Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis.
Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis.
复制标题
基于代谢组学的新型个性化通路模型揭示了乳腺癌诊断的关键代谢通路。
DOI：
10.1186/s13073-016-0289-9
复制
发表时间：
2016-03-31
期刊：
影响因子：
12.3
通讯作者：
Garmire LX
中科院分区：
文献类型：
--
作者：
Huang S;Chong N;Lewis NE;Jia W;Xie G;Garmire LX
乳腺癌是世界范围内女性最常见的恶性肿瘤，迫切需要更准确的诊断方法来诊断。基于血液的代谢组学是一种很有前途的乳腺癌诊断方法。然而，许多代谢生物标志物很难在研究中重复。我们建议代谢组学数据的高阶功能表示，如基于通路的代谢组学特征，可以用作乳腺癌的稳健生物标志物。为此，我们开发了一种新的计算方法，使用个性化的通路失调评分进行疾病诊断。我们应用这种方法来预测乳腺癌的发生，结合相关特征选择（CFS）和分类方法。所得到的全阶段和早期诊断模型在两组测试血液样本中高度准确，平均AUC（曲线下面积，受试者工作特征曲线）为0.968和0.934，灵敏度为0.946和0.954，特异性为0.934和0.918。这两种基于代谢组学的途径模型通过基于RNA-Seq的TCGA（癌症基因组图谱）乳腺癌数据进一步验证，AUC为0.995和0.993。此外，重要的代谢途径，如牛磺酸和亚牛磺酸代谢以及丙氨酸、天冬氨酸和谷氨酸途径，被揭示为乳腺癌早期诊断的关键生物学途径。我们已经成功地开发了一种新型的基于路径的模型来研究用于疾病诊断的代谢组学数据。将这种方法应用于基于血液的乳腺癌代谢组学数据，我们发现了乳腺癌诊断，特别是早期诊断的关键代谢途径特征。此外，这种建模方法可以推广到用于疾病诊断的其他组学数据类型。本文的在线版本（doi：10.1186/s13073-016-0289-9）包含补充材料，可供授权用户使用。
More accurate diagnostic methods are pressingly needed to diagnose breast cancer, the most common malignant cancer in women worldwide. Blood-based metabolomics is a promising diagnostic method for breast cancer. However, many metabolic biomarkers are difficult to replicate among studies. We propose that higher-order functional representation of metabolomics data, such as pathway-based metabolomic features, can be used as robust biomarkers for breast cancer. Towards this, we have developed a new computational method that uses personalized pathway dysregulation scores for disease diagnosis. We applied this method to predict breast cancer occurrence, in combination with correlation feature selection (CFS) and classification methods. The resulting all-stage and early-stage diagnosis models are highly accurate in two sets of testing blood samples, with average AUCs (Area Under the Curve, a receiver operating characteristic curve) of 0.968 and 0.934, sensitivities of 0.946 and 0.954, and specificities of 0.934 and 0.918. These two metabolomics-based pathway models are further validated by RNA-Seq-based TCGA (The Cancer Genome Atlas) breast cancer data, with AUCs of 0.995 and 0.993. Moreover, important metabolic pathways, such as taurine and hypotaurine metabolism and the alanine, aspartate, and glutamate pathway, are revealed as critical biological pathways for early diagnosis of breast cancer. We have successfully developed a new type of pathway-based model to study metabolomics data for disease diagnosis. Applying this method to blood-based breast cancer metabolomics data, we have discovered crucial metabolic pathway signatures for breast cancer diagnosis, especially early diagnosis. Further, this modeling approach may be generalized to other omics data types for disease diagnosis. The online version of this article (doi:10.1186/s13073-016-0289-9) contains supplementary material, which is available to authorized users.