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Characterization of moyamoya disease molecular subtypes through disulfidptosis‑related genes and immune landscape analysis
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- Published online on: February 14, 2025 https://doi.org/10.3892/etm.2025.12824
- Article Number: 74
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Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
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Abstract
Moyamoya disease (MMD), a chronic cerebrovascular disorder, is characterized by progressive stenosis of major intracranial arteries. However, the mechanisms underlying the pathological narrowing have remained largely elusive. Disulfidptosis is a new mode of cell death caused by the vulnerability of the actin cytoskeleton to disulfide stress, and proteomic profiling of MMD has revealed that abnormal proliferation of endothelial cells may be induced by upregulation of focal adhesion‑related proteins. However, the role of disulfidptosis in MMD has not yet been reported. The Gene Expression Omnibus database was searched for datasets with a sample size of more than six and four microarray datasets (GSE189993, GSE157628, GSE141024 and GSE141022) were downloaded. Based on the expression profiles of DRGs in each sample, MMD was clustered into three discrete molecular subtypes. Differential expression analysis was performed using the R package ‘limma’ to analyze the differences in gene expression between MMD and controls. Functional enrichment analysis was used to explore the molecular functions and mechanisms of the differentially expressed DRGs in MMD. Based on the results of differential expression analysis, the intersection among four comparison groups, which included C1 vs. C2, C1 vs. C3, C2 vs. C3, and MMD vs. controls, were taken and four hub genes were selected for further study. In addition, the expression and distribution of 22 types of immune cells in each sample was analyzed. Spearman's correlation analysis was performed to explore the correlation between the hub genes and the proportion of immune cells. MMD‑related genes were identified and the relationship between them and hub genes was analyzed. Furthermore, ELISA was performed to verify the expression of the four MMD hub genes. In the present study, a novel molecular classification of MMD based on disulfidptosis gene expression was established and a total of 348 upregulated and 801 downregulated genes were identified in patients with MMD compared with controls. A total of four hub genes (WDR27, OSBPL11, MSOM1 and NEIL2) were selected as biomarkers for the different subtypes of MMD. The DRG results indicated that disulfidptosis may affect the progression of MMD pathogenesis. Based on this, MMD molecular subtypes were constructed and four hub genes were selected. Immune infiltration analysis indicated a relationship between hub genes and immune dysfunction, which could lead to abnormal migration and proliferation of endothelial cells in MMD. The results of the gene set enrichment analysis and gene set variation analysis correlated with the results of immune dysfunction. Differential analysis of MMD‑related genes revealed that MEG3, NCL, NFIB and others were significantly differentially expressed in patients with MMD compared to controls. NEIL2 showed a significant positive correlation with MEG3 expression (Pearson's r=0.4), whereas WDR27 showed a significant negative correlation with MEG3 expression (Pearson's r=0.415). Correlation analysis showed that the four hub genes were significantly associated with endothelial migration‑ and proliferation‑related genes. ELISA revealed that four hub genes (WDR27, OSBPL11, MSOM1 and NEIL2) were significantly decreased in MMD compared to healthy controls, which correlated with the results of the present bioinformatic analyses. In conclusion, disulfidptosis may be involved in the pathogenesis of MMD. Immune infiltration analysis demonstrated immune dysregulation among different disulfidptosis subtypes, which may lead to the migration and proliferation of endothelial cells. The present study was the first to explore the correlation between MMD pathogenesis and disulfidptosis, providing novel insights and identifying potential subtype classifications and biomarkers for the diagnosis of MMD.