Identification of potential genomic biomarkers for Parkinson’s disease using data pooling of gene expression microarrays

Abstract

Aim: In this study, we aimed to identify potential diagnostic biomarkers Parkinson’s disease (PD) by exploring microarray gene expression data of PD patients. Materials & methods: 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. Results: We identified two PD-associated modules, in which genes from the chemokine signaling pathway were primarily enriched. In particular, CS, PRKCD, RHOG and VAMP2 directly interacted with known PD-associated genes and showed higher expression in the PD samples, and may thus be potential biomarkers in PD diagnosis. Conclusion: A DFG-analysis identified a four-gene panel ( CS, PRKCD, RHOG, VAMP2) as a potential diagnostic predictor to diagnose PD.