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Published on: October 4, 2019
Gene expression profile as a prognostic factor in high-grade gliomas
Tomasz Czernicki1, Jolanta Zegarska, Leszek Paczek
1Department of Neurosurgery, Medical University of Warsaw, 02-097 Warsaw, Poland. tczernicki@wp.pl
International Journal of Oncology
|December 5, 2006
Summary
Gene expression profiling offers a more precise way to predict high-grade glioma outcomes than traditional clinical factors. This genetic predictor proved to be the sole independent factor in survival analysis, highlighting its importance.
Area of Science:
- Neuro-oncology
- Genomics
- Molecular Biology
Background:
- Clinical factors offer limited precision in predicting high-grade glioma prognosis.
- Tumor behavior is primarily driven by gene alterations, necessitating advanced predictive markers.
- Gene profiling using microarray technology shows promise for improved prognostic accuracy.
Purpose of the Study:
- To evaluate if gene expression profiling is a superior predictor of malignant glioma prognosis compared to clinical parameters.
- To identify a gene-based prediction model for high-grade gliomas.
Main Methods:
- Gene expression analysis of 28 gliomas (WHO grades II-IV) and 5 normal brain samples using oligonucleotide arrays (3,757 genes).
- Signal-to-noise statistics and leave-one-out cross-validation to identify differentially expressed genes.
- Fuzzy c-means clustering for gene prediction model development and survival analysis incorporating clinical factors.
Main Results:
- 7 or 9 differentially expressed genes clearly separated gliomas from normal brain samples.
- Gene expression profile was a significant predictor of survival in univariate analysis.
- Gene expression profile emerged as the only independent predictor of survival in multivariate analysis (p = 0.007).
Conclusions:
- Gene expression profiling is a powerful tool for predicting outcomes in high-grade gliomas.
- Genetic markers provide more accurate prognostic information than clinical factors alone.
- This study establishes gene expression patterns as an independent predictor of survival in malignant gliomas.
