An analysis of prognostic risk and immunotherapy response of glioblastoma patients based on single-cell landscape and nitrogen metabolism.

in Neurobiology of disease by Minfeng Tong, Zhijian Xu, Lude Wang, Huahui Chen, Xing Wan, Hu Xu, Song Yang, Qi Tu

TLDR

  • The study identified nitrogen metabolism-related biomarkers for GBM prognosis and immunotherapy by analyzing single-cell data and constructed a 7-gene prognostic model.
  • The researchers found significant differences in survival rates, immune levels, gene mutations, and sensitivity to drugs between cluster1 and cluster2.

Abstract

Glioblastoma (GBM) is a highly invasive brain tumor of astrocytic origin. Nitrogen metabolism plays an instrumental role in the growth and progression of various tumors, including GBM. This study intended to mine nitrogen metabolism-related biomarkers for GBM-related research of prognosis and immunotherapy. Through single-cell data analysis of GBM, we identified four cell types (Astrocytes, Macrophages, Fibroblasts, and Endothelial cells). We calculated the nitrogen metabolism scores and conducted trajectory analysis for the most abundant cells, Astrocytes, revealing 6 differentiation directions of Astrocytes, which included the main differentiation direction from cells with low nitrogen metabolism scores to cells with high nitrogen metabolism scores. Furthermore, based on the differentially expressed genes (DEGs) with high/low nitrogen metabolism scores, we constructed a 7-gene prognostic model by utilizing regression analysis. qRT-PCR analysis showed that IGFBP2, CHPF, CTSZ, UPP1, TCF12, ZBTB20 and RBP1 were all significantly up-regulated in the GBM cells. Through differential analysis, a protein-protein interaction (PPI) network, and enrichment analyses, we identified and analyzed the DEGs in the high RiskScore subgroup, revealing complex interactions among DEGs, which were mainly related to pathways such as TNF signaling pathway and NF-κB signaling pathway. By leveraging univariate analysis, survival-related genes were selected from the nitrogen metabolism-related gene sets. Clustering, survival, immune, and mutation analyses manifested that the collected nitrogen metabolism-related genes had good classification performance, presenting notable differences in survival rates, immune levels, gene mutations, and sensitivity to drugs between cluster1 and cluster2. In conclusion, the project investigated the prognosis and classification value of nitrogen metabolism-related genes in GBM from multiple perspectives, predicting the sensitivity of different subtypes of patients to immunotherapy response and drug sensitivity. These findings are expected to show new research directions for further exploration in these fields.

Overview

  • The study aimed to identify nitrogen metabolism-related biomarkers for glioblastoma (GBM) prognosis and immunotherapy by analyzing single-cell data of GBM.
  • The study focused on identifying four cell types (Astrocytes, Macrophages, Fibroblasts, and Endothelial cells) and calculating nitrogen metabolism scores to analyze their trajectories.
  • The researchers constructed a 7-gene prognostic model using regression analysis and validated it using qRT-PCR analysis, identifying seven significantly up-regulated genes in GBM cells.

Comparative Analysis & Findings

  • The study revealed that the high-risk subgroup had complex interactions among differentially expressed genes, mainly related to the TNF signaling pathway and NF-κB signaling pathway.
  • The researchers found that the nitrogen metabolism-related genes had good classification performance, presenting notable differences in survival rates, immune levels, gene mutations, and sensitivity to drugs between cluster1 and cluster2.
  • The study identified a 7-gene prognostic model that predicted the sensitivity of different subtypes of patients to immunotherapy response and drug sensitivity.

Implications and Future Directions

  • The study's findings provide new research directions for exploring the role of nitrogen metabolism in GBM prognosis and immunotherapy.
  • Future research could focus on validating the identified biomarkers and predicting their utility in clinical practice.
  • The study's approach could be applied to analyze other cancers to identify novel biomarkers and therapeutic targets.
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