Utilizing machine learning algorithms to identify biomarkers associated with Alzheimer's disease and ferroptosis-related genes.

Background: Alzheimer's disease (AD) is a complex neurodegenerative disorder that complicates our understanding of its origins. Identifying AD-specific biomarkers can reveal its mechanisms and foster the development of innovative diagnostics and therapies, aiming to unlock new ways to combat this pervasive condition.

Methods: We analyzed gene expression data using Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning (random forest, lasso regression, and SVM-REF) to differentiate AD patients from controls and explore gene functions.

Results: We identified 641 differentially expressed genes (DEGs) and 22 co-expressed genes, with functional enrichment analysis revealing their involvement in immune responses. Notably, EGR1 emerged as a potential diagnostic and therapeutic target.

Conclusion: In our study, we applied WGCNA, DEGs and diverse machine learning approaches to uncover potential biomarkers linked to Alzheimer's Disease (AD) and ferroptosis. A particular hub gene emerged as a promising candidate for novel diagnostic and therapeutic markers specifically within the context of ferroptosis in AD. This discovery sheds new light on the pathogenesis of AD, potentially facilitating the development of groundbreaking diagnostic and therapeutic techniques.