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Integrative bioinformatics analysis of miRNA and mRNA expression profiles and identification of associated miRNA-mRNA network in intracranial aneurysms - PubMed

  • ️Mon Jan 01 2024

Integrative bioinformatics analysis of miRNA and mRNA expression profiles and identification of associated miRNA-mRNA network in intracranial aneurysms

Dongxiao Xu et al. Noncoding RNA Res. 2024.

Abstract

Background: Intracranial aneurysms (IAs) represent protrusions in the vascular wall, with their growth and wall thinning influenced by various factors. These processes can culminate in the rupture of the aneurysm, leading to subarachnoid hemorrhage (SAH). Unfortunately, over half of the patients prove unable to withstand SAH, succumbing to adverse outcomes despite intensive therapeutic interventions, even in premier medical facilities. This study seeks to discern the pivotal microRNAs (miRNAs) and genes associated with the formation and progression of IAs.

Methods: The investigation gathered expression data of miRNAs (from GSE66240) and mRNAs (from GSE158558) within human aneurysm tissue and superficial temporal artery (STA) samples, categorizing them into IA and normal groups. This classification was based on the Gene Expression Omnibus (GEO) database.

Results: A total of 70 differentially expressed microRNAs (DEMs) and 815 differentially expressed mRNAs (DEGs) were pinpointed concerning IA. Subsequently, a miRNA-mRNA network was constructed, incorporating 9 significantly upregulated DEMs and 211 significantly downregulated DEGs. Simultaneously, functional enrichment and pathway analyses were conducted on both DEMs and DEGs. Through protein-protein interaction (PPI) network analysis and functional enrichment, 9 significantly upregulated DEMs (hsa-miR-188-5p, hsa-miR-590-5p, hsa-miR-320b, hsa-miR-423-5p, hsa-miR-140-5p, hsa-miR-486-5p, hsa-miR-320a, hsa-miR-342-3p, and hsa-miR-532-5p) and 50 key genes (such as ATP6V1G1, KBTBD6, VIM, PA2G4, DYNLL1, METTL21A, MDH2, etc.) were identified, suggesting their potential significant role in IA. Among these genes, ten were notably negatively regulated by at least two key miRNAs.

Conclusions: The findings of this study provide valuable insights into the potential pathogenic mechanisms underlying IA by elucidating a miRNA-mRNA network. This comprehensive approach sheds light on the intricate interplay between miRNAs and genes, offering a deeper understanding of the molecular dynamics involved in IA development and progression.

Keywords: Aortic aneurysm; Bioinformatical analysis; Intracranial aneurysms; Regulatory network; Transcription factors; mRNA; miRNA.

© 2024 The Authors.

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Conflict of interest statement

Ozal Beylerli is an editorial board member for Non-coding RNA Research and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.

Figures

Fig. 1
Fig. 1

The workflow of the study.

Fig. 2
Fig. 2

Illustrates the expression profiles and cluster analysis of microRNAs (miRNAs) in intracranial aneurysm (IA) samples. In panel (A), a heatmap showcases the differentially expressed microRNAs (DEMs). The color scheme employs blue for IA samples and red for control samples, effectively portraying the distinct expression patterns between the two groups. In panel (B), a volcano plot represents the DEMs, emphasizing the relationship between statistical significance and fold change. Red dots signify upregulation, green dots denote downregulation, and gray dots indicate no significant differential expression. This visualization offers a clear and concise representation of the magnitude and significance of expression changes in the analyzed miRNAs.

Fig. 3
Fig. 3

Presents the expression profiles and cluster analysis of gene targets in intracranial aneurysm (IA) samples. In panel (A), a heatmap illustrates the expression values of differentially expressed genes (DEGs). The color scheme assigns red to IA samples and blue to control samples, effectively depicting the distinctive expression patterns between the two groups. In panel (B), a volcano plot showcases the DEGs, emphasizing the relationship between statistical significance and fold change. Red dots indicate upregulation, green dots signify downregulation, and gray dots denote no significant differential expression. This visual representation succinctly communicates the magnitude and significance of expression changes in the analyzed genes.

Fig. 4
Fig. 4

Illustrates the construction of the protein-protein interaction (PPI) network, showcasing the interconnected relationships between downregulated differentially expressed genes (DEGs) and the targeted 9 upregulated differentially expressed microRNAs (DEMs). In this network, nodes represent individual genes, and the lines connecting them depict the interactions between these genes. This visual representation offers insights into the complex web of interactions between the identified genes and microRNAs, providing a comprehensive overview of their regulatory relationships in the context of intracranial aneurysms (IAs).

Fig. 5
Fig. 5

Red nodes signify a strong expression level of 2 top microRNAs (miRNAs), while blue nodes signify a low level of expression levels of top their target genes.

