Seven Hub Genes Associated with Huntington's Disease and Diagnostic and Therapeutic Potentials Identified by Computational Biology.

Huntington's disease (HD) is characterized by progressive motor dysfunction and cognitive decline. Early diagnosis and new therapeutic targets are essential for effective interventions. We performed integrative analyses of mRNA profiles from three microarrays and one RNA-seq dataset from the Gene Expression Omnibus database. The datasets included were GSE8762, GSE24250, GSE45516, and GSE64810. Data pre-processing included background correction, normalization, log2 transformation, probe-to-gene symbol mapping, and differential expression analysis. We identified 80 differentially expressed genes (DEGs) based on a significance threshold ( < 0.05) and absolute log fold change (logFC) >0.65. Additionally, we conducted Gene Ontology (GO) and pathway analyses of the identified genes. Protein-protein interactions among DEGs revealed a network from which seven hub genes ( and ) were identified using the cytoHubba plugin in Cytoscape software. Two top DEGs, (up-regulated) and (up-regulated), also hold potential as therapeutic targets. Insofar as biological contextualization of the findings is concerned, the top enriched GO terms were skeletal system development, blood vessel development, and vasculature development. Molecular function terms highlighted signaling receptor binding, extracellular matrix structural constituent, and platelet-derived growth factor binding. Notably, the significant KEGG pathways included amoebiasis, the AGE-RAGE signaling pathway in diabetic complications, and the relaxin signaling pathway. In conclusion, the present computational biology integrative analyses of multiple datasets discovered new DEGs and seven hub genes, shedding light on molecular mechanisms of HD. These findings call for translational clinical omics research and may potentially lead to future precision medicine interventions and novel diagnostic biomarkers and therapeutic targets.