Identification of Critical Genes and miRNAs Associated with the Development of Parkinson's Disease.
Jia LiYajuan SunJiajun ChenPublished in: Journal of molecular neuroscience : MN (2018)
The purpose of this study was to explore the key mechanism involved in the pathogenesis of Parkinson's disease (PD) based on microarray analysis. The expression profile data of GSE7621, which contained 9 substantia nigra tissues isolated from normals and 16 substantia nigra tissues isolated from PD patients, was obtained from Gene Expression Omnibus. The differentially expressed genes (DEGs) were screened, followed by functional enrichment analysis and protein-protein interaction (PPI) network construction. After the miRNAs regulating the DEGs were predicted, the miRNA-DEG regulatory network was then constructed. Besides, the 6-hydroxydopamine rat model of PD was established and the expression of key DEGs and miRNA was detected. A total of 388 DEGs were identified, including 218 upregulated genes and 170 downregulated ones. Tyrosine hydroxylase (TH) and solute carrier family 6 member 3 (SLC6A3) were significantly related to the functional terms of catecholamine biosynthetic process and dopamine biosynthetic process. TH and SLC6A3 were hub nodes in the PPI network. EBF3 could be targeted by miR-218. Moreover, TH and SLC6A3 were found downregulated in the 6-OHDA rat model of PD, while miR-218 was markedly upregulated. Our results reveal that SLC6A3, TH, and EBF3 targeted by miR-218 could be involved in PD. These molecules might provide a new insight into the development of therapeutic strategies for PD.
Keyphrases
- bioinformatics analysis
- gene expression
- protein protein
- cell proliferation
- long non coding rna
- genome wide
- long noncoding rna
- poor prognosis
- small molecule
- newly diagnosed
- end stage renal disease
- transcription factor
- metabolic syndrome
- squamous cell carcinoma
- wastewater treatment
- prognostic factors
- big data
- genome wide identification
- lymph node
- radiation therapy
- binding protein
- drug delivery
- early stage
- machine learning
- artificial intelligence
- uric acid
- single cell
- neoadjuvant chemotherapy
- drug induced