miRNA-mRNA Interaction Network Analysis in Alzheimer's Disease for Biomarker Discovery

Area of Science:

• Neuroscience
• Genomics
• Bioinformatics

Background:

• Alzheimer's disease (AD) involves complex gene expression dysregulation.
• MicroRNAs (miRNAs) are key post-transcriptional regulators impacting neuronal function and survival.
• Disrupted miRNA-mRNA interactions are implicated in AD pathogenesis.

Purpose of the Study:

• To develop a transcriptomic framework for identifying AD-associated miRNA-mRNA regulatory signatures.
• To discover potential diagnostic biomarkers for Alzheimer's disease.
• To prioritize candidate genes and miRNAs for further investigation.

Main Methods:

• Differential gene expression analysis of transcriptomic data from AD and control samples (ADNI, GSE48552).
• Integration of miRNA-target interactions from miRTarBase.
• Construction and analysis of a miRNA-mRNA regulatory network using centrality metrics.
• Application of supervised machine learning models for classification.

Main Results:

• Identified 123 differentially expressed genes (DEGs) between AD and controls.
• Constructed a regulatory network revealing 2,104 miRNAs and 103 significant genes, highlighting PBX1 and KREMEN1 as hub genes.
• Machine learning models achieved high accuracy in distinguishing AD from control samples.

Conclusions:

• The developed framework effectively identifies AD-associated molecular signatures and potential biomarkers.
• Prioritized hub genes and miRNA-mRNA interactions offer targets for future research.
• The study provides a biologically interpretable strategy for AD biomarker discovery.