Identification of Potential Genomic Biomarkers for Parkinson's Disease Using Data Pooling of Gene Expression Microarrays

In this study, we aimed to identify potential diagnostic biomarkers Parkinson's disease (PD) by exploring microarray gene expression data of PD patients. Differentially expressed genes associated with PD were screened from the GSE99039 dataset using weighted gene co-expression network analysis, followed by gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, gene-gene interaction network analysis and receiver operator characteristics analysis. We identified two PD-associated modules, in which genes from the chemokine signaling pathway were primarily enriched. In particular, , ,  and directly interacted with known PD-associated genes and showed higher expression in the PD samples, and may thus be potential biomarkers in PD diagnosis. A DFG-analysis identified a four-gene panel (, , , ) as a potential diagnostic predictor to diagnose PD.