Bioinformatics Analysis of Moyamoya Disease and Moyamoya Syndrome Pathology Based on Genetic Genes

doi:10.11817/j.issn.1673-9248.2024.04.009

Abstract

Abstract:

Objective

To explore the functional activities of Moyamoya disease (MMD) and Moyamoya syndrome (MMS) pathology based on risk genes identified in databases.

Methods

Genetic studies about MMD and MMS were searched in database, and MMD and MMS related genes were identified initially. Then, gene ontology (GO, including biological process, cellular component, molecular function) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional activities were enriched for identified risk genes, respectively. Finally, the protein-protein interaction (PPI) networks were constructed using STRING web-based tool, and the hub genes were identified using cytoHubba plugin. The R software and related packages were performed and visualized.

Results

In total, 53 MMD articles and 51 MMS articles were enrolled, and 126 MMD related genes and 51 MMS related genes were identified. Bioinformatic analysis of functional activities was explored according to all the susceptibility genes. For GO enrichment, biological processes were mainly enriched in cytokine-response, cytokine-mediated signaling pathway, regulation of cell adhesion and differentiation, cell migration; cellular components were mainly enriched in cell surface, receptor of complex, plasma membrane protein complex, intrinsic components, and external side of plasma membrane; molecular functions were mainly enriched in enzyme binding, kinase binding, immune receptor activity, signaling receptor binding, and protein-containing complex binding. For KEGG enrichment, pathways in cancer, viral infection, and immune activities were mainly enriched, especially MAPK signaling pathway and Jak-STAT signaling pathway. PPI was constructed, and five overlapped genes were identified according five algorithms, namely PTPN11, GRB2, ITGB3, CBL, HIF1A.

Conclusion

Bioinformatic analysis of functional activities involved in MMD and MMS provides valuable clues and novel insights to investigate the pathophysiology of Moyamoya disorder. The pathogenesis of Moyamoya disease may be based on genetic inheritance, and multiple environmental factors are involved in the progression of the disease.

Key words: Moyamoya disease, Moyamoya syndrom, Genetic pathology, Bioinformatics analysis

Lei Cao, Yipu Shao, Zhizhong Zhang, Chenchao Wang, Kaiwen Sun, Yang Dong, Dongming Yan, Hongwei Li, Bo Yang. Bioinformatics Analysis of Moyamoya Disease and Moyamoya Syndrome Pathology Based on Genetic Genes[J]. Chinese Journal of Cerebrovascular Diseases(Electronic Edition), 2024, 18(04): 350-356.

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URL: https://zhnxgbzz.cma-cmc.com.cn/EN/10.11817/j.issn.1673-9248.2024.04.009

https://zhnxgbzz.cma-cmc.com.cn/EN/Y2024/V18/I04/350

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References 35

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