University of Milano-Bicocca, Dept. of Medicine and Surgery, Monza, 20900, Italy.
Stanford University, Dept. of Pathology, California 94305, USA.
Sci Rep. 2017 Apr 7;7:46290. doi: 10.1038/srep46290.
The complicated, evolving landscape of cancer mutations poses a formidable challenge to identify cancer genes among the large lists of mutations typically generated in NGS experiments. The ability to prioritize these variants is therefore of paramount importance. To address this issue we developed OncoScore, a text-mining tool that ranks genes according to their association with cancer, based on available biomedical literature. Receiver operating characteristic curve and the area under the curve (AUC) metrics on manually curated datasets confirmed the excellent discriminating capability of OncoScore (OncoScore cut-off threshold = 21.09; AUC = 90.3%, 95% CI: 88.1-92.5%), indicating that OncoScore provides useful results in cases where an efficient prioritization of cancer-associated genes is needed.
癌症突变的复杂、不断变化的情况给在 NGS 实验中通常产生的大量突变列表中识别癌症基因带来了巨大挑战。因此，优先考虑这些变体的能力至关重要。为了解决这个问题，我们开发了 OncoScore，这是一种文本挖掘工具，它根据可用的生物医学文献，根据与癌症的关联对基因进行排名。在手动整理的数据集上进行的接收器操作特性曲线和曲线下面积 (AUC) 指标验证了 OncoScore 的出色区分能力 (OncoScore 截止阈值=21.09；AUC=90.3%，95%CI：88.1-92.5%)，表明 OncoScore 在需要有效优先考虑癌症相关基因的情况下提供了有用的结果。
