Identification of Differentially Expressed microRNAs Associated with Ischemic Stroke by Integrated Bioinformatics Approaches

Jiang, Shengqiang; Wu, Jie; Geng, Yan; Zhang, Yuting; Wang, Yupeng; Wu, Jinrong; Lu, Chunqu; Luo, Guoxuan; Zan, Jie; Zhang, Yong

doi:https://doi.org/10.1155/2022/9264555

International Journal of Genomics

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Abstract Introduction Materials and Methods Results Discussion Conclusions Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 9264555 | https://doi.org/10.1155/2022/9264555

Identification of Differentially Expressed microRNAs Associated with Ischemic Stroke by Integrated Bioinformatics Approaches

Shengqiang Jiang,^1,2Jie Wu,^1,2Yan Geng,^2,3Yuting Zhang,³Yupeng Wang,³Jinrong Wu,³Chunqu Lu,⁴Guoxuan Luo,²Jie Zan,^2,3and Yong Zhang^1,2

Academic Editor: Luca Falzone

Received22 Jun 2022

Accepted11 Aug 2022

Published10 Oct 2022

Abstract

Ischemic stroke (IS) is one of the leading causes of disability and mortality worldwide. This study aims to find the crucial exosomal miRNAs associated with IS by using bioinformatics methods, reveal potential biomarkers for IS, and investigate the association between the identified biomarker and immune cell pattern in the peripheral blood of IS patients. In this study, 3 up-regulated miRNAs (hsa-miR-15b-5p, hsa-miR-184, and hsa-miR-16-5p) miRNAs in the serum exosomes between IS patients and healthy controls from GEO database (GSE199942) and 25 down-regulated genes of peripheral blood mononuclear cells of IS patients from GSE22255 were obtained with the help of the R software. GO annotation and KEGG pathway enrichment analysis showed that the 25 down-regulated genes were associated with coenzyme metabolic process and were mainly enriched in the N-glycan biosynthesis pathway. Furthermore, we performed the LASSO algorithm to narrow down the above 25 intersected genes, and identified 8 key genes which had a good diagnostic value in discriminating IS patients from the healthy controls analyzed with ROC curve. CIBERSORT algorithm indicated that the abundance of M0 macrophages and resting mast cells was significantly lower than that of the control group. The spearman correlation analysis showed that STT3A was negatively correlated with the proportion of follicular helper T cells, activated NK cells and resting dendritic cells. Finally, GSE117064 showed that has-miR-16-5p was more advantageous for diagnosing stroke. In conclusion, hsa-miR-15b-5p, hsa-miR-184, and hsa-miR-16-5p are identified as specific related exosomal miRNAs for IS patients. These genes may provide new targets for the early identification of IS.

1. Introduction

Ischemic stroke (IS) is one of the leading causes of disability and mortality worldwide and accounts for 80% of all strokes [1]. Intravenous thrombolysis is the only approved treatment strategy for acute IS by FDA [2]. Due to the short treatment time window within 4.5 h of onset and the high risk of hemorrhage, the thrombolysis rate in IS patients is very low [3], suggesting early diagnosis is extremely critical. Nowadays, clinical diagnosis of IS mainly relies on magnetic resonance imaging and computed tomography [4]. However, most community medical institutions lack the testing equipment, and the brain imaging examinations are relatively expensive, which limits the clinical diagnosis of IS [5]. Thus, developing new diagnostic markers and therapeutic targets for IS are urgently needed.

