Alzheimer's disease (AD) is a complex neurodegenerative disorder which is multifactorial in nature. Some of its characteristics are slow cognitive decline, memory problems and behavioral changes. AD patient brains show a progressive synaptic toxicity, autophagy, neuroinflammation, excess generation of reactive oxygen species (ROS), neuronal death and oxidative stress, which occurs due to disrupted metal homeostasis along with tau and amyloid-β protein deposition. Notably, lipid peroxidation, iron buildup and elevated oxidative stress in AD brains suggest a possible molecular connection between ferroptosis and AD neurodegeneration. This study explores the genetic and bioinformatics perspective on the relationship between ferroptosis and AD aiming to identify potential therapeutic potential biomarkers using Neural network (NN) and Machine learning models. Six ferroptosis related genes were found to be differentially expressed in AD. Further machine learning analysis shortlisted four key biomarker genes. An NN-based diagnostic prediction model was developed and validated using AUC-ROC anaysis, which gave high diagnostic values (AUC- 0.92) in the analysis. The findings highlight a strong correlation between ferroptosis and altered metabolic functions in AD. miRNA-gene interaction analysis revealed that two biomarker genes, CYBB and ACSL4 can be regulated by several regulatory miRNAs i.e., hsa-miR-146-5p, hsa-miR-106b-5p, hsa-miR-223-3p, hsa-miR-155-5p, hsa-miR-34a-5p, hsa-miR-125b-5p and hsa-miR-27a-3p suggesting their potential as early diagnostic potential biomarkers. Immune microenvironment analysis revealed strong neuroinflammatory responses in AD with increased infiltration of macrophages (M0, M1 and M2), monocytes and multiple T cell subsets. This heightened immune activity may be driven by ferroptosis-induced oxidative stress contributing to neuronal death. Furthermore, druggability of these targets was evaluated and several drugs were identified that may be potentially repurposed for therapeutic intervention in AD pathogenesis. This study presents a diagnostic predictive model integrating gene expression, miRNA regulation and immune infiltration analysis, offering a novel perspective on early AD detection. The identified ferroptosis-related potential biomarkers and regulatory miRNAs could serve as valuable tools for clinical diagnosis and targeted therapeutic intervention, advancing personalized treatment strategies for Alzheimer's disease.
