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<p>Glioblastoma (GBM) is the most common primary brain cancer, and the prognosis of traditional treatment methods is not favorable. Therefore, we aimed to identify new molecular targets and construct a risk model for GBM diagnosis and prognosis prediction on the basis of neutrophil extracellular trap (NET)-related genes. Gene expression and clinical data were obtained from the TCGA. We constructed a risk model based on four NET-related genes, namely, <i>MAPK1, P2RX1</i>, <i>PARVB</i> and <i>STAT3</i>, using univariate, LASSO and multivariate Cox analyses. The receiver operating characteristic curve analysis with the pROC package revealed that the area under the curve (AUC) values for 1-, 2- and 3-year survival were 0.73, 0.81 and 0.83, respectively. In addition, correlation analysis revealed that <i>MAPK1</i> expression was negatively correlated with IL15, whereas <i>PARVB</i>, <i>P2RX1</i> and <i>STAT3</i> expression levels were positively correlated with IL15, PADI4, CXCL1 and CSF1. In vitro, overexpression of <i>PARVB</i> and <i>P2RX1 STAT3</i> and the knockdown of <i>MAPK1</i> promoted the malignant behaviors of GBM cells and activated EMT pathways. Drug sensitivity analysis revealed that STAT3 expression was positively associated with sensitivity to most drugs, whereas MAPK1, PARVB and P2RX1 expression levels were negatively correlated with most drug sensitivity. In conclusion, we identified four NET-related hub genes related to the prognosis of GBM and constructed a risk model with good performance.</p>

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A novel neutrophil extracellular trap-related gene signature for predicting glioblastoma prognosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月14日

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  • Chao Huang,
  • Xue-bin Yu,
  • Yong-zhi Zhou

摘要

Glioblastoma (GBM) is the most common primary brain cancer, and the prognosis of traditional treatment methods is not favorable. Therefore, we aimed to identify new molecular targets and construct a risk model for GBM diagnosis and prognosis prediction on the basis of neutrophil extracellular trap (NET)-related genes. Gene expression and clinical data were obtained from the TCGA. We constructed a risk model based on four NET-related genes, namely, MAPK1, P2RX1, PARVB and STAT3, using univariate, LASSO and multivariate Cox analyses. The receiver operating characteristic curve analysis with the pROC package revealed that the area under the curve (AUC) values for 1-, 2- and 3-year survival were 0.73, 0.81 and 0.83, respectively. In addition, correlation analysis revealed that MAPK1 expression was negatively correlated with IL15, whereas PARVB, P2RX1 and STAT3 expression levels were positively correlated with IL15, PADI4, CXCL1 and CSF1. In vitro, overexpression of PARVB and P2RX1 STAT3 and the knockdown of MAPK1 promoted the malignant behaviors of GBM cells and activated EMT pathways. Drug sensitivity analysis revealed that STAT3 expression was positively associated with sensitivity to most drugs, whereas MAPK1, PARVB and P2RX1 expression levels were negatively correlated with most drug sensitivity. In conclusion, we identified four NET-related hub genes related to the prognosis of GBM and constructed a risk model with good performance.