Bioinformatics Analysis of Blood Vessel Samples to Identify Potentially Key Genes Associated with Moyamoya Disease

doi:10.3969/j.issn.1673-5765.2024.04.006

Abstract

Abstract: Objective In this study, bioinformatics analysis of differentially expressed genes (DEGs) between blood vessel samples of patients with moyamoya disease were conducted to explore the potential pathogenesis of moyamoya disease.
Methods The cerebral blood vessel samples from patients with moyamoya disease and internal carotid aneurysm were taken as research subjects. The linear models for microarray data (limma) package in R was used to analyze the GSE141025 dataset downloaded from the gene expression omnibus (GEO). The dataset included samples from 4 moyamoya disease patients and 4 internal carotid aneurysm patients, providing a total of 16 samples including one middle cerebral artery and one superficial temporal artery from each patient. A total of 12 samples of middle cerebral artery and superficial temporal artery from moyamoya disease patients and superficial temporal artery from internal carotid aneurysm patients were selected for DEGs screening. Gene ontology (GO) enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis on the DEGs were conducted using the clusterProfiler package in R. The STRING database was utilized to construct the protein-protein interaction (PPI) network, and the network visualization software Cytoscape was used to visualize the protein network and to filter hub genes.
Results In the comparison between middle cerebral artery and superficial temporal artery samples from patients with moyamoya disease, 138 DEGs were identified, including 18 up-regulated genes and 120 down-regulated genes. GO enrichment analysis revealed that these DEGs were significantly enriched in extracellular matrix, receptor ligand activity, and growth factor activity, suggesting their potential involvement in vasculopathy and neuroprotective mechanisms associated with moyamoya disease. KEGG pathway analysis indicated that DEGs were primarily enriched in the tyrosine metabolic pathway. Through PPI network analysis, 9 hub genes were filtered, including periostin (POSTN), brain derived neurotrophic factor (BDNF), platelet derived growth factor receptor alpha (PDGFRA), Thy-1 cell surface antigen (THY1), collagen type ⅩⅤ alpha 1 chain (COL15A1), fibroblast growth factor 7 (FGF7), lumican (LUM), laminin subunit alpha 2 (LAMA2) and reelin (RELN). Additionally, the up-regulated gene delta like canonical Notch ligand 4 (DLL4) was firstly founded possibly associated with pathological angiogenesis of moyamoya disease in this study, suggesting that it may play a significant role in moyamoya disease.
Conclusions The study indicates that the dysregulation of the expression of extracellular matrix components, growth factors and their receptors might be involved in the pathogenesis of moyamoya disease. The hub genes selected by DEGs analysis (POSTN, BDNF, PDGFRA, THY1, COL15A1, FGF7, LUM, LAMA2, RELN) and DLL4 may play a role in the pathological process of moyamoya disease.

Key words: Moyamoya disease; Bioinformatics analysis; Gene expression data; Hub gene

摘要： 目的本研究对烟雾病患者血管样本的差异表达基因（differentially expressed genes，DEGs）进行生物信息学鉴定和分析，旨在探讨烟雾病的潜在发病机制。
方法本研究以烟雾病和颈内动脉瘤患者大脑血管样本为研究对象。利用R语言线性模型微阵列数据（linear models for microarray data，limma）分析包对基因表达综合数据库（gene expression omnibus，GEO）中的GSE141025数据集进行分析，该数据集涵盖4例烟雾病患者和4例颈内动脉瘤患者的大脑中动脉和颞浅动脉样本各1个，共计16个样本。选择烟雾病患者的大脑中动脉、颞浅动脉及颈内动脉瘤患者的颞浅动脉共12个样本进行DEGs筛选。通过R语言功能富集分析工具包clusterProfiler，对筛选出的DEGs进行基因本体（gene ontology，GO）富集分析和京都基因与基因组百科全书（Kyoto encyclopedia of genes and genomes，KEGG）通路分析。利用STRING数据库构建蛋白质-蛋白质相互作用（protein-protein interaction，PPI）网络，并使用网络可视化软件Cytoscape进行蛋白质网络的可视化和枢纽基因筛选。
结果本研究在烟雾病患者的大脑中动脉与颞浅动脉样本间鉴定出138个DEGs，包括18个上调基因和120个下调基因。GO富集分析显示，以上DEGs在细胞外基质、受体配体活性和生长因子活性等方面显著富集，可能与烟雾病相关的血管病变和神经保护机制有关。KEGG通路分析提示，DEGs主要在酪氨酸代谢通路中富集。通过PPI网络分析，共筛选出9个枢纽基因，包括骨膜蛋白（periostin，POSTN）、脑源性神经营养因子（brain derived neurotrophic factor，BDNF）、血小板衍生生长因子受体α（platelet derived growth factor receptor alpha，PDGFRA）、Thy-1细胞表面抗原（Thy-1 cell surface antigen，THY1）、ⅩⅤ型胶原蛋白α1链（collagen type ⅩⅤ alpha 1 chain，COL15A1）、成纤维细胞生长因子7（fibroblast growth factor 7，FGF7）、光蛋白聚糖（lumican，LUM）、层粘连蛋白α2亚基（laminin subunit alpha 2，LAMA2）和RELN（reelin）。此外，上调基因delta样典型Notch配体4（delta like canonical Notch ligand 4，DLL4）在本研究中首次被发现可能在烟雾病中扮演重要角色，或与烟雾病的病理性血管生成有关。
结论细胞外基质、生长因子及其受体的表达失调等可能参与烟雾病的发病过程。DEGs分析筛选出的枢纽基因（POSTN、BDNF、PDGFRA、THY1、COL15A1、FGF7、LUM、LAMA2、RELN）以及DLL4可能在烟雾病的病理形成过程中发挥作用。

关键词: 烟雾病; 生物信息学分析; 基因表达数据; 枢纽基因

CLC Number:

LIU Yang, YANG Junhua, WU Jun, WANG Shuo. Bioinformatics Analysis of Blood Vessel Samples to Identify Potentially Key Genes Associated with Moyamoya Disease[J]. Chinese Journal of Stroke, 2024, 19(4): 431-439.

刘洋, 杨俊华, 吴俊, 王硕. 血管样本生物信息学分析鉴定烟雾病相关的潜在关键基因[J]. 中国卒中杂志, 2024, 19(4): 431-439.

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