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) 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.

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Publication Details

Journal
Bioinformatics and Biology Insights
Published
2025-03-01
DOI
https://doi.org/10.1177/11779322251342412
Citations
1
Primary Topic
Moyamoya disease diagnosis and treatment
Type
article
Field-Weighted Citation Impact
1.67
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Integrative Machine Learning Approach to Explore Glycosylation Signatures and Immune Landscape in Moyamoya Disease

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article

Integrative Machine Learning Approach to Explore Glycosylation Signatures and Immune Landscape in Moyamoya Disease

Shihao He, Cunxin Tan, Junze Zhang, Ran Duan, Shaoqi Xu, Jing Wang, Zhenyu Zhou, Yanru Wang
article en
1 citations

Abstract

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.

Bioinformatics and Biology InsightsVol. 19
Capital Medical University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Peking University (CN), Peking Union Medical College Hospital (CN), Beijing Tian Tan Hospital (CN), Peking University International Hospital (CN), Suzhou Vocational Health College (CN), Stanford University (US)
Openalex Percentile: Top 17%
Moyamoya disease diagnosis and treatment
1.67
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