Evaluating the Prognostic Accuracy of Biomarkers for Glioblastoma Multiforme Using The Cancer Genome Atlas Data

• Main Authors: Nan Hu, Haojie Cheng, Kevin Zhang, Randy Jensen
• Format: Article
• Language: English
• Published: SAGE Publishing 2017-10-01
• Series: Cancer Informatics
• Online Access: https://doi.org/10.1177/1176935117734844

Background: Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumor. Previous studies on GBM biomarkers focused on the effect of the biomarkers on overall survival (OS). Until now, no study has been published that evaluates the performance of biomarkers for prognosing OS. We examined the performance of microRNAs, gene expressions, gene signatures, and methylation that were previously identified to be prognostic. In addition, we investigated whether using clinical risk factors in combination with biomarkers can improve the prognostic performance. Methods: The Cancer Genome Atlas, which provides both biomarkers and OS information, was used in this study. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic accuracy. Results: For prognosis of OS by 2 years from diagnosis, the area under the ROC curve (AUC) of microRNAs, Mir21 and Mir222, was 0.550 and 0.625, respectively. When age was included in the risk prediction score of these biomarkers, the AUC increased to 0.719 and 0.701, respectively. The SAMSN1 gene expression attains an AUC of 0.563, and the “8-gene” signature identified by Bao achieves an AUC of 0.613. Conclusions: Although some biomarkers are significantly associated with OS, the ability of these biomarkers for prognosing OS events is limited. Incorporating clinical risk factors, such as age, can greatly improve the prognostic performance.