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Background <p>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.</p> Materials and methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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Deciphering the role of CircRNA-miRNA networks in multiple sclerosis pathogenesis through minimal cut-set analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月24日

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  • Mohammad Reza Zabihi,
  • Zahra Moradi,
  • Zahra Salehi,
  • Kaveh Kavousi

摘要

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.