Dataset Information

Integrated Analysis and Identification of Novel Biomarkers in Parkinson's Disease.

ABSTRACT: Parkinson's disease (PD) is a quite common neurodegenerative disorder with a prevalence of approximately 1:800-1,000 in subjects over 60 years old. The aim of our study was to determine the candidate target genes in PD through meta-analysis of multiple gene expression arrays datasets and to further combine mRNA and miRNA expression analyses to identify more convincing biological targets and their regulatory factors. Six included datasets were obtained from the Gene Expression Omnibus database by systematical search, including five mRNA datasets (150 substantia nigra samples in total) and one miRNA dataset containing 32 peripheral blood samples. A chip meta-analysis of five microarray data was conducted by using the metaDE package and 94 differentially expressed (DE) mRNAs were comprehensively obtained. And 19 deregulated DE miRNAs were obtained through the analysis of one miRNAs dataset by Qlucore Omics Explorer software. An interaction network formed by DE mRNAs, DE miRNAs, and important pathways was discovered after we analyzed the functional enrichment, protein-protein interactions, and miRNA targetome prediction analysis. In conclusion, this study suggested that five significantly downregulated mRNAs (MAPK8, CDC42, NDUFS1, COX4I1, and SDHC) and three significantly downregulated miRNAs (miR-126-5p, miR-19-3p, and miR-29a-3p) were potentially useful diagnostic markers in clinic, and lipid metabolism (especially non-alcoholic fatty liver disease pathway) and mitochondrial dysregulation may be the keys to biochemically detectable molecular defects. However, the role of these new biomarkers and molecular mechanisms in PD requires further experiments in vivo and in vitro and further clinical evidence.

SUBMITTER: Chi J

PROVIDER: S-EPMC6016006 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Integrated Analysis and Identification of Novel Biomarkers in Parkinson's Disease.

Chi Jieshan J Xie Qizhi Q Jia Jingjing J Liu Xiaoma X Sun Jingjing J Deng Yuanfei Y Yi Li L

Frontiers in aging neuroscience 20180618

Parkinson's disease (PD) is a quite common neurodegenerative disorder with a prevalence of approximately 1:800-1,000 in subjects over 60 years old. The aim of our study was to determine the candidate target genes in PD through meta-analysis of multiple gene expression arrays datasets and to further combine mRNA and miRNA expression analyses to identify more convincing biological targets and their regulatory factors. Six included datasets were obtained from the Gene Expression Omnibus database by ...[more]

PMID: 29967579

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Project description:Parkinson's disease (PD) is a progressive, degene-rative neurological disease, typically characterized by tremors and muscle rigidity. The present study aimed to identify differe-ntially expressed genes (DEGs) between patients with PD and healthy patients, and clarify their association with additional biological processes that may regulate factors that lead to PD. An integrated analysis of publicly available Gene Expression Omnibus datasets of PD was performed. DEGs were identified between PD and normal blood samples. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses, as well as protein‑protein interaction (PPI) networks were used to predict the functions of identified DEGs. Reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) was performed to validate the predicted expression levels of identified DEGs in whole blood samples obtained from patients with PD and normal healthy controls. A total of 292DEGs were identified between the PD and normal blood samples. Of these, 156 genes were significantly upregulated and 136 genes were significantly downregulated in PD samples following integrated analysis of four PD expression datasets. The 10 most upregulated and downregulated genes were used to construct a PPI network, where ubiquitin C‑terminal hydrolase L1 (UCHL1), 3‑phosphoinositide dependent protein kinase 1 (PDPK1) and protein kinase cAMP‑activated catalytic subunit β (PRKACB) demonstrated the highest connectivity in the network. DEGs were significantly enriched in amoebiasis, vascular smooth muscle contraction, and the Wnt and calcium signaling pathways. The expression levels of significant DEGs, UCHL1, PDPK1 and PRKACB were validated using RT‑qPCR analysis. The findings revealed that UCHL1 and PDPK1 were upregulated and PRKACB was downregulated in patients with PD when compared with normal healthy controls. In conclusion, the results indicate that the significant DEGs, including UCHL1, PDPK1 and PRKACB may be associated with the development of PD. In addition, these factors may be involved in various signaling pathways, including amoebiasis, vascular smooth muscle contraction and the Wnt and calcium signaling pathways.

Project description:BackgroundParkinson's disease (PD) is Pengfei Zhang Liwen Zhao Pengfei Zhang Liwen Zhao a common neurological disorder involving a complex relationship with immune infiltration. Therefore, we aimed to explore PD immune infiltration patterns and identify novel immune-related diagnostic biomarkers.Materials and methodsThree substantia nigra expression microarray datasets were integrated with elimination of batch effects. Differentially expressed genes (DEGs) were screened using the "limma" package, and functional enrichment was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to explore the key module most significantly associated with PD; the intersection of DEGs and the key module in WGCNA were considered common genes (CGs). The CG protein-protein interaction (PPI) network was constructed to identify candidate hub genes by cytoscape. Candidate hub genes were verified by another two datasets. Receiver operating characteristic curve analysis was used to evaluate the hub gene diagnostic ability, with further gene set enrichment analysis (GSEA). The immune infiltration level was evaluated by ssGSEA and CIBERSORT methods. Spearman correlation analysis was used to evaluate the hub genes association with immune cells. Finally, a nomogram model and microRNA-TF-mRNA network were constructed based on immune-related biomarkers.ResultsA total of 263 CGs were identified by the intersection of 319 DEGs and 1539 genes in the key turquoise module. Eleven candidate hub genes were screened by the R package "UpSet." We verified the candidate hub genes based on two validation sets and identified six (SYT1, NEFM, NEFL, SNAP25, GAP43, and GRIA1) that distinguish the PD group from healthy controls. Both CIBERSORT and ssGSEA revealed a significantly increased proportion of neutrophils in the PD group. Correlation between immune cells and hub genes showed SYT1, NEFM, GAP43, and GRIA1 to be significantly related to immune cells. Moreover, the microRNA-TFs-mRNA network revealed that the microRNA-92a family targets all four immune-related genes in PD pathogenesis. Finally, a nomogram exhibited a reliable capability of predicting PD based on the four immune-related genes (AUC = 0.905).ConclusionBy affecting immune infiltration, SYT1, NEFM, GAP43, and GRIA1, which are regulated by the microRNA-92a family, were identified as diagnostic biomarkers of PD. The correlation of these four genes with neutrophils and the microRNA-92a family in PD needs further investigation.

Dataset Information

Integrated Analysis and Identification of Novel Biomarkers in Parkinson's Disease.

Publications

Integrated Analysis and Identification of Novel Biomarkers in Parkinson's Disease.

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