microRNAs (miRNAs) are a class of small and noncoding RNA, which regulate gene expression through silencing target genes and play important roles in the nervous system diseases, such as traumatic brain injury, spinal cord injury, subarachnoid hemorrhage, and IS [6–8]. For example, miR-31 inhibits traumatic brain injury-triggered neuronal cell apoptosis by regulating hypoxia-inducible factor-1A/vascular endothelial growth factor A axis [7]. miR-672-3p promotes functional recovery in rats with contusive spinal cord injury by inhibiting ferroptosis suppressor protein 1 [8]. Nowadays, more and more studies identify that some aberrant expressed circulating miRNAs in the serum, which are identified via bioinformatics methods, could be used as potential candidates for disease diagnosis. For example, Hsa-miR-484, hsa-miR-185-5p, hsa-miR-340-5p, hsa-miR-146a-5p, and hsa-miR-195-5p have a prognostic value in breast cancer patients treated with integrative interventions, including diet and physical activity [9]. Specific alteration of 20 miRNAs has a direct association with pesticide exposure and the development of neurodegenerative diseases [10]. Hsa-miR-181a-3p, hsa-miR-214-3p, hsa-miR-18a-5p, and hsa-miR-938 are positively related to the IL-6 signaling pathway activation in various cancers [11]. 14 potentially important miRNAs are identified as IS diagnostic signatures via bioinformatics method combined with logistic regression analysis [12]. Whereas circulating miRNAs in the serum are generally affected by the different pathophysiological conditions and circulating ribonucleases [13], there are still short of reliable prognostic or diagnostic blood biomarkers.

Exosomes are spherical extracellular nanovesicles with a bilayer lipid structure for the outer membrane, defined by a diameter of 30–100 nm [14]. Exosomes contain proteins, mRNAs, and miRNAs and play essential roles in intercellular communication [15]. Due to their high stability, serum exosomal miRNAs are considered to be powerful non-invasive biomarkers in many diseases, including breast cancer, pancreatic ductal adenocarcinoma, and hepatocellular carcinoma [16–18]. Recently, Tong et al. [19] found that miR-151a-5p, miR-24, mir-485-5p, mir-331-5p, and mir-214 were upregulated with statistical significance in both the serum exosome and cerebrospinal fluid exosomes, and may be considered as a biomarker for the diagnosis of Parkinson’s disease.

In this study, we attempt to identify the crucial exosomal miRNAs associated with IS by using bioinformatics methods, reveal potential biomarkers for IS, and investigate the association between the identified biomarker and immune cell infiltration in IS.

2. Materials and Methods

2.1. Data Sources

The data were downloaded from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo). The inclusion and exclusion criteria for these data sets were as follows. Inclusion criteria: (i) data sets containing miRNA and mRNA expression levels of IS patients; (ii) data sets reporting miRNAs expression levels of both IS patient and healthy patient; and (iii) data sets containing miRNA expression data of at least 5 IS samples and 5 controls. Exclusion criteria: (i) data sets containing exclusively IS sample and (ii) data sets containing miRNAs expression levels of animal models, cell lines or other in vitro experiments.

GSE199942 included the miRNA expression profiles of serum exosomes in five acute IS patients and five healthy controls detected by RNA-seq using llumina HiSeqTM 2500. GSE22255 included the gene expression profiling in peripheral blood mononuclear cells (PBMCs) of 20 IS patients and 20 sex- and age-matched controls using Affymetrix microarrays. GSE16561 included the gene expression profiles of peripheral whole blood RNA from 39 IS patients and 24 healthy controls. GSE117064 included the microRNA profiles of 1785 samples, which consist of 173 of CVD patients, 1612 of non-CVD control using microarrays. PMC8702168 included the miRNA expression profiles of human stroke brain tissue in five acute IS patients and three non-stroke controls detected by RNA-seq using Illumina NextSeq 500. All the data sets used in this study could be available on GEO data sets.

2.2. Analysis of Differentially Expressed miRNAs and Genes

The differentially expressed miRNAs from GSE199942 were obtained using the edgeR package [20]. The differentially expressed genes from GSE22255 were obtained using the limma package [21]. The P value <0.05 was used as threshold for nominally significant differential expression. gplot2 and pheatmap packages were used to draw the volcanic maps and heat maps, respectively.

2.3. Sankey Diagram of the miRNAs-mRNAs Network

The miRNAs-mRNAs network was drawn using the “ggalluvial” package of R software.

