A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease
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 unk...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2023-11-01
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| Series: | Bioinformatics and Biology Insights |
| Online Access: | https://doi.org/10.1177/11779322231210098 |
| _version_ | 1797449538690088960 |
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| author | Tahera Mahnaz Meem Umama Khan Md Bazlur Rahman Mredul Md Abdul Awal Md Habibur Rahman Md Salauddin Khan |
| author_facet | Tahera Mahnaz Meem Umama Khan Md Bazlur Rahman Mredul Md Abdul Awal Md Habibur Rahman Md Salauddin Khan |
| author_sort | Tahera Mahnaz Meem |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T14:26:25Z |
| format | Article |
| id | doaj.art-0b47d201cae1488888c9e0e618ba4ed3 |
| institution | Directory Open Access Journal |
| issn | 1177-9322 |
| language | English |
| last_indexed | 2024-03-09T14:26:25Z |
| publishDate | 2023-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Bioinformatics and Biology Insights |
| spelling | doaj.art-0b47d201cae1488888c9e0e618ba4ed32023-11-28T06:33:39ZengSAGE PublishingBioinformatics and Biology Insights1177-93222023-11-011710.1177/11779322231210098A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington DiseaseTahera Mahnaz Meem0Umama Khan1Md Bazlur Rahman Mredul2Md Abdul Awal3Md Habibur Rahman4Md Salauddin Khan5Statistics Discipline, Science, Engineering & Technology School, Khulna University, Khulna, BangladeshBiotechnology & Genetic Engineering Discipline, Khulna University, Khulna, BangladeshStatistics Discipline, Science, Engineering & Technology School, Khulna University, Khulna, BangladeshElectronics and Communication Engineering Discipline, Khulna University, Khulna, BangladeshDepartment of Computer Science and Engineering, Islamic University, Kushtia, BangladeshStatistics Discipline, Science, Engineering & Technology School, Khulna University, Khulna, BangladeshHuntington 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.https://doi.org/10.1177/11779322231210098 |
| spellingShingle | Tahera Mahnaz Meem Umama Khan Md Bazlur Rahman Mredul Md Abdul Awal Md Habibur Rahman Md Salauddin Khan A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease Bioinformatics and Biology Insights |
| title | A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease |
| title_full | A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease |
| title_fullStr | A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease |
| title_full_unstemmed | A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease |
| title_short | A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease |
| title_sort | comprehensive bioinformatics approach to identify molecular signatures and key pathways for the huntington disease |
| url | https://doi.org/10.1177/11779322231210098 |
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