ArticleMolecular medicine reports2017
Identification of therapeutic targets for Parkinson's disease via bioinformatics analysis.
Article in Molecular medicine reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Discovering promising drug candidates for Parkinson's disease: integrating miRNA and DEG analysis with molecular dynamics and MMPBSA.Journal of computer-aided molecular design · 2025Article
- Impact of potential biomarkers, SNRPE, COX7C, and RPS27, on idiopathic Parkinson's disease.Genes & genomics · 2025Article
- A Systematic Review of Extracellular Matrix-Related Alterations in Parkinson's Disease.Brain sciences · 2024Review
- Cross-disease transcriptomic analysis reveals DOK3 and PAPOLA as therapeutic targets for neuroinflammatory and tumorigenic processes.Frontiers in immunology · 2024Article
- Identification of Hub Genes and Potential Molecular Pathogenesis in Substantia Nigra in Parkinson's Disease via Bioinformatics Analysis.Parkinson's disease · 2023Article
- Electroacupuncture Inhibits Autophagy of Neuron Cells in Postherpetic Neuralgia by Increasing the Expression of miR-223-3p.BioMed research international · 2021Article
- Identification of biomarkers associated with Parkinson's disease by gene expression profiling studies and bioinformatics analysis.AIMS neuroscience · 2019Review
- MicroRNA-424 regulates cisplatin resistance of gastric cancer by targeting SMURF1 based on GEO database and primary validation in human gastric cancer tissues.OncoTargets and therapy · 2019Article
- A workflow for the integrative transcriptomic description of molecular pathology and the suggestion of normalizing compounds, exemplified by Parkinson's disease.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present study aimed to identify molecular targets that have important roles in the progression of Parkinson's disease. The gene expression profile dataset GSE7621 and the microRNA (miRNA) expression profile dataset GSE16658 were downloaded from the Gene Expression Omnibus database. R programing software was used to identify differentially expressed genes and miRNAs. Subsequently, enriched Gene Ontology terms of differentially expressed genes were obtained using the Database for Annotation, Visualization and Integrated Discovery. Target genes of differentially expressed miRNAs were identified using the starBase database and a miRNA‑gene regulatory network was constructed using Cytoscape software. A total of 391 differentially expressed genes and 88 differentially expressed miRNAs were identified. Gene Ontology terms that were associated with nervous system activity, including synapse and dopamine metabolic process, were shown to be enriched in the differentially expressed genes. A total of 620 target genes were identified from the differentially expressed miRNAs, and 10 overlaps were identified between these target genes and differentially expressed genes. Furthermore, 10 miRNA‑gene regulation pairs were obtained between the overlaps and differentially expressed miRNAs. In conclusion, the present study used bioinformatics analysis of gene and miRNA expression profile datasets, and identified potential therapeutic targets for Parkinson's disease.
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