An integrated bioinformatics and machine learning approach to identifying biomarkers connecting parkinson's disease with purine metabolism-related genes.

ABSTRACT:

Background

Parkinson's disease (PD), a prevalent neurodegenerative disorder in the aging population, poses significant challenges in unraveling its pathogenesis and progression. A key area of investigation is the disruption of oncological metabolic networks in PD, where diseased cells display distinct metabolic profiles compared to healthy counterparts. Of particular interest are Purine Metabolism Genes (PMGs), which play a pivotal role in nucleic acid synthesis.

Methods

In this study, bioinformatics analyses were employed to identify and validate PMGs associated with PD. A set of 20 candidate PMGs underwent differential expression analysis. GSEA and GSVA were conducted to explore the biological roles and pathways of these PMGs. Lasso regression and SVM-RFE methods were appl

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12001721 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature