Integrative Machine Learning Approach to Explore Glycosylation Signatures and Immune Landscape in Moyamoya Disease
Moyamoya disease (MMD) is a rare, chronic cerebrovascular disorder of uncertain etiology. Although abnormal glucose metabolism has been implicated, the contribution of glycosylation-related genes in MMD remains elusive. In this study, we analyzed 2 transcriptome data sets (GSE189993 and GSE131293) f...
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| Format: | Article |
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SAGE Publishing
2025-05-01
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| Series: | Bioinformatics and Biology Insights |
| Online Access: | https://doi.org/10.1177/11779322251342412 |
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| author | Cunxin Tan MD, PhD Jing Wang MD, PhD Yanru Wang MD Shaoqi Xu BS Zhenyu Zhou MD Junze Zhang MD, PhD Shihao He MD, PhD Ran Duan MD, PhD |
| author_facet | Cunxin Tan MD, PhD Jing Wang MD, PhD Yanru Wang MD Shaoqi Xu BS Zhenyu Zhou MD Junze Zhang MD, PhD Shihao He MD, PhD Ran Duan MD, PhD |
| author_sort | Cunxin Tan MD, PhD |
| collection | DOAJ |
| description | Moyamoya disease (MMD) is a rare, chronic cerebrovascular disorder of uncertain etiology. Although abnormal glucose metabolism has been implicated, the contribution of glycosylation-related genes in MMD remains elusive. In this study, we analyzed 2 transcriptome data sets (GSE189993 and GSE131293) from the Gene Expression Omnibus (GEO) database to identify 723 differentially expressed genes (DEGs) between MMD patients and controls. Intersection genes with known glycosylation-related genes underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We utilized machine learning to select key hub genes, followed by immune cell infiltration and correlation analyses. In-depth immune cell analysis indicated that both CFP and MGAT5B were closely tied to various immune components, suggesting potential crosstalk between glycosylation pathways and immune regulation. Notably, CFP was positively associated with pDCs, HLA, and CCR, whereas MGAT5B correlated with B-cells, check-points, and T helper cells but showed a negative relationship with Tregs, hinting at an immunoregulatory mechanism influencing MMD progression. Motif-TF annotation highlighted csibp_M2095 as the motif with the highest normalized enrichment score (NES: 6.57). Reverse microRNA (miRNA)-gene prediction identified 75 miRNAs regulating these focus genes, along with 126 miRNA-miRNA interconnections. Connectivity Map (Cmap) analysis revealed that Chenodeoxycholic acid, MRS-1220, Phenytoin, and Piceid were strongly negatively correlated with MMD expression profiles, suggesting potential therapeutic candidates. Enzyme-linked immunosorbent assays confirmed elevated CFP and MGAT5B and reduced PTPN11 in MMD, aligning with our bioinformatic findings. Moreover, PTPN11 knockdown in human brain microvascular endothelial cells (HBMECs) significantly enhanced tube formation, indicating a role in vascular remodeling. Collectively, these results emphasize the importance of glycosylation-related genes and immune dysregulation in MMD pathogenesis. These findings broaden our understanding of MMD’s underlying mechanisms and underscore the necessity of continued research into glycosylation-driven pathways for improved disease management. |
| format | Article |
| id | doaj-art-d52cacbf936d4e14b24d8514d94df423 |
| institution | OA Journals |
| issn | 1177-9322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SAGE Publishing |
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| spelling | doaj-art-d52cacbf936d4e14b24d8514d94df4232025-08-20T02:26:58ZengSAGE PublishingBioinformatics and Biology Insights1177-93222025-05-011910.1177/11779322251342412Integrative Machine Learning Approach to Explore Glycosylation Signatures and Immune Landscape in Moyamoya DiseaseCunxin Tan MD, PhD0Jing Wang MD, PhD1Yanru Wang MD2Shaoqi Xu BS3Zhenyu Zhou MD4Junze Zhang MD, PhD5Shihao He MD, PhD6Ran Duan MD, PhD7Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaDepartment of Neurosurgery, Peking University International Hospital, Beijing, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaSuzhou Vocational Health College, Suzhou, ChinaDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, ChinaDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USADepartment of Neurosurgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, ChinaDepartment of Neurosurgery, Peking University International Hospital, Beijing, ChinaMoyamoya disease (MMD) is a rare, chronic cerebrovascular disorder of uncertain etiology. Although abnormal glucose metabolism has been implicated, the contribution of glycosylation-related genes in MMD remains elusive. In this study, we analyzed 2 transcriptome data sets (GSE189993 and GSE131293) from the Gene Expression Omnibus (GEO) database to identify 723 differentially expressed genes (DEGs) between MMD patients and controls. Intersection genes with known glycosylation-related genes underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We utilized machine learning to select key hub genes, followed by immune cell infiltration and correlation analyses. In-depth immune cell analysis indicated that both CFP and MGAT5B were closely tied to various immune components, suggesting potential crosstalk between glycosylation pathways and immune regulation. Notably, CFP was positively associated with pDCs, HLA, and CCR, whereas MGAT5B correlated with B-cells, check-points, and T helper cells but showed a negative relationship with Tregs, hinting at an immunoregulatory mechanism influencing MMD progression. Motif-TF annotation highlighted csibp_M2095 as the motif with the highest normalized enrichment score (NES: 6.57). Reverse microRNA (miRNA)-gene prediction identified 75 miRNAs regulating these focus genes, along with 126 miRNA-miRNA interconnections. Connectivity Map (Cmap) analysis revealed that Chenodeoxycholic acid, MRS-1220, Phenytoin, and Piceid were strongly negatively correlated with MMD expression profiles, suggesting potential therapeutic candidates. Enzyme-linked immunosorbent assays confirmed elevated CFP and MGAT5B and reduced PTPN11 in MMD, aligning with our bioinformatic findings. Moreover, PTPN11 knockdown in human brain microvascular endothelial cells (HBMECs) significantly enhanced tube formation, indicating a role in vascular remodeling. Collectively, these results emphasize the importance of glycosylation-related genes and immune dysregulation in MMD pathogenesis. These findings broaden our understanding of MMD’s underlying mechanisms and underscore the necessity of continued research into glycosylation-driven pathways for improved disease management.https://doi.org/10.1177/11779322251342412 |
| spellingShingle | Cunxin Tan MD, PhD Jing Wang MD, PhD Yanru Wang MD Shaoqi Xu BS Zhenyu Zhou MD Junze Zhang MD, PhD Shihao He MD, PhD Ran Duan MD, PhD Integrative Machine Learning Approach to Explore Glycosylation Signatures and Immune Landscape in Moyamoya Disease Bioinformatics and Biology Insights |
| title | Integrative Machine Learning Approach to Explore Glycosylation Signatures and Immune Landscape in Moyamoya Disease |
| title_full | Integrative Machine Learning Approach to Explore Glycosylation Signatures and Immune Landscape in Moyamoya Disease |
| title_fullStr | Integrative Machine Learning Approach to Explore Glycosylation Signatures and Immune Landscape in Moyamoya Disease |
| title_full_unstemmed | Integrative Machine Learning Approach to Explore Glycosylation Signatures and Immune Landscape in Moyamoya Disease |
| title_short | Integrative Machine Learning Approach to Explore Glycosylation Signatures and Immune Landscape in Moyamoya Disease |
| title_sort | integrative machine learning approach to explore glycosylation signatures and immune landscape in moyamoya disease |
| url | https://doi.org/10.1177/11779322251342412 |
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