Open AccessIdentification of potential diagnostic biomarkers for Parkinson's disease
Author(s) -
Jiang Fenghua,
Wu Qianqian,
Sun Shuqian,
Bi Guanghui,
Guo Ling
Publication year - 2019
Publication title -
febs open bio
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.718
H-Index - 31
ISSN - 2211-5463
DOI - 10.1002/2211-5463.12687
Subject(s) - biomarker , microarray , disease , diagnostic biomarker , gene , medicine , microarray analysis techniques , gene expression , parkinson's disease , bioinformatics , computational biology , oncology , biology , diagnostic accuracy , genetics
The identification of biomarkers for early diagnosis of Parkinson's disease ( PD ) prior to the onset of symptoms may improve the effectiveness of therapy. To identify potential biomarkers, we downloaded microarray datasets of PD from the Gene Expression Omnibus database. Differentially expressed genes ( DEG s) between PD and normal control ( NC ) groups were obtained, and the feature selection procedure and classification model were used to identify optimal diagnostic gene biomarkers for PD . A total of 1229 genes (640 up‐regulated and 589 down‐regulated) were obtained for PD , and nine DEG s ( PTGDS , GPX 3 , SLC 25A20 , CACNA 1D , LRRN 3 , POLR 1D , ARHGAP 26 , TNFSF 14 and VPS 11 ) were selected as optimal PD biomarkers with great diagnostic value. These nine DEG s were significantly enriched in regulation of circadian sleep/wake cycle, sleep and gonadotropin‐releasing hormone signaling pathway. Finally, we examined the expression of GPX 3 , SLC 25A20 , LRRN 3 and POLR 1D in blood samples of patients with PD by qRT ‐ PCR . GPX 3 , LRRN 3 and POLR 1D exhibited the same expression pattern as in our analysis. In conclusion, this study identified nine DEG s that may serve as potential biomarkers of PD .