Integrative molecular subtyping and diagnostic model construction for Moyamoya disease based on diverse programmed cell death gene patterns
Abstract Background The role of cell death in the pathogenesis of Moyamoya disease (MMD) remains unclear. Methods Gene expression data from two publicly available MMD datasets (GSE157628 and GSE189993) were integrated. Differential expression analysis of cell death-related genes was performed, follo...
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BMC
2025-06-01
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| Series: | Orphanet Journal of Rare Diseases |
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| Online Access: | https://doi.org/10.1186/s13023-025-03816-y |
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| author | Qikai Tang Chenfeng Ma Hu Li Jiaheng Xie Weiqi Bian Qingyu Lu Zeyu Wan Wei Wu |
| author_facet | Qikai Tang Chenfeng Ma Hu Li Jiaheng Xie Weiqi Bian Qingyu Lu Zeyu Wan Wei Wu |
| author_sort | Qikai Tang |
| collection | DOAJ |
| description | Abstract Background The role of cell death in the pathogenesis of Moyamoya disease (MMD) remains unclear. Methods Gene expression data from two publicly available MMD datasets (GSE157628 and GSE189993) were integrated. Differential expression analysis of cell death-related genes was performed, followed by immune cell infiltration analysis using the CIBERSORT algorithm. Unsupervised clustering identified molecular subgroups within MMD patients, and these were further analyzed using Gene Set Variation Analysis (GSVA) and Weighted Gene Co-expression Network Analysis (WGCNA). Diagnostic models were constructed using machine learning algorithms, and key model genes were validated by qRT-PCR in arterial samples from MMD patients. Results Significant differences in gene expression were observed, with immune cell infiltration analysis showing differences in T follicular helper cells, activated dendritic cells, resting and activated mast cells, and eosinophils. Unsupervised clustering identified two distinct patient groups, and differential gene expression revealed upregulation of genes like ADRB2, FGR, ICAM1, IL1B, DDIT4, CXCL1, CASP4, G0S2, CYP1B1, and CD74 in C2 cluster. Weighted gene co-expression network analysis (WGCNA) identified key genes in the most correlated module. Machine learning models (RF, SVM, GLM, XGB) were constructed, with the SVM model showing the best performance (AUC = 1.000). A nomogram based on the SVM model demonstrated high predictive accuracy. Enrichment analysis revealed that key genes were involved in apoptosis and antigen processing, with strong diagnostic performance confirmed by ROC curves. PCR analysis revealed that model genes RGS1, MUC1, KCNA2, TAC1, and SOST were all up-regulated in MMD group. Conclusions This study provides novel insights into the molecular landscape of MMD, highlighting the importance of cell death-related pathways and immune responses. The identified biomarkers and molecular subgroups offer potential targets for therapeutic intervention and improved diagnostic strategies in MMD. |
| format | Article |
| id | doaj-art-0ba2e8019dc443c18c6d4da2ba901937 |
| institution | OA Journals |
| issn | 1750-1172 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Orphanet Journal of Rare Diseases |
| spelling | doaj-art-0ba2e8019dc443c18c6d4da2ba9019372025-08-20T02:05:42ZengBMCOrphanet Journal of Rare Diseases1750-11722025-06-0120111410.1186/s13023-025-03816-yIntegrative molecular subtyping and diagnostic model construction for Moyamoya disease based on diverse programmed cell death gene patternsQikai Tang0Chenfeng Ma1Hu Li2Jiaheng Xie3Weiqi Bian4Qingyu Lu5Zeyu Wan6Wei Wu7Department of Neurosurgery, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Neurosurgery, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Neurosurgery, People’s Hospital of Ganyu DistrictDepartment of Plastic Surgery, Xiangya Hospital, Central South UniversityDepartment of Neurosurgery, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Neurosurgery, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Neurosurgery, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Neurosurgery, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical UniversityAbstract Background The role of cell death in the pathogenesis of Moyamoya disease (MMD) remains unclear. Methods Gene expression data from two publicly available MMD datasets (GSE157628 and GSE189993) were integrated. Differential expression analysis of cell death-related genes was performed, followed by immune cell infiltration analysis using the CIBERSORT algorithm. Unsupervised clustering identified molecular subgroups within MMD patients, and these were further analyzed using Gene Set Variation Analysis (GSVA) and Weighted Gene Co-expression Network Analysis (WGCNA). Diagnostic models were constructed using machine learning algorithms, and key model genes were validated by qRT-PCR in arterial samples from MMD patients. Results Significant differences in gene expression were observed, with immune cell infiltration analysis showing differences in T follicular helper cells, activated dendritic cells, resting and activated mast cells, and eosinophils. Unsupervised clustering identified two distinct patient groups, and differential gene expression revealed upregulation of genes like ADRB2, FGR, ICAM1, IL1B, DDIT4, CXCL1, CASP4, G0S2, CYP1B1, and CD74 in C2 cluster. Weighted gene co-expression network analysis (WGCNA) identified key genes in the most correlated module. Machine learning models (RF, SVM, GLM, XGB) were constructed, with the SVM model showing the best performance (AUC = 1.000). A nomogram based on the SVM model demonstrated high predictive accuracy. Enrichment analysis revealed that key genes were involved in apoptosis and antigen processing, with strong diagnostic performance confirmed by ROC curves. PCR analysis revealed that model genes RGS1, MUC1, KCNA2, TAC1, and SOST were all up-regulated in MMD group. Conclusions This study provides novel insights into the molecular landscape of MMD, highlighting the importance of cell death-related pathways and immune responses. The identified biomarkers and molecular subgroups offer potential targets for therapeutic intervention and improved diagnostic strategies in MMD.https://doi.org/10.1186/s13023-025-03816-yMoyamoya diseaseBioinformaticsWGCNARNA-sequencingBiomarker |
| spellingShingle | Qikai Tang Chenfeng Ma Hu Li Jiaheng Xie Weiqi Bian Qingyu Lu Zeyu Wan Wei Wu Integrative molecular subtyping and diagnostic model construction for Moyamoya disease based on diverse programmed cell death gene patterns Orphanet Journal of Rare Diseases Moyamoya disease Bioinformatics WGCNA RNA-sequencing Biomarker |
| title | Integrative molecular subtyping and diagnostic model construction for Moyamoya disease based on diverse programmed cell death gene patterns |
| title_full | Integrative molecular subtyping and diagnostic model construction for Moyamoya disease based on diverse programmed cell death gene patterns |
| title_fullStr | Integrative molecular subtyping and diagnostic model construction for Moyamoya disease based on diverse programmed cell death gene patterns |
| title_full_unstemmed | Integrative molecular subtyping and diagnostic model construction for Moyamoya disease based on diverse programmed cell death gene patterns |
| title_short | Integrative molecular subtyping and diagnostic model construction for Moyamoya disease based on diverse programmed cell death gene patterns |
| title_sort | integrative molecular subtyping and diagnostic model construction for moyamoya disease based on diverse programmed cell death gene patterns |
| topic | Moyamoya disease Bioinformatics WGCNA RNA-sequencing Biomarker |
| url | https://doi.org/10.1186/s13023-025-03816-y |
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