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Article Open Access

Identification of differentially expressed genes and regulatory relationships in Huntington's disease by bioinformatics analysis

  • Authors:
    • Xiaoyu Dong
    • Shuyan Cong
  • View Affiliations / Copyright

    Affiliations: Department of Neurology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, P.R. China
    Copyright: © Dong et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 4317-4326
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    Published online on: January 9, 2018
       https://doi.org/10.3892/mmr.2018.8410
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Abstract

Huntington's disease (HD) is an inherited, progressive neurodegenerative disease caused by a CAG expansion in the huntingtin (HTT) gene; various dysfunctions of biological processes in HD have been proposed. However, at present the exact pathogenesis of HD is not fully understood. The present study aimed to explore the pathogenesis of HD using a computational bioinformatics analysis of gene expression. GSE11358 was downloaded from the Gene Expression Omnibus andthe differentially expressed genes (DEGs) in the mutant HTT knock‑in cell model STHdhQ111/Q111 were predicted. DEGs between the HD and control samples were screened using the limma package in R. Functional and pathway enrichment analyses were conducted using the database for annotation, visualization and integrated discovery software. A protein‑protein interaction (PPI) network was established by the search tool for the retrieval of interacting genes and visualized by Cytoscape. Module analysis of the PPI network was performed utilizing MCODE. A total of 471 DEGs were identified, including ribonuclease A family member 4 (RNASE4). In addition, 41 significantly enriched Kyoto Encyclopedia of Genes and Genomes pathways, as well as several significant Gene Ontology terms (including cytokine‑cytokine receptor interaction and cytosolic DNA‑sensing) were identified. A total of 18 significant modules were identified from the PPI network. Furthermore, a novel transcriptional regulatory relationship was identified, namely signal transducer and activator of transcription 3 (STAT3), which is regulated by miRNA‑124 in HD. In conclusion, deregulation of 18 critical genes may contribute to the occurrence of HD. RNASE4, STAT3, and miRNA‑124 may have a regulatory association with the pathological mechanisms in HD.
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Copy and paste a formatted citation
Spandidos Publications style
Dong X and Cong S: Identification of differentially expressed genes and regulatory relationships in Huntington's disease by bioinformatics analysis. Mol Med Rep 17: 4317-4326, 2018.
APA
Dong, X., & Cong, S. (2018). Identification of differentially expressed genes and regulatory relationships in Huntington's disease by bioinformatics analysis. Molecular Medicine Reports, 17, 4317-4326. https://doi.org/10.3892/mmr.2018.8410
MLA
Dong, X., Cong, S."Identification of differentially expressed genes and regulatory relationships in Huntington's disease by bioinformatics analysis". Molecular Medicine Reports 17.3 (2018): 4317-4326.
Chicago
Dong, X., Cong, S."Identification of differentially expressed genes and regulatory relationships in Huntington's disease by bioinformatics analysis". Molecular Medicine Reports 17, no. 3 (2018): 4317-4326. https://doi.org/10.3892/mmr.2018.8410
Copy and paste a formatted citation
x
Spandidos Publications style
Dong X and Cong S: Identification of differentially expressed genes and regulatory relationships in Huntington's disease by bioinformatics analysis. Mol Med Rep 17: 4317-4326, 2018.
APA
Dong, X., & Cong, S. (2018). Identification of differentially expressed genes and regulatory relationships in Huntington's disease by bioinformatics analysis. Molecular Medicine Reports, 17, 4317-4326. https://doi.org/10.3892/mmr.2018.8410
MLA
Dong, X., Cong, S."Identification of differentially expressed genes and regulatory relationships in Huntington's disease by bioinformatics analysis". Molecular Medicine Reports 17.3 (2018): 4317-4326.
Chicago
Dong, X., Cong, S."Identification of differentially expressed genes and regulatory relationships in Huntington's disease by bioinformatics analysis". Molecular Medicine Reports 17, no. 3 (2018): 4317-4326. https://doi.org/10.3892/mmr.2018.8410
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