Deciphering isonicotinylation and mast cell-associated pathogenic patterns in Duchenne muscular dystrophy: evidence from ensemble learning-driven multi-omics and in vitro validation
4 affiliations
- 1Department of Anesthesiology, Jinhua Municipal Central Hospital, Jinhua, Zhejiang, China
- 2Nursing Department, Jiangyin People’s Hospital, Jiangyin, China
- 3Nursing Department, the First People’s Hospital of Wenling, Wenling, Zhejiang, China
- 4Nursing Department, the Second Hospital of Jinhua, Jinhua, Zhejiang, China
Abstract
Background: Duchenne muscular dystrophy (DMD) is dystrophin-deficiency skeletal and cardiac muscle disease. Protein post-translational modification (PTM) and aberrant immune cell activation play a key role in its pathogenesis.
Objective: Our study aims to investigate isonicotinylation (Kinic)-associated signature modulatory patterns in mast cells and DMD pathogenesis.
Methods: We first performed integrated bioinformatic methods, such as WGCNA, GSVA, and CIBERSORT to identify Kinic and mast cell (KM)-related differentially expressed genes (DEGs) in DMD patient muscle bulk profiles (GSE38417 and GSE109178). Next, consensus clustering and assembling machine learning pipeline identified KM-associated diagnostic model and molecular subgroups in training the DMD patient muscle bulk set (GSE6011), and then, diagnostic model performance was examined in the independent DMD patient muscle bulk set (GSE32720). In addition, SHAP analysis decoded the main contributor in KM-associated diagnostic model construction, and contributor molecular and immune characters in DMD were estimated in GSE6011 and the DMD mouse muscle single-cell transcriptomic dataset (GSE213925) assisted by computational virtual cells. In addition, the deep learning framework and network pharmacology identified the drug synergy strategy for the treatment of DMD. Finally, the in vitro assay identified expression patterns of the main contributor.
Results: KM-associated molecular patterns can potentially guide the diagnostic and molecular stratification for DMD patients. USP18 can be considered an upregulated and mast cell-distributed hub gene involved in pathogenesis of DMD. In addition, finafloxacin with BRD-K10482608 can be considered two potential therapeutic candidates for DMD patient clinical applications.
Conclusion: Our investigation described a novel forecasting and therapeutic model for DMD patients, which potentially enhances DMD patient clinical translation.
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BibTeX
@article{yu2026deciphering,
title = {{Deciphering isonicotinylation and mast cell-associated pathogenic patterns in Duchenne muscular dystrophy: evidence from ensemble learning-driven multi-omics and in vitro validation}},
author = {Sunrui Yu and Duojia Xu and Yuting Hua and Hangfeng Mo and Yujin Liu},
journal = {Frontiers in Molecular Biosciences},
year = {2026},
month = oct,
doi = {10.3389/fmolb.2026.1853169},
url = {https://doi.org/10.3389/fmolb.2026.1853169},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Deciphering isonicotinylation and mast cell-associated pathogenic patterns in Duchenne muscular dystrophy: evidence from ensemble learning-driven multi-omics and in vitro validation
AU - Sunrui Yu
AU - Duojia Xu
AU - Yuting Hua
AU - Hangfeng Mo
AU - Yujin Liu
PY - 2026
DA - 2026/10/13
JO - Frontiers in Molecular Biosciences
DO - 10.3389/fmolb.2026.1853169
UR - https://doi.org/10.3389/fmolb.2026.1853169
AB - Background: Duchenne muscular dystrophy (DMD) is dystrophin-deficiency skeletal and cardiac muscle disease. Protein post-translational modification (PTM) and aberrant immune cell activation play a key role in its pathogenesis. Objective: Our study aims to investigate isonicotinylation (Kinic)-associated signature modulatory patterns in mast cells and DMD pathogenesis. Methods: We first performed integrated bioinformatic methods, such as WGCNA, GSVA, and CIBERSORT to identify Kinic and mast cell (KM)-related differentially expressed genes (DEGs) in DMD patient muscle bulk profiles (GSE38417 and GSE109178). Next, consensus clustering and assembling machine learning pipeline identified KM-associated diagnostic model and molecular subgroups in training the DMD patient muscle bulk set (GSE6011), and then, diagnostic model performance was examined in the independent DMD patient muscle bulk set (GSE32720). In addition, SHAP analysis decoded the main contributor in KM-associated diagnostic model construction, and contributor molecular and immune characters in DMD were estimated in GSE6011 and the DMD mouse muscle single-cell transcriptomic dataset (GSE213925) assisted by computational virtual cells. In addition, the deep learning framework and network pharmacology identified the drug synergy strategy for the treatment of DMD. Finally, the in vitro assay identified expression patterns of the main contributor. Results: KM-associated molecular patterns can potentially guide the diagnostic and molecular stratification for DMD patients. USP18 can be considered an upregulated and mast cell-distributed hub gene involved in pathogenesis of DMD. In addition, finafloxacin with BRD-K10482608 can be considered two potential therapeutic candidates for DMD patient clinical applications. Conclusion: Our investigation described a novel forecasting and therapeutic model for DMD patients, which potentially enhances DMD patient clinical translation.
SN - 2296-889X
ER -