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. 2022 Feb 18:15:1715-1731.
doi: 10.2147/IJGM.S354359. eCollection 2022.

Screening for Regulatory Network of miRNA-Inflammation, Oxidative Stress and Prognosis-Related mRNA in Acute Myocardial Infarction: An in silico and Validation Study

Xunli Yin  1 , Xuebing Wang  1 , Shiai Wang  1 , Youwei Xia  2 , Huihui Chen  1 , Ling Yin  3 , Keqing Hu  4
Affiliations

Screening for Regulatory Network of miRNA-Inflammation, Oxidative Stress and Prognosis-Related mRNA in Acute Myocardial Infarction: An in silico and Validation Study

Xunli Yin et al. Int J Gen Med. .

Abstract

Background: Acute myocardial infarction (AMI), which commonly leads to heart failure, is among the leading causes of mortality worldwide. The aim of this study was to find potential regulatory network for miRNA-inflammation, oxidative stress and prognosis-related mRNA to uncover molecular mechanisms of AMI.

Methods: The expression profiles of miRNA and mRNA in the blood samples from AMI patients were downloaded from the Gene Expression Omnibus (GEO) dataset for differential expression analysis. Weighted gene co-expression network analysis (WGCNA) was used to further identify important mRNAs. The negatively regulatory network construction of miRNA-inflammation, oxidative stress and prognosis-related mRNAs was performed, followed by protein-protein interaction (PPI) and functional analysis of mRNAs.

Results: A total of three pairs of negatively regulatory network of miRNA-inflammation and prognosis-related mRNAs (hsa-miR-636/hsa-miR-491-3p/hsa-miR-188-5p/hsa-miR-188-3p-AQP9, hsa-miR-518a-3p-C5AR1 and hsa-miR-509-3-5p/hsa-miR-127-5p-PLAUR), two pairs of negatively regulatory network of miRNA-oxidative stress and prognosis-related mRNAs (hsa-miR-604-TLR4 and hsa-miR-139-5p-CXCL1) and three pairs of negatively regulatory network of miRNA-inflammation, oxidative stress and prognosis-related mRNA (hsa-miR-634/hsa-miR-591-TLR2, hsa-miR-938-NFKBIA and hsa-miR-520h/hsa-miR-450b-3p-ADM) were identified. In the KEGG analysis, some signaling pathways were identified, such as complement and coagulation cascades, pathogenic Escherichia coli infection, chemokine signaling pathway and cytokine-cytokine receptor interaction and Toll-like receptor signaling pathway.

Conclusion: Identified negatively regulatory network of miRNA-inflammation/oxidative stress and prognosis-related mRNA may be involved in the process of AMI. Those inflammation/oxidative stress and prognosis-related mRNAs may be diagnostic and prognostic biomarkers for AMI.

Keywords: acute myocardial infarction; inflammation; mRNAs; miRNAs; oxidative stress; prognosis.

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Conflict of interest statement

The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
The heat map of all miRNAs (A) and top 100 mRNAs (B) in AMI.
Figure 2
Figure 2
Modules identified by the WGCNA analysis. MRNAs in modules are marked with different colors.
Figure 3
Figure 3
(A) The heat map of the first 400 mRNA in nine modules. The horizontal and vertical axes of different colors represent different modules. The brightness of yellow in the middle represents the connectivity of different modules. (B) Correlation between modules. (C) Heat map of the relationship between modules and AMI incidence. In each unit, the upper number and lower number refers to the correlation coefficient of each module and the corresponding p value, respectively. (D) Scatter plot of modular feature mRNAs in green module.
Figure 4
Figure 4
A total of 214 pairs of negatively regulatory network of miRNA-inflammation-related mRNA. Triangle and circle represents miRNA and mRNA, respectively.
Figure 5
Figure 5
A total of 155 pairs of negatively regulatory network of miRNA-oxidative stress-related mRNA. Triangle and circle represents miRNA and mRNA, respectively.
Figure 6
Figure 6
PPI network of 116 hub mRNAs. Red and green represents up-regulation and down-regulation, respectively. The mRNA with black borders represents top 10 up-regulation/down-regulation mRNA.
Figure 7
Figure 7
The expression box diagram of AQP9 (A), TLR2 (B), TLR4 (C), CXCL1 (D), C5AR1 (E), PLAUR (F), NFKBIA (G) and ADM (H) in the GSE59867, GSE62646 and GSE123342 datasets. *p value < 0.05; **p value < 0.01; ***p value < 0.001; ****p value < 0.0001.
Figure 8
Figure 8
KEGG analysis of 116 hub mRNAs in AMI.
Figure 9
Figure 9
In vitro validation of inflammation and oxidative stress-related AQP9, TLR2, TLR4, CXCL1, C5AR1 and PLAUR. *p value < 0.05; **p value < 0.01.
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