Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach
Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach
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DOI：
10.1016/j.artmed.2008.03.001
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发表时间：
2008-06-01
影响因子：
7.5
通讯作者：
Ball, Graham R.
中科院分区：
文献类型：
--
作者：
Lancashire, Lee J.;Rees, Robert C.;Ball, Graham R.
目的：微阵列技术的出现吸引了生物学家的极大兴趣，因为它具有高通量分析数十万个基因转录本的潜力。随后的数据分析可以鉴定对应于群体内感兴趣的特征的特定特征，例如，分析癌症患者中的基因表达谱以鉴定对应于预后结果的分子标记。这些高通量的技术导致了前所未有的数据生成速度，往往是高复杂性，突出需要新的数据分析方法，将科普这种性质的data of this nature.Methods：逐步方法，使用人工神经网络（ANN）已开发出一个最佳子集的预测基因转录从高维微阵列数据。在这里，这些方法已被应用到一个基因芯片数据集，以确定和验证基因签名对应的雌激素受体和淋巴结状态在乳腺cancer.Results：许多基因转录本被确定，其表达可以区分患者非常高的准确性的基础上，第一，他们是否是阳性或阴性的雌激素受体，其次是否转移到腋窝淋巴结已经发生。这些基因中的一些先前已被报道在癌症中起作用。与其他先前的研究相比，使用的基因明显较少。使用最佳基因子集的模型使用广泛的随机样本交叉验证程序进行内部验证，并使用来自不同患者队列的随访数据集在包含相同和额外探针集的较新阵列芯片上进行外部验证。在这里，模型保持了高精度，强调了这种方法在分析复杂系统中的潜在力量。这些发现显示了所提出的方法如何允许快速分析和随后的基因表达特征的详细询问，以提供对潜在分子机制的进一步理解，这些机制在确定与癌症相关的新型预后标志物方面可能是重要的。(C)2008 Elsevier B.V.保留所有权利。
Objective: The advent of microarrays has attracted considerable interest from biologists due to the potential for high throughput analysis of hundreds of thousands of gene transcripts. Subsequent analysis of the data may identify specific features which correspond to characteristics of interest within the population, for example, analysis of gene expression profiles in cancer patients to identify molecular signatures corresponding with prognostic outcome. These high throughput technologies have resulted in an unprecedented rate of data generation, often of high complexity, highlighting the need for novel data analysis methodologies that will cope with data of this nature.Methods: Stepwise methods using artificial neural networks (ANNs) have been developed to identify an optimal subset of predictive gene transcripts from highly dimensional microarray data. Here these methods have been applied to a gene microarray dataset to identify and validate gene signatures corresponding with estrogen receptor and lymph node status in breast cancer.Results: Many gene transcripts were identified whose expression could differentiate patients to very high accuracies based upon firstly whether they were positive or negative for estrogen receptor, and secondly whether metastasis to the axillary lymph node had occurred. A number of these genes had been previously reported to have a role in cancer. Significantly fewer genes were used compared to other previous studies. The models using the optimal gene subsets were internally validated using an extensive random sample cross-validation procedure and externally validated using a follow up dataset from a different cohort of patients on a newer array chip containing the same and additional probe sets. Here, the models retained high accuracies, emphasising the potential power of this approach in analysing complex systems. These findings show how the proposed method allows for the rapid analysis and subsequent detailed interrogation of gene expression signatures to provide a further understanding of the underlying molecular mechanisms that could be important in determining novel prognostic markers associated with cancer. (C) 2008 Elsevier B.V. All rights reserved.