PDF(Pubmed)
Abstract：
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.
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.
摘要：
背景：多形性胶质母细胞瘤（GBM）是最常见和侵袭性的原发性脑肿瘤。先前对GBM生物标志物的研究集中于生物标志物对总生存期(OS)的影响。直到现在,尚未发表评估预测OS的生物标志物性能的研究。我们检查了microRNA的性能，基因表达，基因签名，和甲基化，以前被确定为预后。此外,我们调查了将临床危险因素与生物标志物结合使用是否可以改善预后表现.
方法：癌症基因组图谱，提供生物标志物和操作系统信息，在这项研究中使用。时间依赖性受试者工作特征（ROC）曲线用于评估预后准确性。
结果：关于OS在诊断后2年的预后，微小RNA的ROC曲线下面积（AUC），Mir21和Mir222分别为0.550和0.625。当年龄包括在这些生物标志物的风险预测评分中时，AUC分别增加至0.719和0.701。SAMSN1基因表达的AUC为0.563，而Bao鉴定的“8基因”标记的AUC为0.613。
结论：尽管一些生物标志物与OS显著相关，这些生物标志物预测OS事件的能力有限.纳入临床危险因素，比如年龄,可以大大提高预后表现。
方法：癌症基因组图谱，提供生物标志物和操作系统信息，在这项研究中使用。时间依赖性受试者工作特征（ROC）曲线用于评估预后准确性。
结果：关于OS在诊断后2年的预后，微小RNA的ROC曲线下面积（AUC），Mir21和Mir222分别为0.550和0.625。当年龄包括在这些生物标志物的风险预测评分中时，AUC分别增加至0.719和0.701。SAMSN1基因表达的AUC为0.563，而Bao鉴定的“8基因”标记的AUC为0.613。
结论：尽管一些生物标志物与OS显著相关，这些生物标志物预测OS事件的能力有限.纳入临床危险因素，比如年龄,可以大大提高预后表现。
