Abstract
Background
Parkinson's disease (PD) is a chronic, progressive neurodegenerative illness presenting motor and nonmotor symptoms. The primary pathology involves the gradual degeneration of dopaminergic neurons substantia nigra in the midbrain, causing motor dysfunction. PD is linked with changing gene expression, especially in the dopamine-dependent oxidative stress passages. Messenger RNA is a vital intermediary in gene expression, and dysregulation may exacerbate neuroinflammation and neuronal damage.
Objectives
Our objective was to identify differentially expressed mRNAs in PD through diverse analysis methods, comparing commonly utilized differential analysis techniques. This aims to enhance the understanding of underlying molecular mechanisms, elucidating affected biological significance, pathways, and processes in PD.
Methods
We employed the GEO database to investigate mRNA expressions in PD using the GSE168496 dataset. Utilizing the R-programming packages edgeR and DESeq2, we performed differential expression analysis. A Venn Diagram compares the differential expression genes (DEGs) identified by edgeR and DESeq2. Subsequently, a functional enrichment and regulatory network analysis was conducted on the significantly differing DEGs between PD and healthy controls (HC).
Results
The study used edgeR and DESeq2 to find PD with an adjusted p-value of less than 0.05 as the significance criterion. There was evidence of the downregulation of 17 genes (ALDH1A1, AGTR1, PITX3, RSPO2, and others) and the upregulation of five genes (LAMB1, MCOLN3, CYP2W1, MLPH, COL5A1). Utilizing Reactome Gene Sets, WikiPathways, and KEGG Pathway analyses unveiled biological processes associated with PD, including dopaminergic neurogenesis and synapse. Protein-protein interaction analysis highlighted 16 crucial proteins with 20 interactions, providing insights for therapeutic interventions and further research.
Conclusion
In conclusion, we identified distinct gene expression patterns, revealing five upregulated biomarkers and 17 downregulated markers in PD compared to HC. Combining differential expression analysis, pathway enrichment, network visualization, and Protein-Protein Interaction exploration provided a robust foundation for understanding the molecular intricacies of PD, offering valuable insights for future research and therapeutic development.