Prognostic Prediction Model for Glioblastoma: A Ferroptosis-Related Gene Prediction Model and Independent External Validation.
Wenlin ChenChuxiang LeiYuekun WangDan GuoSumei ZhangXiaoxi WangZixin ZhangYu WangWenbin MaPublished in: Journal of clinical medicine (2023)
Glioblastoma (GBM) is the most common primary malignant intracranial tumor with a poor prognosis. Ferroptosis is a newly discovered, iron-dependent, regulated cell death, and recent studies suggest its close correlation to GBM. The transcriptome and clinical data were obtained for patients diagnosed with GBM from TCGA, GEO, and CGGA. Ferroptosis-related genes were identified, and a risk score model was constructed using Lasso regression analyses. Survival was evaluated by univariate or multivariate Cox regressions and Kaplan-Meier analyses, and further analyses were performed between the high- and low-risk groups. There were 45 ferroptosis-related different expressed genes between GBM and normal brain tissues. The prognostic risk score model was based on four favorable genes, CRYAB, ZEB1, ATP5MC3, and NCOA4 , and four unfavorable genes, ALOX5 , CHAC1, STEAP3, and MT1G . A significant difference in OS between high- and low-risk groups was observed in both the training cohort ( p < 0.001) and the validation cohorts ( p = 0.029 and 0.037). Enrichment analysis of pathways and immune cells and functioning was conducted between the two risk groups. A novel prognostic model for GBM patients was developed based on eight ferroptosis-related genes, suggesting a potential prediction effect of the risk score model in GBM.
Keyphrases
- cell death
- poor prognosis
- genome wide
- end stage renal disease
- newly diagnosed
- ejection fraction
- long non coding rna
- gene expression
- genome wide identification
- prognostic factors
- transcription factor
- dna methylation
- risk assessment
- copy number
- wastewater treatment
- functional connectivity
- multiple sclerosis
- resting state
- bioinformatics analysis
- artificial intelligence