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. 2023 Mar 9;16(1):51.
doi: 10.1186/s12920-023-01478-y.

GAS6-AS1, a long noncoding RNA, functions as a key candidate gene in atrial fibrillation related stroke determined by ceRNA network analysis and WGCNA

Rui-Bin Li  1 , Xiao-Hong Yang  1 , Ji-Dong Zhang  1 , Wei Cui  2
Affiliations

GAS6-AS1, a long noncoding RNA, functions as a key candidate gene in atrial fibrillation related stroke determined by ceRNA network analysis and WGCNA

Rui-Bin Li et al. BMC Med Genomics. .

Abstract

Background: Stroke attributable to atrial fibrillation (AF related stroke, AFST) accounts for 13 ~ 26% of ischemic stroke. It has been found that AFST patients have a higher risk of disability and mortality than those without AF. Additionally, it's still a great challenge to treat AFST patients because its exact mechanism at the molecular level remains unclear. Thus, it's vital to investigate the mechanism of AFST and search for molecular targets of treatment. Long non-coding RNAs (lncRNAs) are related to the pathogenesis of various diseases. However, the role of lncRNAs in AFST remains unclear. In this study, AFST-related lncRNAs are explored using competing endogenous RNA (ceRNA) network analysis and weighted gene co-expression network analysis (WGCNA).

Methods: GSE66724 and GSE58294 datasets were downloaded from GEO database. After data preprocessing and probe reannotation, differentially expressed lncRNAs (DELs) and differentially expressed mRNAs (DEMs) between AFST and AF samples were explored. Then, functional enrichment analysis and protein-protein interaction (PPI) network analysis of the DEMs were performed. At the meantime, ceRNA network analysis and WGCNA were performed to identify hub lncRNAs. The hub lncRNAs identified both by ceRNA network analysis and WGCNA were further validated by Comparative Toxicogenomics Database (CTD).

Results: In all, 19 DELs and 317 DEMs were identified between the AFST and AF samples. Functional enrichment analysis suggested that the DEMs associated with AFST were mainly enriched in the activation of the immune response. Two lncRNAs which overlapped between the three lncRNAs identified by the ceRNA network analysis and the 28 lncRNAs identified by the WGCNA were screened as hub lncRNAs for further validation. Finally, lncRNA GAS6-AS1 turned out to be associated with AFST by CTD validation.

Conclusion: These findings suggested that low expression of GAS6-AS1 might exert an essential role in AFST through downregulating its downstream target mRNAs GOLGA8A and BACH2, and GAS6-AS1 might be a potential target for AFST therapy.