2.4. GO and KEGG Analysis

Gene ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were performed using the “clusterProfiler” R package [22]. P value <0.05 was considered the criterion for statistical significance.

2.5. Evaluation of Immune Cell Abundance

The gene expression profiling of GSE22255 was used to compare immune cell abundance in the peripheral blood of 20 IS patients and 20 healthy controls. The “CIBERSORT” algorithm was used to calculate the relative proportions of 22 types of immune cells [23]. Significant alterations in immune cells were identified using the Wilcoxon test at . The “ggcorrplot” package was used to visualize the results of correlation between immune cells.

2.6. Diagnostic Value of Differentially Expressed Genes in IS

The receiver operating characteristic (ROC) curve analysis is often used to determine the best diagnostic threshold for a diagnostic method. Generally, the area under the curve (AUC) value of >0.7 obtained from ROC curve analysis is considered to have a good predictive value [24].

3. Results

3.1. Identification of Differentially Expressed Exosomal miRNAs Related to IS

In this study, we analyzed the differentially expressed miRNAs in serum exosomes between IS patients and healthy controls from GSE199942 data sets, and found that 35 upregulated and 24 downregulated miRNAs (Table 1). The expression of these miRNAs is shown in Figures 1(a) and 1(b). Furthermore, the differentially expressed serum exosomes miRNAs were intersected with those differentially expressed miRNAs from freshly removed human stroke brain tissue [25], and three upregulated miRNAs were obtained: hsa-miR-15b-5p, hsa-miR-184, and hsa-miR-16-5p (Figures 1(c) and 1(d)).

(a)

(b)

(c)

(d)

3.2. Construction of miRNA-mRNA Network

Next, we analyzed the potential targets of the three differentially expressed serum microvesicles miRNAs by miRWalk, and found a total of 593 genes may be co-regulated by at least two of the three miRNAs, of which 192 genes were co-targeted by hsa-miR-15b-5p and hsa-miR-16-5p, 250 genes were co-targeted by hsa-miR-15b-5p and hsa-miR-184, 92 genes were co-targeted by hsa-miR-16-5p and hsa-miR-184, and 59 genes were co-targeted by the three miRNAs (Figures 2(a) and 2(b) and Table 2). Given that miRNA negatively regulates the expressions of its targets, we further intersected the 593 prediction genes with the downregulated-expressed genes of PBMCs of IS patients from GSE22255 [26], and obtained 25 genes (Figure 2(c)). The expression of the 25 intersected genes was shown in Figure 2(d). Then, the network containing 3 miRNAs and 25 mRNAs was visualized by the “ggallouvial” package of R software and was shown in Figure 3.

(a)

(b)

(c)

(d)

3.3. Enrichment Analysis of Differentially Expressed Genes

To explore the potential biological functions of the above downregulated-expressed 25 mRNAs, we performed GO annotation and KEGG pathway enrichment analysis. The enriched GO annotation included oxidoreduction coenzyme metabolic process, pyridine-containing compound metabolic process, and nicotinamide nucleotide metabolic process in the biological process (BP) category. Sin3 complex, oligosaccharyltransferase complex, and tetraspanin-enriched microdomain were included in the cellular component (CC) category. NAD+ kinase activity, oxidoreductase activity, and beta-1,3-galactosyltransferase activity were included in the molecular function (MF) category (Figure 4(a)). KEGG pathway analysis showed these genes were mainly involved in various types of N-glycan biosynthesis, nicotinate and nicotinamide metabolism, and pentose phosphate pathway (Figure 4(b)).

(a)

(b)

3.4. PPI Network Construction and Validation of Predictive Feature Biomarkers

We further analyzed the protein-protein interaction (PPI) network of the above 25 intersected genes by Genemania and identified the RB binding protein 4 (RBBP4) had the most interactions in the network (Figure 5(a)). Furthermore, we performed the LASSO algorithm to narrow down the above 25 intersected genes and identified eight key genes related to IS (Figure 5(b)). The eight genes were BCL11A, RNLS, UBFD1, STT3A, NADK2, GPR26, PPARA, and SAMHD1. Moreover, we used ROC curve to estimate the predictive value of the eight genes, and found that all of the eight genes had a certain diagnostic value in discriminating IS patients from the healthy controls, with an AUC of 0.7 (Figure 5(C)).

