Diagnostic classification of schizophrenia by neural network analysis of blood-based gene expression signatures-猫眼课题宝

DOI：

10.1016/j.schres.2009.12.024

发表时间：

2010-06

期刊：

Schizophrenia Research

影响因子：

4.5

通讯作者：

中科院分区：

医学2区

文献类型：

作者：

Makoto Takahashi;Hiroshi Hayashi;Yuichiro Watanabe;K. Sawamura;N. Fukui;J. Watanabe;T. Kitajima;Y. Yamanouchi;N. Iwata;K. Mizukami;T. Hori;K. Shimoda;H. Ujike;N. Ozaki;Kentaro Iijima;Kazuo Takemura;H. Aoshima;T. Someya

基因表达谱与微阵列技术表明，外周血细胞可能是一个替代品，死后脑组织的研究精神分裂症。开发一种可获得的外周生物标志物将大大有助于这种疾病的诊断。我们使用生物信息学方法来检查全血中的基因表达特征是否包含足够的信息来做出精神分裂症的特异性诊断。来自52名无抗精神病药物的精神分裂症患者和49名正常对照的基因表达数据集的非配对t检验鉴定出792个差异表达探针。大卫的功能分析显示，这些基因中有11个先前被报道与精神分裂症相关，其中73个在脑组织中表达。我们分析了数据集与监督分类器之一，人工神经网络（ANN）。样本被细分为训练集和测试集。质量过滤和逐步向前选择确定了14个探针作为诊断的预测因子。然后用选定的探针作为输入，已知诊断的训练集作为输出来训练ANN。所构建的模型在训练集上达到了91.2%的诊断准确率，在保持测试集上达到了87.9%的准确率。另一方面，层次聚类，一个标准的，但无监督的分类器，未能区分患者和对照组。这些结果表明，用监督分类器ANN分析基于血液的基因表达特征可能是精神分裂症的诊断工具。

Gene expression profiling with microarray technology suggests that peripheral blood cells might be a surrogate for postmortem brain tissue in studies of schizophrenia. The development of an accessible peripheral biomarker would substantially help in the diagnosis of this disease. We used a bioinformatics approach to examine whether the gene expression signature in whole blood contains enough information to make a specific diagnosis of schizophrenia. Unpaired t-tests of gene expression datasets from 52 antipsychotics-free schizophrenia patients and 49 normal controls identified 792 differentially expressed probes. Functional profiling with DAVID revealed that eleven of these genes were previously reported to be associated with schizophrenia, and 73 of them were expressed in the brain tissue. We analyzed the datasets with one of the supervised classifiers, artificial neural networks (ANNs). The samples were subdivided into training and testing sets. Quality filtering and stepwise forward selection identified 14 probes as predictors of the diagnosis. ANNs were then trained with the selected probes as the input and the training set for known diagnosis as the output. The constructed model achieved 91.2% diagnostic accuracy in the training set and 87.9% accuracy in the hold-out testing set. On the other hand, hierarchical clustering, a standard but unsupervised classifier, failed to separate patients and controls. These results suggest analysis of a blood-based gene expression signature with the supervised classifier, ANNs, might be a diagnostic tool for schizophrenia.

Diagnostic classification of schizophrenia by neural network analysis of blood-based gene expression signatures

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