Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jan 7;12(1):225.
doi: 10.1038/s41598-021-04390-6.

Epithelial-mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysis

Yong'an Jiang #  1 , JingXing Leng #  2 , Qianxia Lin  3 , Fang Zhou  4
Affiliations

Epithelial-mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysis

Yong'an Jiang et al. Sci Rep. .

Abstract

Intracranial aneurysm (IA) can cause fatal subarachnoid hemorrhage (SAH) after rupture, and identifying patients with unruptured IAs is essential for reducing SAH fatalities. The epithelial-mesenchymal transition (EMT) may be vital to IA progression. Here, identified key EMT-related genes in aneurysms and their pathogenic mechanisms via bioinformatic analysis. The GSE13353, GSE75436, and GSE54083 datasets from Gene Expression Omnibus were analyzed with limma to identify differentially expressed genes (DEGs) among unruptured aneurysms, ruptured aneurysms, and healthy samples. The results revealed that three EMT-related DEGs (ADIPOQ, WNT11, and CCL21) were shared among all groups. Coexpression modules and hub genes were identified via weighted gene co-expression network analysis, revealing two significant modules (red and green) and 14 EMT-related genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses suggested that cytokine interactions were closely related. Gene set enrichment analysis revealed that unruptured aneurysms were enriched for the terms "inflammatory response" and "vascular endothelial growth". Protein-protein interaction analysis identified seven key genes, which were evaluated with the GSE54083 dataset to determine their sensitivity and specificity. In the external validation set, we verified the differential expression of seven genes in unruptured aneurysms and normal samples. Together, these findings indicate that FN1, and SPARC may help distinguish normal patients from patients with asymptomatic IAs.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Sample differential gene and expression analysis. (A) Epithelial–mesenchymal transition (EMT)-related differentially expressed genes (DEGs) of the ruptured and healthy groups. (B) EMT-DEGs of the ruptured and unruptured groups. (C) EMT-DEGs of the unruptured and healthy groups. (D) Common genes among the three groups (ruptured, unruptured, healthy). (E) Relative expression levels of three genes (ADIPOQ, WNT11, and CCL21) in the merged dataset samples. DEGs (differentially expressed genes), up (red): upregulated genes, down (blue): downregulated genes. NS (black): no significance. Above all figures were visualized by R software 4.0.3.
Figure 2
Figure 2
Enrichment and pathway analyses of various DEGs. (A, B) Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs (ruptured vs. healthy). (B, C) GO and KEGG analyses of DEGs (Ruptured vs. Unruptured). (D, E) GO and KEGG analyses of DEGs (unruptured vs. healthy). BP (biological process), CC (cellular component), MF (molecular function).above all figures were visualized by R software 4.0.3.
Figure 3
Figure 3
Co-expression network analysis of the top 900 EMT-related genes. (A) The scale-free fit index and average connectivity verification of weighted gene co-expression network analysis (softpower = 6). (B) When softpower = 6, the connectivity and linear relationship were visualized (R2 = 0.93, slope = − 2). (C) Based on the difference cluster analysis of consensus topology overlap, the gene dendrogram was obtained with the corresponding module color indicated by the color row. The color modules contained highly connected genes. We set a threshold of 0.25 for the dynamic cut tree merging module. Nine color modules were identified. (D) Hierarchical clustering was used to visualize the gene network (TOM plot). (E) According to Pearson’s correlation analysis, the correlations between color modules and traits were revealed (unruptured, ruptured, and healthy groups). Red represents positive correlation and blue represents negative correlation. Above all figures were visualized by R software 4.0.3.
Figure 4
Figure 4
Correlation between traits (unruptured, healthy) and genes in the module and the gene internal connection degree. (A, B) Pearson’s correlation analysis of the correlation between traits (unruptured and healthy groups) and genes in the module. (C) The degree of gene connectivity in the top 9 color modules. Above all figures were visualized by R software 4.0.3.
Figure 5
Figure 5
Gene set expression analysis (GSEA) of red and green modules and protein–protein interaction (PPI) network construction. (A) GSEA results of the red module biological processes (BP). (B) GSEA analysis of red module (Kyoto Encyclopedia of Genes and Genomes [KEGG]). (C) GSEA analysis of the green module (BP). (D) GSEA result of the green module (KEGG). (E) PPI network construction of EMT hub genes via cytoscape; the darker the color, the higher the connectivity. A–D were visualized by R software 4.0.3 and E were visualized via Cytoscape_v3.8.2.
Figure 6
Figure 6
Expression verification of EMT-related key genes. (A–G) The above-mentioned seven EMT-related genes (CDH11, SPARC, FN1, FSTL1, WNT11, PCDH9, and GPC3) expressed with relative expression trends in the GSE54083 dataset (unruptured, healthy) were visualized via Graphpad Prism 8.0.0. (H–J) Receiver operating characteristic (ROC) curves of (SPARC, FN1), and the area under the ROC curve (AUC) to evaluate the diagnostic value were visualized by R software 4.0.3.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Frösen J, Cebral J, Robertson AM, Aoki T. Flow-induced, inflammation-mediated arterial wall remodeling in the formation and progression of intracranial aneurysms. Neurosurg. Focus. 2019;47:E21. doi: 10.3171/2019.5.Focus19234. - DOI - PMC - PubMed
    1. Rinkel, G. J. Natural history, epidemiology and screening of unruptured intracranial aneurysms. J. Neuroradiol.35, 99–103. 10.1016/j.neurad.2007.11.004 (2008). - PubMed
    1. Hoh BL, et al. Stromal cell-derived factor-1 promoted angiogenesis and inflammatory cell infiltration in aneurysm walls. J. Neurosurg. 2014;120:73–86. doi: 10.3171/2013.9.Jns122074. - DOI - PMC - PubMed
    1. Maderna E, et al. Expression of vascular endothelial growth factor receptor-1/-2 and nitric oxide in unruptured intracranial aneurysms. Neurol. Sci. 2010;31:617–623. doi: 10.1007/s10072-010-0378-2. - DOI - PubMed
    1. Luo L, Hong X, Diao B, Chen S, Hei M. Sulfur dioxide attenuates hypoxia-induced pulmonary arteriolar remodeling via Dkk1/Wnt signaling pathway. Biomed. Pharmacother. 2018;106:692–698. doi: 10.1016/j.biopha.2018.07.017. - DOI - PubMed

MeSH terms