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

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.