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Background <p>Non-apoptotic regulatory cell death (NARCD) encompasses a range of cell death mechanisms beyond apoptosis. It plays a crucial role in the development and progression of brain gliomas; however, the specific regulatory mechanisms involved remain insufficiently explored. This study aims to construct a prognostic model to predict disease progression based on the potential mechanisms of NARCD-related genes in brain gliomas.</p> Methods <p>mRNA expression profiles and clinical data of brain glioma patients were downloaded from The Cancer Genome Atlas (TCGA) database. Control gene expression profiles were obtained from the Genotype-Tissue Expression (GTEx) project. The external validation set was sourced from the Chinese Glioma Genome Atlas (CGGA). Molecular subtyping was performed using unsupervised clustering, and functional analysis was conducted on the differentially expressed genes among the subtypes. A NARCD-related prognostic model was developed using univariate Cox, LASSO, and multivariate Cox regression analysis. Subsequently, patients were stratified into high- and low-risk groups, and comprehensive validations were performed to evaluate the prognostic value of the model. Additionally, the associations between the prognostic model, immune infiltration, and drug sensitivity were explored. Quantitative PCR (q-PCR) experiments were conducted to validate the expression of nine key genes.</p> Results <p>This study categorized brain glioma patients into three subtypes based on key NARCD genes. A prognostic risk model was established using nine differential genes among the three subtypes. Patients with high-risk scores exhibited poorer overall prognosis. Furthermore, in the high-risk population, we observed reduced immune cell infiltration, suppressed immune function, and elevated tumor mutation burden (TMB). Patients in the low-risk group were more sensitive to drugs than those in the high-risk group. The q-PCR results for the characteristic genes were in agreement with the outcomes of the model analysis.</p> Conclusion <p>The NARCD-related prognostic model established in this study serves as a reliable biomarker for predicting clinical outcomes in brain glioma patients, assessing immune infiltration status, and forecasting drug sensitivity.</p>

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Establishment of molecular subtypes and prognostic models for glioblastoma based on non-apoptotic regulatory cell death genes

  • 期刊论文

  • 发表日期：

    2025年10月10日

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  • Wentao Dong,
  • Haidong Zhang,
  • Yangyang Wei

摘要

Background

Non-apoptotic regulatory cell death (NARCD) encompasses a range of cell death mechanisms beyond apoptosis. It plays a crucial role in the development and progression of brain gliomas; however, the specific regulatory mechanisms involved remain insufficiently explored. This study aims to construct a prognostic model to predict disease progression based on the potential mechanisms of NARCD-related genes in brain gliomas.

Methods

mRNA expression profiles and clinical data of brain glioma patients were downloaded from The Cancer Genome Atlas (TCGA) database. Control gene expression profiles were obtained from the Genotype-Tissue Expression (GTEx) project. The external validation set was sourced from the Chinese Glioma Genome Atlas (CGGA). Molecular subtyping was performed using unsupervised clustering, and functional analysis was conducted on the differentially expressed genes among the subtypes. A NARCD-related prognostic model was developed using univariate Cox, LASSO, and multivariate Cox regression analysis. Subsequently, patients were stratified into high- and low-risk groups, and comprehensive validations were performed to evaluate the prognostic value of the model. Additionally, the associations between the prognostic model, immune infiltration, and drug sensitivity were explored. Quantitative PCR (q-PCR) experiments were conducted to validate the expression of nine key genes.

Results

This study categorized brain glioma patients into three subtypes based on key NARCD genes. A prognostic risk model was established using nine differential genes among the three subtypes. Patients with high-risk scores exhibited poorer overall prognosis. Furthermore, in the high-risk population, we observed reduced immune cell infiltration, suppressed immune function, and elevated tumor mutation burden (TMB). Patients in the low-risk group were more sensitive to drugs than those in the high-risk group. The q-PCR results for the characteristic genes were in agreement with the outcomes of the model analysis.

Conclusion

The NARCD-related prognostic model established in this study serves as a reliable biomarker for predicting clinical outcomes in brain glioma patients, assessing immune infiltration status, and forecasting drug sensitivity.