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Phenotypic composition of salivary gland tumors: an application of principle component analysis to tissue microarray data

机译:唾液腺肿瘤的表型组成:主成分分析在组织芯片数据中的应用

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The tissue organization of the salivary gland is complex, and a large number of salivary gland tumor entities with a broad morphologic spectrum are listed, creating tumor classification schema for the salivary glands that are difficult to understand. In the present study, we attempted to examine how the anatomical components of the salivary gland are associated with morphological subtypes of tumors. We selected a panel of 12 molecules, which labeled one or some of the components, with all of the markers covering every component of the salivary glands. Using tissue microarray, expression profiles of these molecules were examined in four representative spots from each of 88 salivary gland tumors. The resulting large data matrix was analyzed using principle component analysis (PCA). We considered the first three eigenvectors to be significant; as the eigenvalues were more than 1.0 and the cumulative proportion achieved was 67%. Comparison with expression patterns in normal tissue suggested that the three components represented myoepithelial differentiation, and luminal and basal cell phenotypes. Then, we compared the PCA results with individual morphologic subtypes. Individual subtypes were clustered among the three dimensions of the components. This implies that salivary gland tumors may be well characterized by using only three components.
机译:唾液腺的组织结构很复杂,并且列出了大量具有广泛形态学特征的唾液腺肿瘤实体,从而为唾液腺创建了难以理解的肿瘤分类方案。在本研究中,我们试图检查唾液腺的解剖成分如何与肿瘤的形态亚型相关。我们选择了由12个分子组成的小组,这些小组标记了一个或一些成分,所有标记物覆盖了唾液腺的每个成分。使用组织微阵列,在88个唾液腺肿瘤中的每一个的四个代表性斑点中检查了这些分子的表达谱。使用主成分分析(PCA)分析所得的大数据矩阵。我们认为前三个特征向量很重要。因为特征值大于1.0,累计比例为67%。与正常组织中表达模式的比较表明,这三个成分代表肌上皮分化以及腔和基底细胞表型。然后,我们将PCA结果与个体形态亚型进行了比较。各个亚型聚集在组件的三个维度中。这意味着唾液腺肿瘤仅使用三种成分就可以很好地表征。

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