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通过RNA表达谱预测1号染色体1p/19q共缺失:当前预测模型的比较

Predicting chromosome 1p/19q codeletion by RNA expression profile: a comparison of current prediction models.

作者信息

Wang Zhi-Liang, Zhao Zheng, Wang Zheng, Zhang Chuan-Bao, Jiang Tao

机构信息

Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.

Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

出版信息

Aging (Albany NY). 2019 Feb 2;11(3):974-985. doi: 10.18632/aging.101795.

Abstract

BACKGROUND

Chromosome 1p/19q codeletion is increasingly being recognized as the crucial genetic marker for glioma patients and have been included in WHO classification of glioma in 2016. Fluorescent in situ hybridization, a widely used method in detecting 1p/19q status, has some methodological limitations which might influence the clinical management for doctors. Here, we attempted to explore an RNA sequencing computational method to detect 1p/19q status.

METHODS

We included 692 samples with 1p/19q status information from TCGA cohort as training set and 222 samples with 1p/19q status information from REMBRANDT cohort as validation set. We reviewed and compared five tools: TSPairs, GSVA, PAM, Caret, smoother, with respect to their accuracy, sensitivity and specificity.

RESULTS

In TCGA cohort, the GSVA method showed the highest accuracy (98.4%) in predicting 1p/19q status (sensitivity=95.5%, specificity=99.6%) and smoother method showed the second-highest accuracy (accuracy=97.8%, sensitivity=96.4%, specificity=98.3%). While in REMBRANDT cohort, smoother method exhibited the highest accuracy (98.6%) (sensitivity= 96.7%, specificity=98.9%) in 1p/19q status prediction.

CONCLUSIONS

Our independent assessment of five tools revealed that smoother method was selected as the most stable and accurate method in predicting 1p/19q status. This method could be regarded as a potential alternative method for clinical practice in future.

摘要

背景

1号染色体短臂/19号染色体长臂共缺失日益被认为是胶质瘤患者的关键遗传标志物,并已被纳入2016年世界卫生组织胶质瘤分类中。荧光原位杂交是检测1p/19q状态的一种广泛使用的方法,但存在一些方法学上的局限性,这可能会影响医生的临床管理。在此,我们试图探索一种RNA测序计算方法来检测1p/19q状态。

方法

我们纳入了来自TCGA队列的692个具有1p/19q状态信息的样本作为训练集,以及来自REMBRANDT队列的222个具有1p/19q状态信息的样本作为验证集。我们审查并比较了五种工具:TSPairs、GSVA、PAM、Caret、smoother,比较了它们的准确性、敏感性和特异性。

结果

在TCGA队列中,GSVA方法在预测1p/19q状态方面显示出最高的准确性(98.4%)(敏感性=95.5%,特异性=99.6%),smoother方法显示出第二高的准确性(准确性=97.8%,敏感性=96.4%,特异性=98.3%)。而在REMBRANDT队列中,smoother方法在1p/19q状态预测中表现出最高的准确性(98.6%)(敏感性=96.7%,特异性=98.9%)。

结论

我们对五种工具的独立评估表明,smoother方法被选为预测1p/19q状态最稳定、最准确的方法。该方法未来可被视为临床实践中一种潜在的替代方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dad9/6382420/cc047085ad98/aging-11-101795-g001.jpg

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