(a)

(b)

(c)

3.5. Immune Cell Patterns in the Peripheral Bloods of IS

Given that peripheral immune system has been shown to play crucial roles in the evolution of ischemic brain damage [27, 28], we further compared the different immune cell patterns of the peripheral bloods between IS patients and normal controls from GSE22255. As shown in Figures 6(a) and 6(b), M0 macrophages and resting mast cells were significantly decreased in the peripheral bloods of IS patients. M1 macrophages and activated mast cells were slightly increased in the IS group but had no significant difference compared to the normal control group (Figure 6(b)). Furthermore, we analyzed the correlation among the immune cells and found that monocytes were significantly positively correlated to M2 macrophages and significantly negatively correlated to activated NK cells and activated mast cells (Figure 7(a)). Moreover, we analyzed the correlation between the immune cells and the eight variables related to IS and found that STT3A was significantly negative-correlated to follicular helper T cells, activated NK cells and resting dendritic cells (Figure 7(b)).

(a)

(b)

(a)

(b)

3.6. Verification of the Diagnostic Efficacy of the Key miRNAs

GSE117064 were included in this study as the verification series to validate the diagnostic efficacy of the above three miRNAs. Has-miR-16-5p was more advantageous for diagnosing stroke than the other two miRNAs, as shown in Figure 8.

4. Discussion

Stroke causes great burden on human health and the economy. Although prominent improvements in the early diagnosis and treatment of IS in the past decade, it is still a leading cause of death and disability. Thus, identifying new biomarkers for the early diagnosis of IS is essential. Recently, serum glutamic oxaloacetic transaminase is reported to may be utilized as predictor in detection of early neurological deterioration in acute IS [29]. Here, we first used the GEO data set to detect the differentially expressed exosomal miRNAs associated with IS and identified three miRNAs (hsa-miR-15b-5p, hsa-miR-184, and hsa-miR-16-5p) intersected with those differentially expressed miRNAs from freshly removed human stroke brain tissue [25]. Among the predicted target genes of the three differentially expressed exosomal miRNAs, 25 mRNAs were obtained by intersecting with differentially expressed genes from GSE22255. Then, we used LASSO algorithm to identify eight key genes related to IS, and further analyzed the independent prediction ROC curve. Subsequently, we analyzed the immune cell patterns in the peripheral bloods of IS patients and the correlation between the immune cells and the eight key genes as potential biomarkers for IS.

Previous studies have reported the significance of the miRNAs, which were identified in our study. hsa-miR-15b-5p is highly expressed in the plasma of Alzheimer’s disease (AD) patients, and could distinguish AD patients from normal controls [30, 31]. Besides, hsa-miR-15b-5p in the aqueous humour of patients with diabetic macular oedema shows a fold change greater than –50 in log2 values, compared to the control group [32]. In this study, we firstly identified hsa-miR-15b-5p is highly expressed in the exosomes of IS patients. Recently, hsa-miR-15b-5p is found to regulate the proliferation and apoptosis of human vascular smooth muscle cells [33]. However, the specific mechanism of action of hsa-miR-15b-5p in stroke is unclear, and needs further investigation. miR-16-5p is found to be highly expressed in the in serum and plasma of patients with colorectal cancer [34], and in the feces of patients with precancerous lesions [35]. Besides, hsa-miR-16-5p may be a good candidate for identification of predictive biomarkers of duloxetine response in patients with major depressive disorder who are responsive to duloxetine treatment [36], suggesting that hsa-miR-16-5p may play important roles in the nervous system disease. Consistently, we also found hsa-miR-16-5p is highly expressed in the serum of IS patients, and the specific role of hsa-miR-16-5p in IS needs further investigation. Hsa-miR-184 has been proposed as a biomarker for several cancers, including non-small cell lung, uterine corpus endometrial carcinoma, and oral squamous cell carcinoma [37–39], and brain-enriched hsa-miR-184 is downregulated in older adults with major depressive disorder [40]. Here, we identified that hsa-miR-184 is highly expressed in the serum of IS patients. Moreover, the co-regulated genes of the above three miRNAs were enriched in the N-glycan biosynthesis, nicotinate and nicotinamide metabolism, and pentose phosphate pathways. Interestingly, N-glycosylated immunoglobulin G has been shown to be associated with IS [41], and plasma N-glycans could as emerging biomarkers of cardiometabolic risk [42]. How the three miRNAs regulating N-glycan biosynthesis to affect the IS development would be explored in our following studies, and it could be a very interesting area.

