Exploration and Verification of Prognostic Value of Endothelial Cells in Glioblastoma
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摘要:目的
探讨并验证胶质母细胞瘤中内皮细胞的预后价值。
方法通过对TCGA和CGGA数据库进行生物信息分析，按照一系列标准筛选GBM单细胞数据中内皮细胞相关标志物并进行单因素Cox回归分析，获取并筛选内皮细胞预后相关标志物，构建内皮细胞相关预后风险评分。qPCR实验验证预后相关标志物在GBM组织及瘤周正常脑组织中的表达差异。Kaplan-Meier法构建生存曲线以鉴定该预后风险评分的预后效能。
结果共筛选获得2 115个GBM预后相关基因，其中1 494个上调，621个下调。GBM单细胞测序分析降维后共获得7群细胞，分别为AC样肿瘤细胞、内皮细胞、单核/巨噬细胞、NB样肿瘤细胞、神经元、OC样肿瘤细胞以及OPC样肿瘤细胞。根据内皮细胞差异基因及相应筛选标准，经分析后共筛选出4个基因（DUSP6、STC1、VWA1和TM4SF1）用于风险评分的构建，并经qPCR实验验证目的基因在GBM组织及瘤周正常脑组织中的表达均显著上调。风险评分=0.171*DUSP6+0.144*STC1+0.041*VWA1−0.004*TM4SF1。
结论本研究构建的胶质母细胞瘤内皮细胞风险评分能够较好地预测患者预后。
Abstract:ObjectiveTo explore and verify the prognostic value of endothelial cells in glioblastoma.
MethodsThrough bioinformatics analysis of the TCGA and CGGA databases, we screened endothelial cell-related markers in GBM single-cell data according to a series of criteria. Moreover, univariate Cox regression analysis was performed to obtain and screen endothelial cell prognosis-related markers and construct endothelial cell-related prognostic risk score. qPCR experiments was used to verify the differences in the expression of prognostic markers in GBM tissues and peritumoral normal brain tissues. Kaplan-Meier method was used to construct the survival curve to identify the prognostic efficacy of the prognostic risk score.
ResultsA total of 2 115 prognostic genes of glioblastoma (GBM) were screened. Among them, 1 494 was upregulated and 621 was downregulated. Seven groups of cells were obtained after GBM single-cell sequencing analysis, including AC-like tumor cells, endothelial cells, monocytes/macrophages, NB-like tumor cells, neurons, OC-like tumor cells, and OPC-like tumor cells. According to the differential genes of endothelial cells and the corresponding screening criteria, four genes (DUSP6, STC1, VWA1, and TM4SF1) were screened for risk-score construction. The expression of the target gene in GBM tissues and normal brain tissues around the tumor was significantly up-regulated detected by qPCR. The risk score=0.171*DUSP6+0.144*STC1+0.041*VWA1−0.004*TM4SF1.
ConclusionThe glioblastoma endothelial cells’ risk score determined in this study can preferably predict the prognosis of patients.
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Key words:
- Glioblastoma /
- Endothelial cell /
- Prognosis /
- Single-cell sequencing
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Competing interests: The authors declare that they have no competing interests.利益冲突声明：所有作者均声明不存在利益冲突。作者贡献：马珩钞、刘羽阳：研究设计、数据收集、文章撰写许 俊：协助处理实验数据、补充研究设计缺陷陶丙岩：提供建议并协助实验操作张 军：指导研究设计、实施及论文撰写
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图 1 GBM单细胞测序分析及内皮细胞预后相关标志物分析
Figure 1 GBM single-cell sequencing analysis and endothelial cell prognostic marker analysis
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图 3 目的基因在GBM组织与瘤周正常脑组织中的表达差异
Figure 3 Difference in the expression of the target gene between GBM tissues and normal brain tissues
表 1 qPCR实验所用引物
Table 1 Primers used in qPCR experiments
Gene name Gene ID Forward primer Reverse primer DUSP6 1 848 5′-GAACTGTGGTGTCTTGGTACATT-3′ 5′-GTTCATCGACAGATTGAGCTTCT-3′ STC1 6781 5′-CACGAGCTGACTTCAACAGGA-3′ 5′-GGATGTGCGTTTGATGTGGG-3′ VWA1 64856 5′-GCAGACTCGGGCTACTATGTG-3′ 5′-CACGTTGGACTCAGGCACTA-3′ TM4SF1 4071 5′-TGTGGCAAACGATGTGCGA-3′ 5′-TGACACAGTAGCCAGATCCTG-3′ 
下载: 导出CSV
表 2 分析及数据可视化所用R包
Table 2 R packages for analysis and data visualization
Analysis item R package used LASSO regression
analysisglmnet (4.1.7) Single factor Cox
regression analysissurvival (3.3.1) and rms (6.3-0) Survival analysis survival (3.3.1), survminer and
ggplot2 (3.3.6)Venn diagram ggplot2 (3.3.6), VennDiagram (1.7.3) Forest plot ggplot2 (3.3.6) Co-expression heat map ggplot2 (3.3.6)
下载: 导出CSV
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