and functional impairment. Recent research has focused on the deregulation of microRNAs (miRNAs) in blood as the potential biomarkers for AD. As such, a differential expression analysis of miRNAs was conducted in this study using an integrated framework that utilized the advantages of statistical and machine learning approaches. Three miRNA candidates that showed the strongest significance and correlation with each other, namely hsa-miR-6501-5p, hsa-miR-4433b-5p, and hsa-miR-143-3p, were identified. The roles and functions of the identified differentiated miRNA candidates with AD development were verified by predicting their target mRNAs, and their networks of interaction in AD pathogenesis were investigated. Pathway analysis showed that the pathways involved in contributing to the development of AD included oxidative phosphorylation, mitochondrial dysfunction, and calcium-mediated signalling. This study supports evidence that the miRNA expression changes in AD and indicates the need for further study in this area.