胶质母细胞瘤恶性进展中不同细胞亚群的动态轨迹和细胞通讯网络

doi:10.19723/j.issn.1671-167X.2024.02.001

摘要/Abstract

摘要：

目的: 探索胶质母细胞瘤(glioblastoma, GBM)恶性进展过程中细胞亚群的动态轨迹以及免疫细胞亚群之间的通讯网络，结合GBM患者的转录组数据和临床信息，挖掘GBM恶性进展过程中的关键风险标志物，以期为该疾病的治疗和预后提供科学依据。方法: 基于单细胞测序数据分析方法，构建GBM恶性进展中的细胞亚群图谱，利用Monocle2技术构建GBM恶性进展中肿瘤细胞亚群的动态进展轨迹，基于基因富集分析，挖掘肿瘤细胞亚群随GBM恶性进展中显著变化的基因所富集的生物学过程，利用CellChat软件识别不同免疫细胞亚群间的复杂通讯网络，通过生存分析识别GBM恶性进展中影响患者预后的关键风险分子标记物。结果: 单细胞测序数据分析识别出6种不同的细胞类型，包括淋巴细胞、周细胞、少突神经胶质细胞、巨噬细胞、胶质瘤细胞、小胶质细胞，单细胞数据集中了27 151个细胞，其中包含3 881个来源于低级别胶质瘤患者的细胞，10 166个来源于新诊断GBM患者的细胞，13 104个来源于复发性胶质瘤患者的细胞。胶质瘤细胞亚群逆时序分析提示，胶质瘤细胞亚群在恶性进展中存在着明显的细胞异质性；免疫细胞亚群的细胞相互作用分析揭示，GBM恶性进展中不同免疫细胞亚群之间的通讯网络共识别出22条具有生物学意义的配体-受体对，涉及12条通路；生存分析识别出8个与GBM患者预后密切相关的基因，其中SERPINE1、COL6A1、SPP1、LTF、C1S、AEBP1、SAA1L是GBM患者的高风险基因，ABCC8是GBM患者的低风险基因。结论: 深入揭示了GBM恶性进展中胶质瘤细胞亚群的动态变化以及免疫细胞亚群之间的通讯模式，对于理解GBM的复杂生物学过程具有重要意义，为GBM的精准医疗和治疗决策提供了科学依据，也为GBM患者更准确的预后评估提供了新的线索。

关键词: 胶质母细胞瘤, 单细胞测序, 拟时序分析, 细胞相互作用, 细胞间通讯

Abstract:

Objective: To delve deeply into the dynamic trajectories of cell subpopulations and the communication network among immune cell subgroups during the malignant progression of glioblastoma (GBM), and to endeavor to unearth key risk biomarkers in the GBM malignancy progression, so as to provide a more profound understanding for the treatment and prognosis of this disease by integrating transcriptomic data and clinical information of the GBM patients. Methods: Utilizing single-cell sequencing data analysis, we constructed a cell subgroup atlas during the malignant progression of GBM. The Monocle2 tool was employed to build dynamic progression trajectories of the tumor cell subgroups in GBM. Through gene enrichment analysis, we explored the biological processes enriched in genes that significantly changed with the malignancy progression of GBM tumor cell subpopulations. CellChat was used to identify the communication network between the different immune cell subgroups. Survival analysis helped in identifying risk molecular markers that impacted the patient prognosis during the malignant progression of GBM. This method ological approach offered a comprehensive and detailed examination of the cellular and molecular dynamics within GBM, providing a robust framework for understanding the disease' s progression and potential therapeutic targets. Results: The analysis of single-cell sequencing data identified 6 different cell types, including lymphocytes, pericytes, oligodendrocytes, macrophages, glioma cells, and microglia. The 27 151 cells in the single-cell dataset included 3 881 cells from the patients with low-grade glioma (LGG), 10 166 cells from the patients with newly diagnosed GBM, and 13 104 cells from the patients with recurrent glioma (rGBM). The pseudo-time analysis of the glioma cell subgroups indicated significant cellular heterogeneity during malignant progression. The cell interaction analysis of immune cell subgroups revealed the communication network among the different immune subgroups in GBM malignancy, identifying 22 biologically significant ligand-receptor pairs across 12 key biological pathways. Survival analysis had identified 8 genes related to the prognosis of the GBM patients, among which SERPINE1, COL6A1, SPP1, LTF, C1S, AEBP1, and SAA1L were high-risk genes in the GBM patients, and ABCC8 was low-risk genes in the GBM patients. These findings not only provided new theoretical bases for the treatment of GBM, but also offered fresh insights for the prognosis assessment and treatment decision-making for the GBM patients. Conclusion: This research comprehensively and profoundly reveals the dynamic changes in glioma cell subpopulations and the communication patterns among the immune cell subgroups during the malignant progression of GBM. These findings are of significant importance for understanding the complex biological processes of GBM, providing crucial new insights for precision medicine and treatment decisions in GBM. Through these studies, we hope to provide more effective treatment options and more accurate prognostic assessments for the patients with GBM.

Key words: Glioblastoma, Single-cell sequencing, Pseudo-time analysis, Cell-cell interaction, Cell communication

中图分类号:

R739.41

蔡祥,王仁东,王世佳,任梓齐,于秋红,李冬果. 胶质母细胞瘤恶性进展中不同细胞亚群的动态轨迹和细胞通讯网络[J]. 北京大学学报（医学版）, 2024, 56(2): 199-206.

Xiang CAI,Rendong WANG,Shijia WANG,Ziqi REN,Qiuhong YU,Dongguo LI. Dynamic trajectory and cell communication of different cell clusters in malignant progression of glioblastoma[J]. Journal of Peking University (Health Sciences), 2024, 56(2): 199-206.

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Gene	P (TCGA)	P (CGGA)
SERPINE1	0.047	0.038
ABCC8	0.016	0.039
COL6A1	0.009	0.021
SPP1	0.047	0.016
LTF	0.024	0.014
C1S	0.046	0.037
AEBP1	0.019	5.861×10^-4
SAA1	0.020	0.028

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