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中华脑血管病杂志(电子版) ›› 2024, Vol. 18 ›› Issue (04) : 350 -356. doi: 10.11817/j.issn.1673-9248.2024.04.009

循证医学

基于遗传基因的烟雾病与烟雾综合征生物信息学分析机制研究
曹磊1, 邵轶普1, 张志中1, 王晨潮1, 孙开文1, 董阳1, 闫东明1, 李红伟1,(), 杨波1,()   
  1. 1. 450000 郑州,郑州大学第一附属医院神经外科
  • 收稿日期:2023-09-18 出版日期:2024-08-01
  • 通信作者: 李红伟, 杨波
  • 基金资助:
    国家重点研发计划(2017YFA0105003)

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

Lei Cao1, Yipu Shao1, Zhizhong Zhang1, Chenchao Wang1, Kaiwen Sun1, Yang Dong1, Dongming Yan1, Hongwei Li1,(), Bo Yang1,()   

  1. 1. Department of Neurosurgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
  • Received:2023-09-18 Published:2024-08-01
  • Corresponding author: Hongwei Li, Bo Yang
引用本文:

曹磊, 邵轶普, 张志中, 王晨潮, 孙开文, 董阳, 闫东明, 李红伟, 杨波. 基于遗传基因的烟雾病与烟雾综合征生物信息学分析机制研究[J]. 中华脑血管病杂志(电子版), 2024, 18(04): 350-356.

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.

目的

基于数据库中已鉴定并发表的疾病危险基因,通过生物信息学分析探究烟雾病和烟雾综合征的病理发生机制。

方法

在PubMed等公共数据库搜索烟雾病、烟雾综合征基因相关研究,整理已鉴定并发表的疾病相关危险基因。借助网络工具,在基因本体数据库(包括生物学过程、细胞组分和分子功能3大类)、京都基因与基因组百科全书(KEGG)数据库对基因集进行功能富集分析,构建蛋白-蛋白互作(PPI)网络并筛选关键基因。

结果

本研究纳入53篇烟雾病研究文献,鉴定126个烟雾病相关危险基因;纳入51篇烟雾综合征研究文献,鉴定出51个烟雾综合征相关危险基因。共计纳入177个疾病相关危险基因。基因本体功能分析:生物学过程主要富集在细胞因子及其介导通路,以及细胞的黏附、分化、迁移等活动;细胞组分主要富集在细胞表面、细胞膜及蛋白复合物、受体复合体等;分子功能主要富集在酶结合、激酶结合、磷酸蛋白结合、受体信号结合、免疫受体活动等。KEGG分析主要富集在癌症信号通路、免疫细胞分化及免疫疾病通路、病毒感染信号通路等,其中MAPK信号代谢通路、Jak-STAT信号代谢通路被显著富集。通过不同算法,在PPI网络筛选出5种关键基因:PTPN11GRB2ITGB3CBLHIF1A

结论

生物信息学分析为阐述烟雾样血管改变的病理机制提供了新的角度。烟雾病发病机制可能是以基因遗传为背景、多种环境因素共同参与的。

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.

图1 烟雾病相关危险基因筛选研究流程图 注:MMD为烟雾病,MMS为烟雾病综合征,KEGG为京都基因与基因组百科全书,PPI为蛋白-蛋白互作网络
图2 基因本体富集分析圈图。图a基因本体富集分析生物学过程结果;图b基因本体富集分析细胞组分结果;图c基因本体分析分子功能结果 注:圈图右侧不同颜色表示富集分析条目,内圈红色代表-log10(P值),圈图左侧表示富集条目投射基因
图3 京都基因与基因组百科全书(KEGG)信号通路分析气泡图。气泡图显示KEGG富集分析前15条信号通路 注:X轴表示基因比率(GeneRatio),Y轴表示信号通路名称,圆圈大小表示通路中富集的基因数量,圆圈颜色表示-log10(P值)
图4 蛋白-蛋白互作网络图 注:网络中节点代表蛋白质并用三维气泡显示,气泡中展示蛋白结构,边代表蛋白质与蛋白质之间连接
图5 蛋白网络关键基因。图a为接近中心性算法识别关键基因,图b为度算法识别关键基因,图c为最大集团中心性算法识别关键基因,图d为边缘渗透分量算法识别关键基因,图e为最大邻域分量算法识别关键基因,图f为关键基因venn图 注:方块代表关键基因;方块颜色代表相应算法评分,颜色越深评分越高
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