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Identification of Dysregulated Mechanisms and Potential Biomarkers in Ischemic Stroke Onset
OBJECTIVE: Ischemic stroke (IS) is a major cause of severe disability. This study aimed to identify potential biomarkers closely related to IS diagnosis and treatment. METHODS: Profiles of gene expression were obtained from datasets GSE16561, GSE22255, GSE112801 and GSE110993. Differentially express...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8390889/ https://www.ncbi.nlm.nih.gov/pubmed/34456585 http://dx.doi.org/10.2147/IJGM.S327594 |
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author | Feng, Bing Meng, Xinling Zhou, Hui Chen, Liechun Zou, Chun Liang, Lucong Meng, Youshi Xu, Ning Wang, Hao Zou, Donghua |
author_facet | Feng, Bing Meng, Xinling Zhou, Hui Chen, Liechun Zou, Chun Liang, Lucong Meng, Youshi Xu, Ning Wang, Hao Zou, Donghua |
author_sort | Feng, Bing |
collection | PubMed |
description | OBJECTIVE: Ischemic stroke (IS) is a major cause of severe disability. This study aimed to identify potential biomarkers closely related to IS diagnosis and treatment. METHODS: Profiles of gene expression were obtained from datasets GSE16561, GSE22255, GSE112801 and GSE110993. Differentially expressed mRNAs between IS and controls were then subjected to weighted gene co-expression network analysis as well as multiscale embedded gene co-expression network analysis. The intersection of the two sets of module genes was subjected to analyses of functional enrichment and of microRNAs (miRNAs) regulation. Then, the area under receiver operating characteristic curves (AUC) was calculated to assess the ability of genes to discriminate IS patients from controls. IS diagnostic signatures were constructed using least absolute shrinkage and selection operator regression. RESULTS: A total of 234 common co-expression network genes were found to be potentially associated with IS. Enrichment analysis found that these genes were mainly associated with inflammation and immune response. The aberrantly expressed miRNAs (hsa-miR-651-5p, hsa-miR-138-5p, hsa-miR-9-3p and hsa-miR-374a-3p) in IS had regulatory effects on IS-related genes and were involved in brain-related diseases. We used the criterion AUC > 0.7 to screen out 23 hub genes from IS-related genes in the GSE16561 and GSE22255 datasets. We obtained an 8-gene signature (ADCY4, DUSP1, ATP5F1, DCTN5, EIF3G, ELAVL1, EXOSC7 and PPIE) from the training set of GSE16561 dataset, which we confirmed in the validation set of GSE16561 dataset and in the GSE22255 dataset. The genes in this signature were highly accurate for diagnosing IS. In addition, the 8-gene signature significantly correlated with infiltration by immune cells. CONCLUSION: These findings provide new clues to molecular mechanisms and treatment targets in IS. The genes in the signature may be candidate markers and potential gene targets for treatments. |
format | Online Article Text |
id | pubmed-8390889 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-83908892021-08-27 Identification of Dysregulated Mechanisms and Potential Biomarkers in Ischemic Stroke Onset Feng, Bing Meng, Xinling Zhou, Hui Chen, Liechun Zou, Chun Liang, Lucong Meng, Youshi Xu, Ning Wang, Hao Zou, Donghua Int J Gen Med Original Research OBJECTIVE: Ischemic stroke (IS) is a major cause of severe disability. This study aimed to identify potential biomarkers closely related to IS diagnosis and treatment. METHODS: Profiles of gene expression were obtained from datasets GSE16561, GSE22255, GSE112801 and GSE110993. Differentially expressed mRNAs between IS and controls were then subjected to weighted gene co-expression network analysis as well as multiscale embedded gene co-expression network analysis. The intersection of the two sets of module genes was subjected to analyses of functional enrichment and of microRNAs (miRNAs) regulation. Then, the area under receiver operating characteristic curves (AUC) was calculated to assess the ability of genes to discriminate IS patients from controls. IS diagnostic signatures were constructed using least absolute shrinkage and selection operator regression. RESULTS: A total of 234 common co-expression network genes were found to be potentially associated with IS. Enrichment analysis found that these genes were mainly associated with inflammation and immune response. The aberrantly expressed miRNAs (hsa-miR-651-5p, hsa-miR-138-5p, hsa-miR-9-3p and hsa-miR-374a-3p) in IS had regulatory effects on IS-related genes and were involved in brain-related diseases. We used the criterion AUC > 0.7 to screen out 23 hub genes from IS-related genes in the GSE16561 and GSE22255 datasets. We obtained an 8-gene signature (ADCY4, DUSP1, ATP5F1, DCTN5, EIF3G, ELAVL1, EXOSC7 and PPIE) from the training set of GSE16561 dataset, which we confirmed in the validation set of GSE16561 dataset and in the GSE22255 dataset. The genes in this signature were highly accurate for diagnosing IS. In addition, the 8-gene signature significantly correlated with infiltration by immune cells. CONCLUSION: These findings provide new clues to molecular mechanisms and treatment targets in IS. The genes in the signature may be candidate markers and potential gene targets for treatments. Dove 2021-08-22 /pmc/articles/PMC8390889/ /pubmed/34456585 http://dx.doi.org/10.2147/IJGM.S327594 Text en © 2021 Feng et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Feng, Bing Meng, Xinling Zhou, Hui Chen, Liechun Zou, Chun Liang, Lucong Meng, Youshi Xu, Ning Wang, Hao Zou, Donghua Identification of Dysregulated Mechanisms and Potential Biomarkers in Ischemic Stroke Onset |
title | Identification of Dysregulated Mechanisms and Potential Biomarkers in Ischemic Stroke Onset |
title_full | Identification of Dysregulated Mechanisms and Potential Biomarkers in Ischemic Stroke Onset |
title_fullStr | Identification of Dysregulated Mechanisms and Potential Biomarkers in Ischemic Stroke Onset |
title_full_unstemmed | Identification of Dysregulated Mechanisms and Potential Biomarkers in Ischemic Stroke Onset |
title_short | Identification of Dysregulated Mechanisms and Potential Biomarkers in Ischemic Stroke Onset |
title_sort | identification of dysregulated mechanisms and potential biomarkers in ischemic stroke onset |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8390889/ https://www.ncbi.nlm.nih.gov/pubmed/34456585 http://dx.doi.org/10.2147/IJGM.S327594 |
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