Fig. 6
Fig. 6

Depicts the outcomes of Gene Ontology (GO) functional enrichment analysis for the identified differentially expressed microRNAs (DEMs). In panel (A), the Biological Process (BP) category highlights the involvement of DEMs in processes such as the regulation of nucleobase, nucleoside, nucleotide, and nucleic acid metabolism, as well as functions related to transport and signal transduction. Panel (B) illustrates the Cellular Component (CC) category, showcasing enrichments in various cellular locales, including the nucleus, cytoplasm, lysosome, Golgi apparatus, and exosomes. Finally, in panel (C), the Molecular Function (MF) category outlines enrichments in activities such as transcription factor activity, protein serine/threonine kinase activity, ubiquitin-specific protease activity, and transcription regulator activity. These visual representations provide a detailed insight into the diverse functional characteristics associated with the identified DEMs.

Fig. 7
Fig. 7

Illustrates the outcomes of Gene Ontology (GO) enrichment analysis for the candidate target genes. Panel (A) focuses on GO-biological process (BP), highlighting enrichments in processes such as the establishment of protein localization, cellular macromolecule localization, positive regulation of biosynthetic processes, and the organization of protein-containing complex subunits. In Panel (B), GO-cellular component (CC) terms reveal enrichments in cellular structures, including mitochondria, catalytic complexes, nuclear protein-containing complexes, and the envelope. Lastly, panel (C) delineates GO-molecular function (MF) terms, showcasing enrichments in activities such as enzyme binding, identical protein binding, ribonucleotide binding, and adenyl nucleotide binding. These visual representations provide a detailed overview of the functional characteristics associated with the candidate target genes.

Fig. 8
Fig. 8

Presents the outcomes of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis conducted for the candidate target genes. In Panel (A), a KEGG Bar plot outlines the distribution of differentially expressed genes (DEGs) across various pathways. Panel (B) presents a KEGG Bubble plot, visually representing the significance and magnitude of enrichment for DEGs within different KEGG pathways. These visualizations collectively provide insights into the pathways associated with the candidate target genes.

Fig. 9
Fig. 9

Depicts the enriched transcription factors (TFs) identified through the analysis of target genes associated with differentially expressed microRNAs (DEMs). The top 10 most significant TFs include Zic family member 1 (ZIC1), Interferon regulatory factor 1 (IRF1), Krueppel-like factor 7 (KLF7), E2F transcription factor 1 (E2F1), Pancreatic duodenal homeobox 1 (PDX1), ZFP161, CACD, Specificity protein 1 (SP1), Specificity protein 4 (SP4), and Early growth response protein 1 (EGR1) (Fig. 7A–B).

Fig. 10
Fig. 10

Schematic illustration of the role of the found (in this study) microRNAs (miRNAs) in abdominal aortic aneurysm (AAA), thoracic aortic aneurysm (TAA), and intracranial aneurysm (IA), and acute aortic dissection (AAD) and subarachnoid hemorrhage (SAH).

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References

    1. Scullen T., Mathkour M., Dumont A., Glennon S., Wang A. Intracranial aneurysms in the context of variant cerebrovascular anatomy: a review of the literature. World Neurosurg. 2022 Sep;165:58–68. doi: 10.1016/j.wneu.2022.05.127. - DOI - PubMed
    1. Tawk R.G., Hasan T.F., D'Souza C.E., Peel J.B., Freeman W.D. Diagnosis and treatment of unruptured intracranial aneurysms and aneurysmal subarachnoid hemorrhage. Mayo Clin. Proc. 2021 Jul;96(7):1970–2000. doi: 10.1016/j.mayocp.2021.01.005. - DOI - PubMed
    1. Rinkel G.J., Ruigrok Y.M. Preventive screening for intracranial aneurysms. Int. J. Stroke. 2022 Jan;17(1):30–36. doi: 10.1177/17474930211024584. - DOI - PMC - PubMed
    1. Gareev I., Beylerli O., Yang G., Sun J., Pavlov V., Izmailov A., Shi H., Zhao S. The current state of MiRNAs as biomarkers and therapeutic tools. Clin. Exp. Med. 2020 Aug;20(3):349–359. doi: 10.1007/s10238-020-00627-2. - DOI - PubMed
    1. Gareev I., Beylerli O., Yang G., Izmailov A., Shi H., Sun J., Zhao B., Liu B., Zhao S. Diagnostic and prognostic potential of circulating miRNAs for intracranial aneurysms. Neurosurg. Rev. 2021 Aug;44(4):2025–2039. doi: 10.1007/s10143-020-01427-8. - DOI - PubMed

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