Identification of potential genomic biomarkers for Parkinson's disease using data pooling of gene expression microarrays.
Zhijian LinLishu ZhouYaosha LiSuni LiuQizhi XieXu XuJun WuPublished in: Biomarkers in medicine (2021)
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.
Keyphrases
- network analysis
- genome wide
- genome wide identification
- gene expression
- copy number
- dna methylation
- signaling pathway
- poor prognosis
- genome wide analysis
- bioinformatics analysis
- end stage renal disease
- chronic kidney disease
- ejection fraction
- binding protein
- newly diagnosed
- electronic health record
- big data
- machine learning
- magnetic resonance imaging
- prognostic factors
- computed tomography
- endoplasmic reticulum stress