Regulatory role and subtype analysis of m6A modifications in dermatomyositis

Abstract

Background Dermatomyositis (DM) is an uncommon autoimmune disease that presents challenges due to the lack of reliable biomarkers in clinical practice. Growing evidence suggests that N6-methyladenosine (m6A) is closely associated with the pathogenesis of autoimmune diseases.

Methods Microarray gene expression matrix for GSE46239, GSE128314, and GSE142807 were downloaded from the GEO database. Random forest (RF), support vector machine (SVM), and nomogram models were developed, with their performance subsequently compared. The identification of m6A subtypes, based on differentially expressed m6A regulatory genes, was followed by the classification of gene subtypes according to the differently expressed genes between the m6A subtypes. Both classification systems were subjected to m6A scoring analysis and visualized via a Sankey diagram.

Results We retrieved 99 dermatomyositis samples and 14 healthy samples. Using an RF model, we identified five core genes—IGFBP3, ZCCHC4, HNRNPC, WTAP, and RBM15—and constructed a predictive nomogram model. Two m6A clusters were developed. Cluster A exhibited a significant increase of CD56-bright natural killer cells, immature B cells, plasmacytoid dendritic cells, regulatory T cells, and type 1 T helper cells distinct from cluster B (p < 0.05). Based on 32 significantly distinctly expressed genes between m6A subtypes (p < 0.05), we further reproduced two m6A gene subtypes. The Sankey diagram showed significant concordance among m6A scores, m6A subtypes, and m6A gene subtypes.

Conclusion m6A regulatory genes significantly influence the pathogenesis of dermatomyositis. In this work, we built a predictive nomogram model, comprehensively evaluated two classification methods, and provided new insights for patient classification.

Keywords

Dermatomyositis N6-methyladenosine / m6A regulatory genes / machine learning

Author contribution

Rui Tu and Youxian He designed the whole study, collected and analyzed the data, and revised the manuscript. Xiang Long and Yidan Hu analyzed the data, searched the literature, and drafted the manuscript. All authors contributed to this work and approved the final manuscript. Corresponding authors: Youxian He and Rui Tu.

Ethics approval and consent to participate

Research involving human data has been performed in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Fourth Affiliated Hospital of Southwest Medical University. It is noted that informed consent was obtained from the patients and individuals involved in the original studies.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We express our gratitude to all authors who have publicized data for the GEO dataset and to the participants included in the research.

Appendix A. Supplementary material

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.gmg.2026.100097.

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