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. 2021 Dec 13:2021:6745282.
doi: 10.1155/2021/6745282. eCollection 2021.

Recognition of the Possible miRNA-mRNA Controlling Network in Stroke by Bioinformatics Examination

Wei Li  1 , Jian Li  2 , Yong Yang  1
Affiliations

Recognition of the Possible miRNA-mRNA Controlling Network in Stroke by Bioinformatics Examination

Wei Li et al. Comput Math Methods Med. .

Retraction in

Abstract

Background: Based on the latest research of WHO, it has been revealed that more than 15 million people suffer from stroke every year worldwide. Of these 15 million people, 6 million succumb to death, and 5 million get permanently disabled. This is the prime reason for the substantial economic burden on all parts of the world.

Methods: These data have been obtained from the GEO database, and the GEO2R tool was used to find out the differentially expressed miRNAs (DEMs) between the stroke and normal patients' blood. FunRich and miRNet were considered to find potential upstream transcription factors and downstream target genes of candidate EMRs. Next, we use GO annotation and KEGG pathway enrichment. Target genes were analyzed with the help of the R software. Then, the STRING database and Cytoscape software were used to conduct PPI and DEM-hub gene networks. Finally, GSE58294 was used to estimate the hub gene expressions.

Results: Six DEMs in total were selected out from GSE95204 and GSE117064 datasets. 663 DEMs' target genes were predicted, and NRF1, EGR1, MYC, YY1, E2F1, SP4, and SP1 were predicted as an upstream transcription factor for DEMs' target genes. Target genes of DEMs were primarily augmented in the PI3K-Akt signaling pathway and p53 signaling pathway. The network construction of DEM hygiene is potentially modulated by hsa-miR-3591-5p, hsa-miR-548as-3p, hsa-miR-206, and hsa-miR-4503 hub genes which were found among the top 10 of the hub genes. Among the top 10 hub genes, justification of CTNNB1, PTEN, ESR1, CCND1, KRAS, AKT1, CCND2, CDKN1B, and MYCN was constant with that in the GSE58294 dataset.

Conclusion: In summary, our research first constructs the miRNA-mRNA network in stroke, which probably renders an awakening purview into the pathogenesis and cure of stroke.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Scanning differentially expressed miRNAs (DEMs). (a) DEMs in GSE95204. (b) DEMs in GSE117064. (c) Venn diagram representing the intersection of DEMs in the 2 datasets (GSE95204 and GSE117064). ∣LogFC | >1 and adjusted P value < 0.05 were set at the gateway to screen DEMs.
Figure 2
Figure 2
Prediction of possible earmark genes of DEMs and upstream transcription factors of target genes. (a) The miRNA-target gene network for DEMs. (b) Transcription factors of DEMs.
Figure 3
Figure 3
GO and KEGG annotation: analysis of the target genes of DEMs. (a) Biological process examination for the earmark genes of DEMs. (b) Cellular component earmark for the selected genes of DEMs. (c) Molecular function examination of the selected genes of DEMs. (d) KEGG examination of the prior genes of DEMs.
Figure 4
Figure 4
Hub genes for DEMs in the PPI matrix and miRNA-hub gene regulatory network. (a) PPI matrix of the prior 30 hub genes for DEMs. (b) miRNA-hub gene authorization matrix of the top 10 hub genes.
Figure 5
Figure 5
The mRNA articulation level of the top 10 hub genes was determined in the GSE58294 dataset. (a) CTNNB1. (b) PTEN. (c) KRAS. (d) CCND2. (e) CCKN1B. (f) MYCN. (g) ESR1. (h) CCND1. (i) AKT1. (j) VEGFA.
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