Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering.
Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering.
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从基因表达数据中发现生物标志物，用于使用神经网络和关系模糊聚类来预测癌症亚组。
DOI：
10.1186/1471-2105-8-5
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发表时间：
2007-01-06
影响因子：
3
通讯作者：
Amari, Shun-ichi
中科院分区：
文献类型：
--
作者：
Pal, Nikhil R.;Aguan, Kripamoy;Sharma, Animesh;Amari, Shun-ichi
神经母细胞瘤、非霍奇金淋巴瘤、横纹肌肉瘤和尤文肉瘤这四种不同类型的儿童癌症表现出与小圆蓝细胞瘤(SRBCT)相似的组织学特征，因此常常导致误诊。识别用于区分这些癌症的生物标志物是一个研究得很好的问题。现有的方法通常单独评估每个基因，并且没有考虑基因和用于设计诊断预测系统的工具之间的非线性相互作用。因此，更多的基因通常被认为是预测所必需的。我们提出了一个寻找一小部分生物标志物的一般方案，以设计一个准确分类癌症亚组的诊断系统。我们使用具有在线基因选择能力的多层网络和关系模糊聚类来识别一小部分生物标记物，以准确分类研究良好的数据集的训练和盲测例集。我们的方法只在训练样本和盲样本中识别了七个准确分类癌症四个亚组的生物标记物。对于同样的问题，其他人提出了19-94个基因。这7个生物标记物包括3个新基因(NAB2、LSP1和EHD1-未被他人识别)，它们具有明显的类特异性特征，在肿瘤生物学中发挥重要作用，包括细胞增殖、跨内皮细胞迁移和MHC类抗原的运输。有趣的是，NAB2在其他肿瘤中表达下调，包括非霍奇金淋巴瘤和神经母细胞瘤，但我们观察到在少数尤文肉瘤和拉巴肌肉瘤中表达中等到高度上调，这表明NAB2在这些肿瘤中可能发生突变。这些基因可以通过无监督学习正确地发现子群，可以区分非SRBCT样本，并且它们与包括支持向量机在内的其他机器学习工具具有同样好的性能。这些生物标志物为诊断任务带来了四条简单的人类可解释的规则。虽然提出的方法是在SRBCT数据集上测试的，但它是相当通用的，可以应用于其他癌症数据集。我们的方案考虑了基因之间的相互作用以及基因与工具之间的相互作用，因此能够找到非常小的集合，并且可以发现新的基因。我们的发现表明，有可能开发专门的微阵列芯片，或使用实时定量聚合酶链式反应分析或基于抗体的方法，如酶联免疫吸附试验和蛋白质印迹分析，以方便和低成本地诊断亚群。
The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell tumor (SRBCT) and thus often leads to misdiagnosis. Identification of biomarkers for distinguishing these cancers is a well studied problem. Existing methods typically evaluate each gene separately and do not take into account the nonlinear interaction between genes and the tools that are used to design the diagnostic prediction system. Consequently, more genes are usually identified as necessary for prediction. We propose a general scheme for finding a small set of biomarkers to design a diagnostic system for accurate classification of the cancer subgroups. We use multilayer networks with online gene selection ability and relational fuzzy clustering to identify a small set of biomarkers for accurate classification of the training and blind test cases of a well studied data set. Our method discerned just seven biomarkers that precisely categorized the four subgroups of cancer both in training and blind samples. For the same problem, others suggested 19–94 genes. These seven biomarkers include three novel genes (NAB2, LSP1 and EHD1 – not identified by others) with distinct class-specific signatures and important role in cancer biology, including cellular proliferation, transendothelial migration and trafficking of MHC class antigens. Interestingly, NAB2 is downregulated in other tumors including Non-Hodgkin lymphoma and Neuroblastoma but we observed moderate to high upregulation in a few cases of Ewing sarcoma and Rabhdomyosarcoma, suggesting that NAB2 might be mutated in these tumors. These genes can discover the subgroups correctly with unsupervised learning, can differentiate non-SRBCT samples and they perform equally well with other machine learning tools including support vector machines. These biomarkers lead to four simple human interpretable rules for the diagnostic task. Although the proposed method is tested on a SRBCT data set, it is quite general and can be applied to other cancer data sets. Our scheme takes into account the interaction between genes as well as that between genes and the tool and thus is able find a very small set and can discover novel genes. Our findings suggest the possibility of developing specialized microarray chips or use of real-time qPCR assays or antibody based methods such as ELISA and western blot analysis for an easy and low cost diagnosis of the subgroups.
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