Machine learning-based identification of a neutrophil extracellular traps-derived signature for improving outcomes and therapy responses in patients with glioma

Neutrophils, a cellular component in glioma, contribute to its heterogeneous microenvironment and clinical outcomes. This study aimed to develop a neutrophil extracellular traps (NETs)-related signature for prognostic stratification and prediction of immunotherapy efficacy in glioma. Expression data and clinical information from public databases were utilized to establish a prognostic risk model using LASSO and machine learning algorithms. The NETs-based signature demonstrated superior accuracy compared to published signatures and served as an independent prognostic factor. High-risk patients exhibited elevated immune and stromal infiltration. NETs-related gene expression correlated with drug sensitivity, and MMP9 was identified as a hub gene associated with the prognostic model. Immunohistochemistry confirmed MMP9's unfavorable prognosis association. These findings suggest that the NETs-based signature can aid in precise patient stratification and guide immunotherapy and chemotherapy decisions in glioma.