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Journal of Peking University (Health Sciences) ›› 2024, Vol. 56 ›› Issue (2): 199-206. doi: 10.19723/j.issn.1671-167X.2024.02.001

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Dynamic trajectory and cell communication of different cell clusters in malignant progression of glioblastoma

Xiang CAI1,Rendong WANG1,Shijia WANG1,Ziqi REN2,Qiuhong YU2,Dongguo LI1,*()   

  1. 1. Department of Intelligent Medical Engineering, School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
    2. Department of Hyperbaric Oxygen, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
  • Received:2023-08-19 Online:2024-04-18 Published:2024-04-10
  • Contact: Dongguo LI E-mail:ldg213@ccmu.edu.cn

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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

CLC Number: 

  • R739.41

Figure 1

Identification of different cell clusters based on single-cell RNA sequencing data A, UMAP plots of 6 cellular clusters; B, heatmap of the marker genes across individual cell clusters. UMAP, uniform manifold approximation and projection."

Figure 2

Pseudo-time analysis of glioma cell subgroups A, UMAP plot of glioma cells, colored for the 6 cell clusters; B-D, monocle plot of glioma cells in 2D-PCA space, with colors representing time, patient origin, and cell clusters; E, heatmap representing the expression of differentially expressed genes along pseudo-timeline, with enrichment analysis of biological processes from GO database modules on the right. UMAP, uniform manifold approximation and projection; PCA, principal component analysis; GO, Gene Ontology; LGG, lower grade glioma; ndGBM, newly diagnosed GBM; rGBM, recurrence of primary GBM."

Figure 3

Analysis of cellular interactions in immune cell subgroups A, UMAP plot of immune cells, colored for the 7 cell clusters; B, dot plot of the significant signaling pathways and ligand-receptor pairs, where the dot color indicates the probabilities of communication and the dot size indicates the corresponding P values; C, dot plot of interaction strength of different cell clusters; D, heatmap representing the outgoing and incoming signaling patterns. UMAP, uniform manifold approximation and projection; NK, natural killer."

Table 1

Prognostic analysis of pseudo-time differential expression genes in TCGA and CGGA databases"

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

Figure 4

Molecular marker survival analysis"

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