Identification of potential blood biomarkers for Parkinson's disease by gene expression and DNA methylation data integration analysis

• Published in: Clinical epigenetics Vol. 11; no. 1; p. 24
• Main Authors: Wang, Changliang, Chen, Liang, Yang, Yang, Zhang, Menglei, Wong, Garry
• Format: Journal Article
• Language: English
• Published: Germany BioMed Central Ltd 11.02.2019; BioMed Central; BMC
• Subjects: Algorithms; Biochemistry; Biological markers; Biomarkers; Biomarkers - blood; Blood; Case-Control Studies; Chromosome Mapping; CpG Islands; Data collection; Data integration; Data processing; Deoxyribonucleic acid; DNA; DNA Methylation; Dopamine; Epigenesis, Genetic; Epigenetic inheritance; Epigenetics; Female; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Gene set enrichment analysis; Genes; Genetic aspects; Genomes; Humans; Identification; Integration; Male; Medical research; Methylation; Movement disorders; Neurodegenerative diseases; Parkinson disease; Parkinson Disease - genetics; Parkinson's disease; Phosphates; Sequence Analysis, DNA - methods; Stem cells; DNA methylation; Gene expression; Data integration; Parkinson’s disease
• DOI: 10.1186/s13148-019-0621-5
• ISSN: 1868-7075; 1868-7083

Blood-based gene expression or epigenetic biomarkers of Parkinson's disease (PD) are highly desirable. However, accuracy and specificity need to be improved, and methods for the integration of gene expression with epigenetic data need to be developed in order to make this feasible. Whole blood gene expression data and DNA methylation data were downloaded from Gene Expression Omnibus (GEO) database. A linear model was used to identify significantly differentially expressed genes (DEGs) and differentially methylated genes (DMGs) according to specific gene regions 5'-C-phosphate-G-3' (CpGs) or all gene regions CpGs in PD. Gene set enrichment analysis was then applied to DEGs and DMGs. Subsequently, data integration analysis was performed to identify robust PD-associated blood biomarkers. Finally, the random forest algorithm and a leave-one-out cross validation method were performed to construct classifiers based on gene expression data integrated with methylation data. Eighty-five (85) significantly hypo-methylated and upregulated genes in PD patients compared to healthy controls were identified. The dominant hypo-methylated regions of these genes were significantly different. Some genes had a single dominant hypo-methylated region, while others had multiple dominant hypo-methylated regions. One gene expression classifier and two gene methylation classifiers based on all or dominant methylation-altered region CpGs were constructed. All have a good prediction power for PD. Gene expression and methylation data integration analysis identified a blood-based 53-gene signature, which could be applied as a biomarker for PD.