Podrobná bibliografie
| Název: |
Deciphering the role of CircRNA-miRNA networks in multiple sclerosis pathogenesis through minimal cut-set analysis. |
| Autoři: |
Zabihi, Mohammad Reza, Moradi, Zahra, Salehi, Zahra, Kavousi, Kaveh |
| Zdroj: |
Discover Applied Sciences; Sep2025, Vol. 7 Issue 9, p1-17, 17p |
| Abstrakt: |
Background: Recent research has emphasized the role of non-coding RNAs, particularly circular RNAs (circRNA), in the pathophysiology of multiple sclerosis (MS). Notably, circRELL1, circRPPH1, and circGSDMB have been identified as significantly upregulated in MS patients. We aimed to elucidate the competing endogenous RNA (ceRNA) network of these circRNAs using minimal cut-set methodology. Materials and methods: We analyzed microRNAs, and their target mRNAs associated with circRELL1, circRPPH1, and circGSDMB by using the CircInteractome web tool and miRTarBase database. A protein–protein interaction (PPI) network for these mRNAs was reconstructed, and minimal cut-set analysis was performed using Gephi. Key driver nodes were identified with the CytoCtrlAnalyser plugin in Cytoscape 3.9. Results: The analysis revealed five proteins—AKT1, CCND2, BAX, CRKL, and EGFL7 as enriched driver nodes. Key circRNA/miRNA/mRNA interactions linked to MS pathogenesis were found, including circ_0001400/miR-637/AKT1 and circRPPH1/miR-663b/CCND2, circ_0001400/miR-126/EGFL7, circ_0001400/miR-126/CRKL, circ_0106803/miR-7-5p and miR-766-3p/BAX. Conclusion: This study revealed potential circRNA-miRNA-mRNA axes and key driver nodes that may be involved in the pathogenesis of MS. Importantly, the identification of driver nodes within these networks highlights the potential of network analysis in deciphering complex disease mechanisms. [ABSTRACT FROM AUTHOR] |
|
Copyright of Discover Applied Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Databáze: |
Complementary Index |