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

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 applied to identify hub genes and assess the diagnostic efficacy of the nine PMGs in distinguishing PD. The correlation between these hub PMGs and clinical characteristics was also explored. Validation of the expression levels of the nine identified PMGs was performed using the GSE6613 and GSE7621 datasets.

RESULTS

The study identified nine PMGs related to PD: NME7, PKM, RRM2, POLR3 C, POLA1, PDE6 C, PDE9 A, PDE11 A, and AMPD1. Biological function analysis highlighted their involvement in processes like neutrophil activation and immune response. The diagnostic potential of these nine PMGs in differentiating PD was found to be substantial.

CONCLUSIONS

This investigation successfully identified nine PMGs associated with PD, providing valuable insights into potential novel biomarkers for this condition. These findings contribute to a deeper understanding of PD's pathogenesis and may aid in monitoring its progression, offering a new perspective in the study of neurodegenerative diseases.