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Integrated Analysis of Competitive Endogenous RNA Networks in Acute Ischemic Stroke

Background: Acute ischemic stroke (AIS) is a severe neurological disease with complex pathophysiology, resulting in the disability and death. The goal of this study is to explore the underlying molecular mechanisms of AIS and search for new potential biomarkers and therapeutic targets. Methods: Inte...

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Autores principales: Wu, Zongkai, Wei, Wanyi, Fan, Hongzhen, Gu, Yongsheng, Li, Litao, Wang, Hebo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8990852/
https://www.ncbi.nlm.nih.gov/pubmed/35401659
http://dx.doi.org/10.3389/fgene.2022.833545
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author Wu, Zongkai
Wei, Wanyi
Fan, Hongzhen
Gu, Yongsheng
Li, Litao
Wang, Hebo
author_facet Wu, Zongkai
Wei, Wanyi
Fan, Hongzhen
Gu, Yongsheng
Li, Litao
Wang, Hebo
author_sort Wu, Zongkai
collection PubMed
description Background: Acute ischemic stroke (AIS) is a severe neurological disease with complex pathophysiology, resulting in the disability and death. The goal of this study is to explore the underlying molecular mechanisms of AIS and search for new potential biomarkers and therapeutic targets. Methods: Integrative analysis of mRNA and miRNA profiles downloaded from Gene Expression Omnibus (GEO) was performed. We explored differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMirs) after AIS. Target mRNAs of DEMirs and target miRNAs of DEGs were predicted with target prediction tools, and the intersections between DEGs and target genes were determined. Subsequently, Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses, Gene set enrichment analysis (GSEA), Gene set variation analysis (GSVA), competitive endogenous RNA (ceRNA) (lncRNA-miRNA-mRNA) network, protein–protein interaction (PPI) network, and gene transcription factors (TFs) network analyses were performed to identify hub genes and associated pathways. Furthermore, we obtained AIS samples with evaluation of immune cell infiltration and used CIBERSORT to determine the relationship between the expression of hub genes and infiltrating immune cells. Finally, we used the Genomics of Drug Sensitivity in Cancer (GDSC) database to predict the effect of the identified targets on drug sensitivity. Result: We identified 293 DEGs and 26 DEMirs associated with AIS. DEGs were found to be mainly enriched in inflammation and immune-related signaling pathways through enrichment analysis. The ceRNA network included nine lncRNAs, 13 miRNAs, and 21 mRNAs. We used the criterion AUC >0.8, to screen a 3-gene signature (FBL, RPS3, and RPS15) and the aberrantly expressed miRNAs (hsa-miR-125a-5p, hsa-miR-125b-5p, hsa-miR-148b-3p, and hsa-miR-143-3p) in AIS, which were verified by a method of quantitative PCR (qPCR) in HT22 cells. T cells CD8, B cells naïve, and activated NK cells had statistical increased in number compared with the acute cerebral infarction group. By predicting the IC50 of the patient to the drug, AZD0530, Z.LLNle.CHO and NSC-87877 with significant differences between the groups were screened out. AIS demonstrated heterogeneity in immune infiltrates that correlated with the occurrence and development of diseases. Conclusion: These findings may contribute to a better understanding of the molecular mechanisms of AIS and provide the basis for the development of novel treatment targets in AIS.
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spelling pubmed-89908522022-04-09 Integrated Analysis of Competitive Endogenous RNA Networks in Acute Ischemic Stroke Wu, Zongkai Wei, Wanyi Fan, Hongzhen Gu, Yongsheng Li, Litao Wang, Hebo Front Genet Genetics Background: Acute ischemic stroke (AIS) is a severe neurological disease with complex pathophysiology, resulting in the disability and death. The goal of this study is to explore the underlying molecular mechanisms of AIS and search for new potential biomarkers and therapeutic targets. Methods: Integrative analysis of mRNA and miRNA profiles downloaded from Gene Expression Omnibus (GEO) was performed. We explored differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMirs) after AIS. Target mRNAs of DEMirs and target miRNAs of DEGs were predicted with target prediction tools, and the intersections between DEGs and target genes were determined. Subsequently, Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses, Gene set enrichment analysis (GSEA), Gene set variation analysis (GSVA), competitive endogenous RNA (ceRNA) (lncRNA-miRNA-mRNA) network, protein–protein interaction (PPI) network, and gene transcription factors (TFs) network analyses were performed to identify hub genes and associated pathways. Furthermore, we obtained AIS samples with evaluation of immune cell infiltration and used CIBERSORT to determine the relationship between the expression of hub genes and infiltrating immune cells. Finally, we used the Genomics of Drug Sensitivity in Cancer (GDSC) database to predict the effect of the identified targets on drug sensitivity. Result: We identified 293 DEGs and 26 DEMirs associated with AIS. DEGs were found to be mainly enriched in inflammation and immune-related signaling pathways through enrichment analysis. The ceRNA network included nine lncRNAs, 13 miRNAs, and 21 mRNAs. We used the criterion AUC >0.8, to screen a 3-gene signature (FBL, RPS3, and RPS15) and the aberrantly expressed miRNAs (hsa-miR-125a-5p, hsa-miR-125b-5p, hsa-miR-148b-3p, and hsa-miR-143-3p) in AIS, which were verified by a method of quantitative PCR (qPCR) in HT22 cells. T cells CD8, B cells naïve, and activated NK cells had statistical increased in number compared with the acute cerebral infarction group. By predicting the IC50 of the patient to the drug, AZD0530, Z.LLNle.CHO and NSC-87877 with significant differences between the groups were screened out. AIS demonstrated heterogeneity in immune infiltrates that correlated with the occurrence and development of diseases. Conclusion: These findings may contribute to a better understanding of the molecular mechanisms of AIS and provide the basis for the development of novel treatment targets in AIS. Frontiers Media S.A. 2022-03-25 /pmc/articles/PMC8990852/ /pubmed/35401659 http://dx.doi.org/10.3389/fgene.2022.833545 Text en Copyright © 2022 Wu, Wei, Fan, Gu, Li and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Wu, Zongkai
Wei, Wanyi
Fan, Hongzhen
Gu, Yongsheng
Li, Litao
Wang, Hebo
Integrated Analysis of Competitive Endogenous RNA Networks in Acute Ischemic Stroke
title Integrated Analysis of Competitive Endogenous RNA Networks in Acute Ischemic Stroke
title_full Integrated Analysis of Competitive Endogenous RNA Networks in Acute Ischemic Stroke
title_fullStr Integrated Analysis of Competitive Endogenous RNA Networks in Acute Ischemic Stroke
title_full_unstemmed Integrated Analysis of Competitive Endogenous RNA Networks in Acute Ischemic Stroke
title_short Integrated Analysis of Competitive Endogenous RNA Networks in Acute Ischemic Stroke
title_sort integrated analysis of competitive endogenous rna networks in acute ischemic stroke
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8990852/
https://www.ncbi.nlm.nih.gov/pubmed/35401659
http://dx.doi.org/10.3389/fgene.2022.833545
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