Identification of molecular targets and small drug candidates for Huntington's disease via bioinformatics and a network-based screening approach

Huntington's disease (HD) is a gradually severe neurodegenerative ailment characterised by an increase of a specific trinucleotide repeat sequence (cytosine-adenine-guanine, CAG). It is passed down as a dominant characteristic that worsens over time, creating a significant risk. Despite being monogenetic, the underlying mechanisms as well as biomarkers remain poorly understood. Furthermore, early detection of HD is challenging, and the available diagnostic procedures have low precision and accuracy. The research was conducted to provide knowledge of the biomarkers, pathways and therapeutic targets involved in the molecular processes of HD using informatic based analysis and applying network-based systems biology approaches. The gene expression profile datasets GSE97100 and GSE74201 relevant to HD were studied. As a consequence, 46 differentially expressed genes (DEGs) were identified. 10 hub genes (TPM1, EIF2S3, CCN2, ACTN1, ACTG2, CCN1, CSRP1, EIF1AX, BEX2 and TCEAL5) were further differentiated in the protein-protein interaction (PPI) network. These hub genes were typically down-regulated. Additionally, DEGs-transcription factors (TFs) connections (e.g. GATA2, YY1 and FOXC1), DEG-microRNA (miRNA) interactions (e.g. hsa-miR-124-3p and has-miR-26b-5p) were also comprehensively forecast. Additionally, related gene ontology concepts (e.g. sequence-specific DNA binding and TF activity) connected to DEGs in HD were identified using gene set enrichment analysis (GSEA). Finally, in silico drug design was employed to find candidate drugs for the treatment HD, and while the possible modest therapeutic compounds (e.g. cortistatin A, 13,16-Epoxy-25-hydroxy-17-cheilanthen-19,25-olide, Hecogenin) against HD were expected. Consequently, the results from this study may give researchers useful resources for the experimental validation of Huntington's diagnosis and therapeutic approaches.