RNA Expression-Based Biomarker Discovery in Alzheimer's disease An Integrated Bioinformatics and Machine Learning Framework
Abstract
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, synaptic dysfunction, and neuroinflammation. Despite extensive research, reliable blood-based biomarkers for early detection and therapeutic targets remain limited. Methods: In this study, we conducted an integrated bioinformatics analysis using transcriptomic data from the GSE63060 cohort (249 samples) to identify differentially expressed genes (DEGs), construct protein-protein interaction (PPI) networks, and develop machine learning-based classification models. Functional enrichment, immune infiltration and drug repurposing, were performed for comprehensive validation. Results: We identified 81 significantly DEGs (45 upregulated, 36 downregulated) associated with immune activation, synaptic dysfunction, and oxidative stress. Functional enrichment revealed significant pathways including Toll-like receptor signaling, TNF signaling, and Alzheimer's disease pathways. Machine learning models (Random Forest, SVM, XGBoost, LASSO) achieved >96% AUC in the training cohort and >92% AUC in external validation (GSE63061). Key hub genes (IL1B, TNF, CD14, TLR4, TREM2) were identified as potential biomarkers. Drug repurposing analysis identified 25 candidate compounds, with Donepezil and EGCG showing the strongest binding affinities. Conclusions: This integrative framework successfully identified robust RNA expression biomarkers for Alzheimer's disease with strong diagnostic performance and biological relevance, providing novel insights for precision medicine approaches in neurodegenerative disease management.
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