Peripheral immune cells interplay with the central nervous system, and play important roles in the brain development, and in the process of IS injury [43, 44]. The immune system is rapidly activated after IS, and peripheral immune cells migrate and infiltrate across the blood–brain barrier into the ischemic region to affect infarction progression and prognosis. Leukocytes are found to aggregate in the ischemic region of MCAO models as early as 30 min after occlusion [45]. Yilmaz et al. fond that CD4+ and CD8+ T cells are distributed in the brain tissue 24 h after IS onset, and act detrimental effects on post-ischemic cerebral immune responses [46]. In this study, we analyzed the different immune cell patterns of the peripheral bloods between IS patients and normal controls from GSE22255, and found M0 macrophages and resting mast cells were significantly decreased in the IS group, and pro-inflammatory M1 macrophages, and activated mast cells were slight increased, suggesting that the inflammatory responses were rapidly activated after stroke. Our result is consistent with recent study that CD11b + CD45+ and CD11b + Ly6G- monocytes and macrophages were greatly increased 3 days after cardiac arrest and resuscitation [47]. Mast cells are rapidly activated and release TNF-α and histamine to aggravate brain damage after IS [48, 49]. Among the eight key genes potentially targeted by the above three miRNAs identified in our study, STT3A, a catalytic subunit of the oligosaccharyltransferase, increases amyloid-β production by promoting N-glycosylation in the pathogenesis of Alzheimer’s disease [50]. Besides, the expression level of STT3A was found to be negatively related to the biomarker expressions of M1 macrophages in breast cancer [51], indicating that STT3A may be involved in the regulating macrophage activation. Although the mechanism of regulating macrophages or mast cells has been extensively study, whether hsa-miR-15b-5p/hsa-miR-184/hsa-miR-16-5p-STT3A axis is involved in the regulating macrophages or mast activation in IS need further investigation.

This study has some limitations. For example, our analytical data are derived from public database with relatively small sample sizes. Second, important analysis results need to be further validated with clinical samples. Further large-scale basic studies can be carried out to verify the conclusions of this study.

5. Conclusions

We for the first time conducted a comprehensive bioinformatics analysis and identified three serum exosomal miRNAs (hsa-miR-15b-5p, hsa-miR-184, and hsa-miR-16-5p), which may be involved in affecting the peripheral immune cell patterns of IS patients and might serve as promising diagnostic biomarkers. However, the specific pathogenesis and molecular targets still need to be further confirmed through molecular experiments.

Data Availability

The data analyzed in the present study are publicly available on the GEO data sets database. The data sets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

There are no conflicts of interest to disclose for all authors.

Acknowledgments

The results of this study were in part derived from the GEO databases (GSE199942, GSE22255, GSE117064 and GSE16561) and from the PMC8702168. This study was supported by the Science and Technology Planning Project of Guangzhou (202102020119, and 202201020514), Guangdong Medical Research Foundation (A2021399), and Science Foundation of Guandong Second Provincial General Hospital (3D-B2020010).

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Copyright © 2022 Shengqiang Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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