Identification of key genes and signaling pathway in the pathogenesis of Huntington's disease via bioinformatics and next generation sequencing data analysis

Background Huntington's disease (HD) could cause progressive motor deficits, psychiatric symptoms, and cognitive impairment. With the increasing use of pharmacotherapies theoretically target neurotransmitters, the incidence of HD is still not decreasing. However, the molecular pathogenesis of HD have not been illuminate. It is momentous to further examine the molecular pathogenesis of HD. Methods The HD next generation sequencing dataset GSE105041 was downloaded from the Gene Expression Omnibus (GEO) database. Using the DESeq2 in R bioconductor package to screen differentially expressed genes (DEGs) between HD samples and normal control samples. Gene ontology (GO) term and REACTOME pathway enrichment were performed on the DEGs. Meanwhile, using the Integrated Interactions Database (IID) database and Cytoscape software to construct protein-protein interaction (PPI) network and module analysis, and identify hub genes with the highest value node degree, betweenness, stress and closeness scores. miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed and analyzed. Receiver operating characteristic curves analysis of hub genes was performed for diagnostic value of hub genes. Results We identified 958 DEGs, consisting of 479 up regulated DEGs and 479 down regulated DEGs. GO terms and REACTOME pathway enrichment analyses of DEGs were performed by g:Profiler online database and the results revealed that the DEGs were mainly enriched in multicellular organismal process, developmental process, signaling by GPCR and MHC class II antigen presentation. Network Analyzer plugin of Cytoscape was performed on the PPI network, and LRRK2, MTUS2, HOXA1, IL7R, ERBB3, EGFR, TEX101, WDR76, NEDD4L and COMT were selected as hub genes. Hsa-mir-1292-5p, hsa-mir-4521, ESRRB and SREBF1 are potential biomarkers predicted to be associated in HD. Conclusion This study investigated the key genes and signaling pathways interactions between HD and its associated complications, which might help reveal the correlation between HD and its associated complications. The current investigation results are captured by prediction, and follow-up biological experiments are enforced for further validation.