Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes

Area of Science:

• Neuroscience
• Genetics
• Bioinformatics

Background:

• Parkinson's disease (PD) pathogenesis involves complex genetic and pathway disturbances across brain regions.
• Understanding these molecular alterations is crucial for advancing PD research and potential therapeutic strategies.

Purpose of the Study:

• To identify commonly disturbed genes and pathways in Parkinson's disease brain regions.
• To uncover novel risk genes and their associated pathways implicated in PD.

Main Methods:

• Systematic analysis of 17 microarray datasets from 7 platforms.
• Weighted gene co-expression network analysis (WGCNA) to identify PD-associated gene modules.
• Functional enrichment analysis using DAVID and pathway relationship analysis.

Main Results:

• Identified a module of 736 genes significantly correlated with PD, enriched in oxidative phosphorylation pathways.
• Revealed 44 pathway pairs and 52 candidate risk genes associated with PD.
• Constructed a risk gene-pathway network, uncovering links to PD, cancers, and metabolism.

Conclusions:

• This research unveils critical disturbed pathways and risk genes in Parkinson's disease.
• The findings provide valuable insights into PD pathogenesis and potential therapeutic targets.