Uncovering ferroptosis in Parkinson's disease via bioinformatics and machine learning, and reversed deducing potential therapeutic natural products.

ABSTRACT: Objective: Ferroptosis, a novel form of cell death, is closely associated with excessive iron accumulated within the substantia nigra in Parkinson's disease (PD). Despite extensive research, the underlying molecular mechanisms driving ferroptosis in PD remain elusive. Here, we employed a bioinformatics and machine learning approach to predict the genes associated with ferroptosis in PD and investigate the interactions between natural products and their active ingredients with these genes. Methods: We comprehensively analyzed differentially expressed genes (DEGs) for ferroptosis associated with PD (PDFerDEGs) by pairing 3 datasets (GSE7621, GSE20146, and GSE202665) from the NCBI GEO database and the FerrDb V2 database. A machine learning approach was then used to screen PDFerD

SUBMITTER: Wang P 

PROVIDER: S-EPMC10358855 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature