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pmid:31089061

Identification of microRNAs and messenger RNAs involved in human umbilical cord mesenchymal stem cell treatment of ischemic cerebral infarction using integrated bioinformatics analysis.

Yin-Meng Qu · Xin Sun · Xiu-Li Yan · Hang Jin · Zhen-Ni Guo · Yi Yang

Neural regeneration research · 2019 doi:10.4103/1673-5374.255998

Paper

complete

Experiment

complete

Data

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Analysis

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Execution

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title
Identification of microRNAs and messenger RNAs involved in human umbilical cord mesenchymal stem cell treatment of ischemic cerebral infarction using integrated bioinformatics analysis.
doi
10.4103/1673-5374.255998
pmid
31089061
authors array · 6 items
[0]
Yin-Meng Qu
[1]
Xin Sun
[2]
Xiu-Li Yan
[3]
Hang Jin
[4]
Zhen-Ni Guo
[5]
Yi Yang
journal
Neural regeneration research
year
2019
abstract
In recent years, a large number of differentially expressed genes have been identified in human umbilical cord mesenchymal stem cell (hUMSC) transplants for the treatment of ischemic cerebral infarction. These genes are involved in various biochemical processes, but the role of microRNAs (miRNAs) in this process is still unclear. From the Gene Expression Omnibus (GEO) database, we downloaded two microarray datasets for GSE78731 (messenger RNA (mRNA) profile) and GSE97532 (miRNA profile). The differentially expressed genes screened were compared between the hUMSC group and the middle cerebral artery occlusion group. Gene ontology enrichment and pathway enrichment analyses were subsequently conducted using the online Database for Annotation, Visualization, and Integrated Discovery. Identified genes were applied to perform weighted gene co-suppression analyses, to establish a weighted co-expression network model. Furthermore, the protein-protein interaction network for differentially expressed genes from turquoise modules was built using Cytoscape (version 3.40) and the most highly correlated subnetwork was extracted from the protein-protein interaction network using the MCODE plugin. The predicted target genes for differentially expressed miRNAs were also identified using the online database starBase v3.0. A total of 3698 differentially expressed genes were identified. Gene ontology analysis demonstrated that differentially expressed genes that are related to hUMSC treatment of ischemic cerebral infarction are involved in endocytosis and inflammatory responses. We identified 12 differentially expressed miRNAs in middle cerebral artery occlusion rats after hUMSC treatment, and these differentially expressed miRNAs were mainly involved in signaling in inflammatory pathways, such as in the regulation of neutrophil migration. In conclusion, we have identified a number of differentially expressed genes and differentially expressed mRNAs, miRNA-mRNAs, and signaling pathways involved in the hUMSC treatment of ischemic cerebral infarction. Bioinformatics and interaction analyses can provide novel clues for further research into hUMSC treatment of ischemic cerebral infarction.
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All identified DEGs between these two groups were screened using a Student’s t-test and fold change (FC) test using the Limma package in R software (https://www.r-project.org/).
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These genes are involved in various biochemical processes, but the role of microRNAs (miRNAs) in this process is still unclear. From the Gene Expression Omnibus (GEO) database, we downloaded two microarray datasets for GSE78731 (messenger RNA (mRNA) profile) and GSE97532 (miRNA profile). The differentially expressed genes screened were compared between the hUMSC group and the middle cerebral artery occlusion group. Gene ontology enrichment and pathway enrichment analyses were subsequently conducted using the online Database for Annotation, Visualization, and Integrated Discovery. Identified genes were applied to perform weighted gene co-suppression analyses, to establish a weighted co-expression network model. Furthermore, the protein-protein interaction network for differentially expressed genes from turquoise modules was built using Cytoscape (version 3.40) and the most highly correlated subnetwork was extracted from the protein-protein interaction network using the MCODE plugin. 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