A comprehensive bioinformatics approach to identify molecular signatures and key pathways for the Huntington disease

Article

Meem, Tahera Mahnaz, Khan, Umama, Mredul, Md Bazlur Rahman, Awal, Md Abdul, Rahman, Md Habibur and Khan, Md Salauddin. 2023. "A comprehensive bioinformatics approach to identify molecular signatures and key pathways for the Huntington disease." Bioinformatics and Biology Insights. 17, pp. 1-17. https://doi.org/10.1177/11779322231210098
Article Title

A comprehensive bioinformatics approach to identify molecular signatures and key pathways for the Huntington disease

ERA Journal ID200214
Article CategoryArticle
AuthorsMeem, Tahera Mahnaz, Khan, Umama, Mredul, Md Bazlur Rahman, Awal, Md Abdul, Rahman, Md Habibur and Khan, Md Salauddin
Journal TitleBioinformatics and Biology Insights
Journal Citation17, pp. 1-17
Number of Pages17
Year2023
ISSN1177-9322
Digital Object Identifier (DOI)https://doi.org/10.1177/11779322231210098
Web Address (URL)https://journals.sagepub.com/doi/full/10.1177/11779322231210098
Abstract

Huntington disease (HD) is a degenerative brain disease caused by the expansion of CAG (cytosine-adenine-guanine) repeats, which is inherited as a dominant trait and progressively worsens over time possessing threat. Although HD is monogenetic, the specific pathophysiology and biomarkers are yet unknown specifically, also, complex to diagnose at an early stage, and identification is restricted in accuracy and precision. This study combined bioinformatics analysis and network-based system biology approaches to discover the biomarker, pathways, and drug targets related to molecular mechanism of HD etiology. The gene expression profile data sets GSE64810 and GSE95343 were analyzed to predict the molecular markers in HD where 162 mutual differentially expressed genes (DEGs) were detected. Ten hub genes among them (DUSP1, NKX2-5, GLI1, KLF4, SCNN1B, NPHS1, SGK2, PITX2, S100A4, and MSX1) were identified from protein-protein interaction (PPI) network which were mostly expressed as down-regulated. Following that, transcription factors (TFs)-DEGs interactions (FOXC1, GATA2, etc), TF-microRNA (miRNA) interactions (hsa-miR-340, hsa-miR-34a, etc), protein-drug interactions, and disorders associated with DEGs were predicted. Furthermore, we used gene set enrichment analysis (GSEA) to emphasize relevant gene ontology terms (eg, TF activity, sequence-specific DNA binding) linked to DEGs in HD. Disease interactions revealed the diseases that are linked to HD, and the prospective small drug molecules like cytarabine and arsenite was predicted against HD. This study reveals molecular biomarkers at the RNA and protein levels that may be beneficial to improve the understanding of molecular mechanisms, early diagnosis, as well as prospective pharmacologic targets for designing beneficial HD treatment.

KeywordsHuntington disease; bioinformatics; system biology; biomarkers; hub genes; pathway
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020310201. Bioinformatic methods development
Byline AffiliationsKhulna University, Bangladesh
Islamic University, Bangladesh
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