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. 2023 Mar 23;18(3):e0283617.
doi: 10.1371/journal.pone.0283617. eCollection 2023.

Integrative identification of hub genes in development of atrial fibrillation related stroke

Kai Huang  1 , Xi Fan  1 , Yuwen Jiang  1 , Sheng Jin  2 , Jiechun Huang  1 , Liewen Pang  1 , Yiqing Wang  1 , Yuming Wu  2 , Xiaotian Sun  1
Affiliations

Integrative identification of hub genes in development of atrial fibrillation related stroke

Kai Huang et al. PLoS One. .

Abstract

Background: As the most common arrhythmia, atrial fibrillation (AF) is associated with a significantly increased risk of stroke, which causes high disability and mortality. To date, the underlying mechanism of stroke occurring after AF remains unclear. Herein, we studied hub genes and regulatory pathways involved in AF and secondary stroke and aimed to reveal biomarkers and therapeutic targets of AF-related stroke.

Methods: The GSE79768 and GSE58294 datasets were used to analyze AF- and stroke-related differentially expressed genes (DEGs) to obtain a DEG1 dataset. Weighted correlation network analysis (WGCNA) was used to identify modules associated with AF-related stroke in GSE66724 (DEG2). DEG1 and DEG2 were merged, and hub genes were identified based on protein-protein interaction networks. Gene Ontology terms were used to analyze the enriched pathways. The GSE129409 and GSE70887 were applied to construct a circRNA-miRNA-mRNA network in AF-related stroke. Hub genes were verified in patients using quantitative real-time polymerase chain reaction (qRT-PCR).

Results: We identified 3,132 DEGs in blood samples and 253 DEGs in left atrial specimens. Co-expressed hub genes of EIF4E3, ZNF595, ZNF700, MATR3, ACKR4, ANXA3, SEPSECS-AS1, and RNF166 were significantly associated with AF-related stroke. The hsa_circ_0018657/hsa-miR-198/EIF4E3 pathway was explored as the regulating axis in AF-related stroke. The qRT-PCR results were consistent with the bioinformatic analysis.

Conclusions: Hub genes EIF4E3, ZNF595, ZNF700, MATR3, ACKR4, ANXA3, SEPSECS-AS1, and RNF166 have potential as novel biomarkers and therapeutic targets in AF-related stroke. The hsa_circ_0018657/hsa-miR-198/EIF4E3 axis could play an important role regulating the development of AF-related stroke.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flow chart of data preparation, processing, and analysis.
GSE79768 (atrial fibrillation mRNA dataset), GSE58294 (stroke mRNA dataset), GSE66724 (atrial fibrillation related stroke mRNA dataset), GSE70887 (atrial fibrillation miRNA dataset), GSE129409 (atrial fibrillation circRNA dataset). DEMis (differently expressed miRNAs), DECircs (differently expressed circRNAs), ceRNA (competitive endogenous RNA).
Fig 2
Fig 2. Identification of differentially expressed genes.
Heatmap of the top 30 differentially expressed genes based on GSE79768 (A), and at < 3 h (B), 5 h (C), 24 h (D) after stroke in GSE58294. The color intensity (from red to green) suggests the higher to lower expression.
Fig 3
Fig 3. Identification of differentially expressed genes.
Heatmap of the top 30 differentially expressed genes based on A GSE66724, B GSE70887, and C GSE129409. The color intensity (from red to green) suggests the higher to lower expression.
Fig 4
Fig 4. Sample clustering and network construction of the weighted co-expressed genes.
Clustering dendrogram of 8 AF without Stroke and 8 AF with Stroke (A). The color intensity was proportional to disease status (with or without Stroke). Analysis of the scale independence (B) and the mean connectivity (C) for various soft‑thresholding powers. The soft‑thresholding power of 5 was selected based on the scale‑free topology criterion. Dendrogram clustered was based on a dissimilarity measure (1‑TOM). Gene expression similarity is assessed by a pair‑wise weighted correlation metric and clustered based on a topological overlap metric into modules. Each color below represents one co‑expression module, and every branch stands for one gene (D). The cluster dendrogram of module eigengenes was demonstrated (E).
Fig 5
Fig 5. The identification of key modules via weighted gene co‑expression network analysis.
Heatmap of the correlation between module eigengenes and the disease status of AF-related Stroke (A). The corresponding correlation coefficient along with P‑value is given in each cell, and each cell is color‑coded by correlation according to the color (legend at right). The turquoise module was most significantly correlated with AF-related Stroke. Scatter plot of module eigengenes in the turquoise module was presented (B). The Venn diagram of genes from the key module and DEGs from GSE66724 was drawn (C).
Fig 6
Fig 6
PPI network of AF-related DEGs (A), PPI network of Stroke-related DEGs (B), and Venn diagrams of AF-related stroke genes (C) were presented. Red, greater degree. blue, lesser degree.
Fig 7
Fig 7. Enrichment analysis of key modules.
Gene ontology enrichment analysis in DEG1 (A), DEG2 (B), and DEG3 (C). The significance of enrichment gradually increases from blue to red, and the size of the dots indicates the number of genes contained in the corresponding pathway. ROC curves of hub genes in GSE66724 (D) and GSE58294 (E) were present.
Fig 8
Fig 8
Nervous and cardiovascular diseases related to hub genes based on the CTD database (A-H), circRNA-miRNA-mRNA network (I).
Fig 9
Fig 9. The expression levels of 8 hub genes, miR-198, and hsa_circ_0018657 (n = 3).
*: P < 0.05.
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Grants and funding

This work was supported by the National Natural Science Foundation of China (81601663, 81772042), Shanghai Shen Kang Clinical Research Cultivation Project (SHDC12018X18), and the National Science Foundation of Hebei Province of China (C20200206025). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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