Bioinformatic analysis for the identification of key candidate genes and pathways in the substantia nigra in Parkinson's disease.

Parkinson's disease is one of the most common diseases in the elderly population, and the substantia nigra is generally involved in the disease process; however, the signaling pathways and related genes underlying Parkinson's disease remain unclear. This study integrated three cohorts of profile datasets to elucidate the potential key candidate genes and pathways in Parkinson's disease. The expression profiles of GSE8397, GSE20186 and GSE49036 were included 55 available substantia nigra tissue samples from individuals diagnosed with Parkinson's disease and 33 substantia nigra tissue samples from healthy controls. These samples were integrated and thoroughly analyzed. Differentially expressed genes (DEGs) were sorted, and candidate genes and pathway enrichments were analyzed. A DEG-associated protein-protein interaction network analysis was performed. 27 shared downregulated DEGs were identified from the three GSE datasets. The DEGs were clustered based on function and signaling pathway with significant enrichment analysis. 52 edges were identified from the DEG protein-protein interaction network complex, which included dopamine metabolism, nerve conduction, reduced neuronal toxicity and proliferation pathways. Using integrated bioinformatic analysis, we identified candidate genes and pathways in Parkinson's disease that could improve our understanding of underlying molecular events, which could be potential therapeutic targets for Parkinson's disease.