Keywords: Atrial fibrillation; Competing endogenous RNA; GAS6-AS1; Long non-coding RNA; Stroke; Weighted gene co‑expression network analysis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart of the study. WGCNA, weighted gene co-expression network analysis; PPI, protein-protein interaction; CTD, Comparative Toxicogenomics Database; miRNA, microRNA; lncRNA, long non-coding RNA; ceRNA, competing endogenous RNA
Fig. 2
Fig. 2
Identification DEMs and DELs in the merged dataset. A Principal component analysis plot for the merged dataset. B The volcano plot shows the upregulated and downregulated DEMs and DELs in AFST samples. The upregulated DEMs and DELs are highlighted in red, while the downregulated ones are highlighted in blue. The vertical lines represent the |FC| equals to 1.5; and the horizontal line represents the FDR equals to 0.05. C Heatmap of the top 100 DELs and DEMs. AF, atrial fibrillation; AFST, atrial fibrillation related stroke; FDR, false discovery rate; FC, fold change; DEMs, differentially expressed mRNAs; DELs, differentially expressed lncRNAs
Fig. 3
Fig. 3
The functional enrichment analysis of the DEMs. A GO enrichment analysis. B KEGG pathway enrichment analysis. In A and B, the dot color reflects the level of significance, whereas the dot size reflects the number of target genes enriched in the corresponding pathway. C Network of enriched terms analyzed by Metascape (colored by cluster ID). D Network of enriched terms analyzed by Metascape (colored by p-value). In C nodes share the same cluster ID are typically close to each other. In D, the deeper of the color, the more significant of the p-value. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEMs, differentially expressed mRNAs
Fig. 4
Fig. 4
Gene-pathway crosstalk network. The large circles represent pathways, and the size of large circles indicates the level of significance of the pathway, and the pathways are grouped according to the kappa score. The small circles represent genes, and the thickness of the lines indicates the strength of the interaction
Fig. 5
Fig. 5
Construction of Co-expression modules used WGCNA. A Sample clustering to detect outliers. The red line represents the threshold for outlier. B Soft-threshold power analysis. The left picture shows the scale free fit index for each soft-thresholding power. The right picture displays the mean connectivity for each soft-thresholding power. C Co-expression cluster dendrogram, based on TOM similarity. Each color represents one module. D Module eigengene clustering and eigengene adjacency heatmap, which shows the correlation between each module. TOM; topological overlap matrix; WGCNA, weighted gene co-expression network analysis
Fig. 6
Fig. 6
Identification of AFST related module and hub lncRNAs by WGCNA. A Heatmap of the correlation between the MEs and clinic traits. The Green module and the brown module are the most relevant modules with AFST. B Barplot of the MS across modules related to AFST. C Scatter plot between GS for AFST and the MM in brown module. D Scatter plot between GS for AFST and the MM in green module. E Scatter plot between GS for AFST and the MM in red module. F A Venn diagram of the lncRNAs identified in ceRNA network analysis and WGCNA. The overlap between lncRNAs in ceRNA network and lncRNAs with |GS|> 0.6 and |MM|> 0.5 in brown, green and red modules represent the hub lncRNAs for further validation. lncRNA, long non-coding RNA; AFST, atrial fibrillation related stroke; ME, module eigengene; MS, module significance; GS, gene significance; MM, module membership; ceRNA, competing endogenous RNA; WGCNA, weighted gene co-expression network analysis
Fig. 7
Fig. 7
Construction of the AFST-related lncRNA-miRNA-mRNA sub-ceRNA network. Rhombuses represent lncRNAs, triangles represent miRNAs and ellipses represent mRNAs, respectively. Red and blue color represent down-regulation and up-regulation, respectively. According to ceRNA theory, lncRNAs are supposed to regulate mRNAs positively, so only the genes with the same color (red) in the network are in accordance with the theoretical expectation. ceRNA, competing endogenous RNA; AFST, atrial fibrillation related stroke; lncRNA, long non-coding RNA; miRNA, microRNA; mRNA, messenger RNA
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References

    1. Zulkifly H, Lip G, Lane DA. Epidemiology of atrial fibrillation. Int J Clin Pract. 2018;72(3):e13070. doi: 10.1111/ijcp.13070. - DOI - PubMed
    1. Chugh SS, Havmoeller R, Narayanan K, Singh D, Rienstra M, Benjamin EJ, et al. Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 study. Circulation. 2014;129(8):837–847. doi: 10.1161/CIRCULATIONAHA.113.005119. - DOI - PMC - PubMed
    1. Benjamin EJ, Wolf PA, D'Agostino RB, Silbershatz H, Kannel WB, Levy D. Impact of atrial fibrillation on the risk of death: the Framingham Heart study. Circulation. 1998;98(10):946–952. doi: 10.1161/01.CIR.98.10.946. - DOI - PubMed
    1. Seiffge DJ, Werring DJ, Paciaroni M, Dawson J, Warach S, Milling TJ, et al. Timing of anticoagulation after recent ischaemic stroke in patients with atrial fibrillation. Lancet Neurol. 2019;18(1):117–126. doi: 10.1016/S1474-4422(18)30356-9. - DOI - PMC - PubMed
    1. Wolf PA, Abbott RD, Kannel WB. Atrial fibrillation as an independent risk factor for stroke: the Framingham study. Stroke. 1991;22(8):983–988. doi: 10.1161/01.STR.22.8.983. - DOI - PubMed

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