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Identification of significant immune-related genes for epilepsy via bioinformatics analysis
BACKGROUND: Epilepsy is one of the most common neurological disorders, but its underlying mechanism has remained obscure, and the role of immune-related genes (IRGs) in epilepsy have not yet been investigated. Therefore, in this study, we explored the association between IRGs and epilepsy. METHODS:...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8350633/ https://www.ncbi.nlm.nih.gov/pubmed/34430602 http://dx.doi.org/10.21037/atm-21-2792 |
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author | Luo, Xiaodan Xiang, Tao Huang, Hongmi Ye, Lin Huang, Yifei Wu, Yuan |
author_facet | Luo, Xiaodan Xiang, Tao Huang, Hongmi Ye, Lin Huang, Yifei Wu, Yuan |
author_sort | Luo, Xiaodan |
collection | PubMed |
description | BACKGROUND: Epilepsy is one of the most common neurological disorders, but its underlying mechanism has remained obscure, and the role of immune-related genes (IRGs) in epilepsy have not yet been investigated. Therefore, in this study, we explored the association between IRGs and epilepsy. METHODS: An IRG list was collected from the ImmPort database. The gene expression profiles of GSE143272 were collected from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Differentially expressed genes (DEGs) between epilepsy and normal samples were analyzed, and the intersections between IRGs and DEGs were identified using the VennDiagram package, with the intersected genes subjected to further analysis. Enrichment function for intersected genes were performed, constructed a protein-protein interaction (PPI) network via the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and the hub genes (top 10) of the PPI network were calculated by the cytoHubba plug-in in Cytoscape. The top correlated genes were selected to perform correlation analysis with immune cells infiltration and expression levels. Finally, we performed validation of the top correlated genes transcriptional expression levels using an animal model. RESULTS: There were a total of 245 DEGs detected in GSE143272, among which 143 were upregulated and 102 downregulated genes in epilepsy. A total of 44 differential IRGs were obtained via intersection of DEGs and IRGs. Enrichment function analysis of DEGs showed that they played a significant role in immune response. The gene CXCL1 was the most correlated with other differentially expressed IRGs via the PPI network. The results of immune cell infiltration analysis indicated that epilepsy patients had higher activated mast cells infiltration (P=0.021), but lower activated CD4 memory T cells (P=0.001), resting CD4 memory T cells (P=0.011), and gamma delta T cells (P=0.038) infiltration. It was revealed that CXCL1 and activated mast cells (R=0.25, P=0.019) and neutrophils (R=0.3, P=0.0043), and a negative correlation with T cells gamma delta (R=−0.25, P=0.018). The levels of CXCL1 expression were significantly lower in epilepsy patients than those in normal samples. CONCLUSIONS: In this study, the results showed that IRGs such as CXCL1 have a significant influence on epilepsy via regulation of immune cells infiltration. |
format | Online Article Text |
id | pubmed-8350633 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-83506332021-08-23 Identification of significant immune-related genes for epilepsy via bioinformatics analysis Luo, Xiaodan Xiang, Tao Huang, Hongmi Ye, Lin Huang, Yifei Wu, Yuan Ann Transl Med Original Article BACKGROUND: Epilepsy is one of the most common neurological disorders, but its underlying mechanism has remained obscure, and the role of immune-related genes (IRGs) in epilepsy have not yet been investigated. Therefore, in this study, we explored the association between IRGs and epilepsy. METHODS: An IRG list was collected from the ImmPort database. The gene expression profiles of GSE143272 were collected from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Differentially expressed genes (DEGs) between epilepsy and normal samples were analyzed, and the intersections between IRGs and DEGs were identified using the VennDiagram package, with the intersected genes subjected to further analysis. Enrichment function for intersected genes were performed, constructed a protein-protein interaction (PPI) network via the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and the hub genes (top 10) of the PPI network were calculated by the cytoHubba plug-in in Cytoscape. The top correlated genes were selected to perform correlation analysis with immune cells infiltration and expression levels. Finally, we performed validation of the top correlated genes transcriptional expression levels using an animal model. RESULTS: There were a total of 245 DEGs detected in GSE143272, among which 143 were upregulated and 102 downregulated genes in epilepsy. A total of 44 differential IRGs were obtained via intersection of DEGs and IRGs. Enrichment function analysis of DEGs showed that they played a significant role in immune response. The gene CXCL1 was the most correlated with other differentially expressed IRGs via the PPI network. The results of immune cell infiltration analysis indicated that epilepsy patients had higher activated mast cells infiltration (P=0.021), but lower activated CD4 memory T cells (P=0.001), resting CD4 memory T cells (P=0.011), and gamma delta T cells (P=0.038) infiltration. It was revealed that CXCL1 and activated mast cells (R=0.25, P=0.019) and neutrophils (R=0.3, P=0.0043), and a negative correlation with T cells gamma delta (R=−0.25, P=0.018). The levels of CXCL1 expression were significantly lower in epilepsy patients than those in normal samples. CONCLUSIONS: In this study, the results showed that IRGs such as CXCL1 have a significant influence on epilepsy via regulation of immune cells infiltration. AME Publishing Company 2021-07 /pmc/articles/PMC8350633/ /pubmed/34430602 http://dx.doi.org/10.21037/atm-21-2792 Text en 2021 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Luo, Xiaodan Xiang, Tao Huang, Hongmi Ye, Lin Huang, Yifei Wu, Yuan Identification of significant immune-related genes for epilepsy via bioinformatics analysis |
title | Identification of significant immune-related genes for epilepsy via bioinformatics analysis |
title_full | Identification of significant immune-related genes for epilepsy via bioinformatics analysis |
title_fullStr | Identification of significant immune-related genes for epilepsy via bioinformatics analysis |
title_full_unstemmed | Identification of significant immune-related genes for epilepsy via bioinformatics analysis |
title_short | Identification of significant immune-related genes for epilepsy via bioinformatics analysis |
title_sort | identification of significant immune-related genes for epilepsy via bioinformatics analysis |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8350633/ https://www.ncbi.nlm.nih.gov/pubmed/34430602 http://dx.doi.org/10.21037/atm-21-2792 |
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