Constructing a prognostic risk model for Alzheimer's disease based on ferroptosis

Area of Science:

• Neuroscience
• Genetics
• Computational Biology

Background:

• Alzheimer's disease (AD) poses a significant challenge, necessitating improved methods for assessing disease severity.
• Ferroptosis, a regulated form of cell death, is implicated in neurodegenerative diseases.
• Gene expression patterns offer potential biomarkers for disease prognostication.

Purpose of the Study:

• To establish a prognostic risk model for Alzheimer's disease (AD) severity.
• To utilize ferroptosis-related gene expression for predicting AD progression.
• To provide a tool for clinical decision-making in AD management.

Main Methods:

• Downloaded and analyzed the GSE138260 dataset from the Gene Expression Omnibus database.
• Employed the ssGSEA algorithm for immune cell infiltration analysis and LASSO regression for optimal scoring model construction.
• Utilized Cell Counting Kit-8 and RT-qPCR for in vitro validation of gene expression changes in response to amyloid-beta (Aβ) exposure.

Main Results:

• Identified differential gene expression between control and clustered groups, selecting nine common genes for the final model.
• In vitro experiments demonstrated decreased cell survival with increasing Aβ1-42 concentrations.
• Observed specific expression pattern changes for POR and RUFY3 genes under varying Aβ1-42 concentrations.

Conclusions:

• A novel ferroptosis-based prognostic risk model for Alzheimer's disease severity has been established.
• The model leverages gene expression data to aid in predicting AD progression.
• This tool can assist clinicians in making informed decisions for improved Alzheimer's disease treatment.