Bioinformatic identification and experiment validation reveal 6 hub genes, promising diagnostic and therapeutic targets for Alzheimer’s disease
Abstract Background Alzheimer’s disease (AD) is a progressive neurodegenerative disease that can cause dementia. We aim to screen out the hub genes involved in AD based on microarray datasets. Methods Gene expression profiles GSE5281 and GSE28146 were retrieved from Gene Expression Omnibus database...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | BMC Medical Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12920-023-01775-6 |
| _version_ | 1827388063015239680 |
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| author | Wenyuan Cao Zhangge Ji Shoulian Zhu Mei Wang Runming Sun |
| author_facet | Wenyuan Cao Zhangge Ji Shoulian Zhu Mei Wang Runming Sun |
| author_sort | Wenyuan Cao |
| collection | DOAJ |
| description | Abstract Background Alzheimer’s disease (AD) is a progressive neurodegenerative disease that can cause dementia. We aim to screen out the hub genes involved in AD based on microarray datasets. Methods Gene expression profiles GSE5281 and GSE28146 were retrieved from Gene Expression Omnibus database to acquire differentially expressed genes (DEGs). Gene Ontology and pathway enrichment were conducted using DAVID online tool. The STRING database and Cytoscape tools were employed to analyze protein-protein interactions and identify hub genes. The predictive value of hub genes was assessed by principal component analysis and receiver operating characteristic curves. AD mice model was constructed, and histology was then observed by hematoxylin-eosin staining. Gene expression levels were finally determined by real-time quantitative PCR. Results We obtained 197 overlapping DEGs from GSE5281 and GSE28146 datasets. After constructing protein-protein interaction network, three highly interconnected clusters were identified and 6 hub genes (RBL1, BUB1, HDAC7, KAT5, SIRT2, and ITGB1) were selected. The hub genes could be used as basis to predict AD. Histological abnormalities of brain were observed, suggesting successful AD model was constructed. Compared with the control group, the mRNA expression levels of RBL1, BUB1, HDAC7, KAT5 and SIRT2 were significantly increased, while the mRNA expression level of ITGB1 was significantly decreased in AD groups. Conclusion RBL1, BUB1, HDAC7, KAT5, SIRT2 and ITGB1 are promising gene signatures for diagnosis and therapy of AD. |
| first_indexed | 2024-03-08T16:12:43Z |
| format | Article |
| id | doaj.art-c7e405645e3c48219a074d22a10bcfb8 |
| institution | Directory Open Access Journal |
| issn | 1755-8794 |
| language | English |
| last_indexed | 2024-03-08T16:12:43Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj.art-c7e405645e3c48219a074d22a10bcfb82024-01-07T12:52:45ZengBMCBMC Medical Genomics1755-87942024-01-0117111210.1186/s12920-023-01775-6Bioinformatic identification and experiment validation reveal 6 hub genes, promising diagnostic and therapeutic targets for Alzheimer’s diseaseWenyuan Cao0Zhangge Ji1Shoulian Zhu2Mei Wang3Runming Sun4Department of Neurology Second Ward, Zibo Municipal HospitalDepartment of Neurology Second Ward, Zibo Municipal HospitalDepartment of Neurology Second Ward, Zibo Municipal HospitalDepartment of Rehabilitation, Zibo Municipal HospitalDepartment of Neurology Second Ward, Zibo Municipal HospitalAbstract Background Alzheimer’s disease (AD) is a progressive neurodegenerative disease that can cause dementia. We aim to screen out the hub genes involved in AD based on microarray datasets. Methods Gene expression profiles GSE5281 and GSE28146 were retrieved from Gene Expression Omnibus database to acquire differentially expressed genes (DEGs). Gene Ontology and pathway enrichment were conducted using DAVID online tool. The STRING database and Cytoscape tools were employed to analyze protein-protein interactions and identify hub genes. The predictive value of hub genes was assessed by principal component analysis and receiver operating characteristic curves. AD mice model was constructed, and histology was then observed by hematoxylin-eosin staining. Gene expression levels were finally determined by real-time quantitative PCR. Results We obtained 197 overlapping DEGs from GSE5281 and GSE28146 datasets. After constructing protein-protein interaction network, three highly interconnected clusters were identified and 6 hub genes (RBL1, BUB1, HDAC7, KAT5, SIRT2, and ITGB1) were selected. The hub genes could be used as basis to predict AD. Histological abnormalities of brain were observed, suggesting successful AD model was constructed. Compared with the control group, the mRNA expression levels of RBL1, BUB1, HDAC7, KAT5 and SIRT2 were significantly increased, while the mRNA expression level of ITGB1 was significantly decreased in AD groups. Conclusion RBL1, BUB1, HDAC7, KAT5, SIRT2 and ITGB1 are promising gene signatures for diagnosis and therapy of AD.https://doi.org/10.1186/s12920-023-01775-6Alzheimer diseaseGene expression profilingProtein interaction mapsComputational biology |
| spellingShingle | Wenyuan Cao Zhangge Ji Shoulian Zhu Mei Wang Runming Sun Bioinformatic identification and experiment validation reveal 6 hub genes, promising diagnostic and therapeutic targets for Alzheimer’s disease BMC Medical Genomics Alzheimer disease Gene expression profiling Protein interaction maps Computational biology |
| title | Bioinformatic identification and experiment validation reveal 6 hub genes, promising diagnostic and therapeutic targets for Alzheimer’s disease |
| title_full | Bioinformatic identification and experiment validation reveal 6 hub genes, promising diagnostic and therapeutic targets for Alzheimer’s disease |
| title_fullStr | Bioinformatic identification and experiment validation reveal 6 hub genes, promising diagnostic and therapeutic targets for Alzheimer’s disease |
| title_full_unstemmed | Bioinformatic identification and experiment validation reveal 6 hub genes, promising diagnostic and therapeutic targets for Alzheimer’s disease |
| title_short | Bioinformatic identification and experiment validation reveal 6 hub genes, promising diagnostic and therapeutic targets for Alzheimer’s disease |
| title_sort | bioinformatic identification and experiment validation reveal 6 hub genes promising diagnostic and therapeutic targets for alzheimer s disease |
| topic | Alzheimer disease Gene expression profiling Protein interaction maps Computational biology |
| url | https://doi.org/10.1186/s12920-023-01775-6 |
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