MicroRNA based Pan-Cancer Diagnosis and Treatment Recommendation.
MicroRNA based Pan-Cancer Diagnosis and Treatment Recommendation.
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DOI：
10.1186/s12859-016-1421-y
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发表时间：
2017-01-13
影响因子：
3
通讯作者：
Gevaert O
中科院分区：
文献类型：
--
作者：
Cheerla N;Gevaert O
目前癌症诊断和治疗的最新水平并不理想；诊断测试是准确的，但具有侵入性，治疗是“一刀切”的，而不是个性化的。最近，miRNA作为癌症生物标记物受到了极大的关注，因为它们易于获得(血液中循环的miRNA)和稳定性。已经有许多研究表明miRNA数据在诊断特定癌症类型方面的有效性，但很少有研究探索miRNA在预测治疗结果中的作用。在这里，我们更进一步，使用来自‘癌症基因组图谱’(TCGA)数据库的21种癌症的组织miRNA和临床数据。我们使用机器学习技术来创建一个准确的泛癌症诊断系统，并建立一个治疗结果的预测模型。最后，使用这些模型，我们创建了一个基于网络的工具，用于诊断癌症并推荐最佳治疗方案。采用径向基支持向量机分类器，分类正确率达到97.2%。当爬上胚胎树并对不同阶段的癌症进行分类时，准确率提高到99.9-100%。我们将准确率定义为正确分类的总实例数与总实例数的比率。该分类器也表现良好，在独立的验证数据集上对许多癌症类型实现了80%以上的灵敏度。我们的特征选择算法选择的许多miRNAs以前与各种癌症和肿瘤进展有很强的相关性。然后，使用miRNA、临床和治疗数据，并将其编码为机器学习可读的格式，我们建立了一个预后预测模型，以85%的准确率预测治疗结果。我们使用这个模型创建了一个推荐个性化治疗方案的工具。诊断和预后模型都被上传到网上，便于访问。该模型结合了半监督学习技术，以提高重复使用的准确性。我们的研究朝着使用非侵入性血液测试诊断癌症和预测治疗建议的最终目标迈出了一步。本文的在线版本(doi：10.1186/s12859-0161421-y)包含补充材料，授权用户可以使用。
The current state-of-the-art in cancer diagnosis and treatment is not ideal; diagnostic tests are accurate but invasive, and treatments are “one-size fits-all” instead of being personalized. Recently, miRNA’s have garnered significant attention as cancer biomarkers, owing to their ease of access (circulating miRNA in the blood) and stability. There have been many studies showing the effectiveness of miRNA data in diagnosing specific cancer types, but few studies explore the role of miRNA in predicting treatment outcome. Here we go a step further, using tissue miRNA and clinical data across 21 cancers from the ‘The Cancer Genome Atlas’ (TCGA) database. We use machine learning techniques to create an accurate pan-cancer diagnosis system, and a prediction model for treatment outcomes. Finally, using these models, we create a web-based tool that diagnoses cancer and recommends the best treatment options. We achieved 97.2% accuracy for classification using a support vector machine classifier with radial basis. The accuracies improved to 99.9–100% when climbing up the embryonic tree and classifying cancers at different stages. We define the accuracy as the ratio of the total number of instances correctly classified to the total instances. The classifier also performed well, achieving greater than 80% sensitivity for many cancer types on independent validation datasets. Many miRNAs selected by our feature selection algorithm had strong previous associations to various cancers and tumor progression. Then, using miRNA, clinical and treatment data and encoding it in a machine-learning readable format, we built a prognosis predictor model to predict the outcome of treatment with 85% accuracy. We used this model to create a tool that recommends personalized treatment regimens. Both the diagnosis and prognosis model, incorporating semi-supervised learning techniques to improve their accuracies with repeated use, were uploaded online for easy access. Our research is a step towards the final goal of diagnosing cancer and predicting treatment recommendations using non-invasive blood tests. The online version of this article (doi:10.1186/s12859-016-1421-y) contains supplementary material, which is available to authorized users.