JoVE - Journal of Visualized Experiments
JoVE Visualize
Contact Us

Evaluating the Predictive Value of a Coagulation-Related Gene Model in Glioma

  • 0WuXi Children's Hospital, Department of Neurosurgery, Wuxi, China.
Turkish Neurosurgery +

|

April 23, 2024

|

April 23, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new coagulation-related gene model effectively predicts glioma prognosis. This model, using four genes, identifies high-risk patients with worse survival, offering insights for glioma treatment.

Area Of Science

  • Oncology
  • Molecular Biology
  • Biostatistics

Background

  • Gliomas are primary brain tumors with variable prognoses.
  • Accurate prognostic biomarkers are crucial for guiding glioma treatment strategies.

Purpose Of The Study

  • To develop and validate a coagulation-related gene expression model for predicting glioma prognosis.
  • To assess the model's ability to differentiate between high-risk and low-risk patient groups.

Main Methods

  • Utilized mRNA expression and clinical data from TCGA and CGGA databases.
  • Employed LASSO regression to construct a prognostic model based on coagulation-related genes.
  • Validated the model using ROC analysis and survival metrics (overall survival, progression-free survival).

Main Results

  • Identified four key genes (SERPINA5, PLAUR, BDKRB1, PTGIR) for the prognostic model.
  • The model effectively stratified glioma patients into high-risk and low-risk groups.
  • High-risk patients exhibited significantly worse overall and progression-free survival.
  • The model demonstrated good predictive accuracy with AUC values >0.65 at 1, 3, and 5 years.

Conclusions

  • A novel coagulation-related gene model serves as a reliable prognostic biomarker for gliomas.
  • This model provides valuable insights into glioma pathogenesis and potential therapeutic targets.
  • The findings support the clinical utility of gene expression profiling in glioma management.