Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine
Xi-Lin Yang1, Zheng Zeng1, Chen Wang1
1Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
Journal of Molecular Neuroscience : MN
|April 25, 2024
Summary
Researchers developed a predictive model to identify long-term survivors (LTS) of glioblastoma (GB). This model, using four key genes, accurately predicts LTS probability, aiding in understanding glioblastoma patient outcomes.
Area of Science:
- Neuro-oncology
- Immunogenomics
- Computational Biology
Background:
- Glioblastoma (GB) is an aggressive brain tumor with poor prognosis.
- Identifying long-term survivors (LTS) is crucial for personalized treatment strategies.
- The role of immune microenvironment in glioblastoma survival remains incompletely understood.
Purpose of the Study:
- To develop and validate a predictive model for identifying glioblastoma patients with long-term survival (overall survival > 3 years).
- To investigate the differences in immune checkpoint genes (ICGs) and immune infiltration between LTS and short-term survivors (STS).
- To identify key genes associated with long-term survival in glioblastoma.
Main Methods:
- Utilized data from 293 glioblastoma patients (CGGA) for training and 169 (TCGA) for validation.
- Compared gene expression of ICGs and immune infiltration between LTS and STS (overall survival < 1.5 years).
- Employed differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to construct a predictive nomogram.
Main Results:
- Short-term survivors (STS) exhibited an immune-resistant status with higher ICG expression and increased immune suppression compared to LTS.
- Four genes (OSMR, FMOD, CXCL14, TIMP1) were identified as key predictors of long-term survival.
- The constructed nomogram demonstrated good predictive potential for LTS probability in both training (C-index=0.791) and validation (C-index=0.770) cohorts.
Conclusions:
- The developed nomogram effectively predicts long-term survival in glioblastoma patients.
- STS patients are more likely to have an immune-cold phenotype.
- The identified predictive genes and model offer valuable tools for clinical prognostication in glioblastoma.
