Decoding Ferroptosis in early onset of Alzheimer’s disease: A multi- risk factor and biomarker-based analysis

Title : Decoding Ferroptosis in early onset of Alzheimer’s disease: A multi- risk factor and biomarker-based analysis

Abstract:

Alzheimer’s disease (AD), a progressive neurodegenerative disorder, is driven by complex molecular mechanisms and influenced by diverse risk factors. Beyond the traditional amyloid-β and tau hypotheses, recent evidence highlights ferroptosis (an iron-dependent, lipid peroxidation driven form of cell death) as a critical contributor to AD pathogenesis. This study aimed to establish a molecular link between AD and ferroptosis using transcriptomic profiling and pathway enrichment analysis, followed by evaluation of major AD risk factors including aging, chronic alcohol use, alcohol–nicotine co-abuse, short-term memory dysfunction, comorbidities and family history. Transcriptomic datasets from GEO (GSE118553, GSE33000 and GSE157239 for AD and risk factor-specific datasets such as GSE44456, GSE20568, GSE127711, GSE110298, GSE254650 and GSE272361) were analyzed for 115 ferroptosis-associated genes identified from FerrDB, KEGG and literature. Differential expression analysis, functional enrichment (GO, KEGG, GSEA) and machine learning based modeling (LASSO, Random Forest) revealed seven key ferroptosis-related genes i.e., CYBB, FERMT1, BAX, SOD1, ACSL4, TP53 and FTH1 as consistently dysregulated and highly predictive (AUC ≥ 0.80). Gene–miRNA network analysis identified regulatory miRNAs (e.g., hsa-miR-34a, hsa-miR-34b/c, hsa-miR-125a-5p, hsa-miR-20a-5p) implicated in oxidative stress, neuroinflammation and synaptic dysfunction. Immune profiling further revealed elevated macrophage, monocyte and T-cell infiltration, suggesting ferroptosis-induced neuroinflammatory amplification. Drug repurposing highlighted glutathione and alpha-tocopherol as potential ferroptosis-targeting therapeutics. Overall, this integrative framework links ferroptosis, genetic and environmental risk factors and immune dysregulation in AD, providing novel biomarkers for early diagnosis and therapeutic intervention, warranting further experimental validation in larger cohorts.

Keywords: Alzheimer’s disease (AD), Ferroptosis, Neurodegeneration, miRNA, Biomarkers, Machine learning

Biography:

Ms. Pratibha Singh is a research scholar specializing in neurodegenerative diseases with a focus on ferroptosis-driven molecular mechanisms in Alzheimer’s disease. Her work integrates transcriptomic profiling, bioinformatics and experimental validation to uncover key gene-miRNA interactions and risk factor influences in neurodegeneration. She is passionate about developing early diagnostic biomarkers and precision therapeutics through multi-omics integration and computational biology approaches.