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首页> 外文期刊>Cellular Oncology: Analytical Cellular Pathology >Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature Maps
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Chromosomal Regions in Prostatic Carcinomas Studied by Comparative Genomic Hybridization, Hierarchical Cluster Analysis and Self-Organizing Feature Maps

机译：比较基因组杂交，层次聚类分析和自组织特征图研究前列腺癌的染色体区域

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Comparative genomic hybridization (CGH) is an established genetic method which enables a genome‐wide survey of chromosomal imbalances. For each chromosome region, one obtains the information whether there is a loss or gain of genetic material, or whether there is no change at that place. Therefore, large amounts of data quickly accumulate which must be put into a logical order. Cluster analysis can be used to assign individual cases (samples) to different clusters of cases, which are similar and where each cluster may be related to a different tumour biology. Another approach consists in a clustering of chromosomal regions by rewriting the original data matrix, where the cases are written as rows and the chromosomal regions as columns, in a transposed form. In this paper we applied hierarchical cluster analysis as well as two implementations of self‐organizing feature maps as classical and neuronal tools for cluster analysis of CGH data from prostatic carcinomas to such transposed data sets. Self‐organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule. We studied a group of 48 cases of incidental carcinomas, a tumour category which has not been evaluated by CGH before. In addition we studied a group of 50 cases of pT2N0‐tumours and a group of 20 pT3N0‐carcinomas. The results show in all case groups three clusters of chromosomal regions, which are (i) normal or minimally affected by losses and gains, (ii) regions with many losses and few gains and (iii) regions with many gains and few losses. Moreover, for the pT2N0‐ and pT3N0‐groups, it could be shown that the regions 6q, 8p and 13q lay all on the same cluster (associated with losses), and that the regions 9q and 20q belonged to the same cluster (associated with gains). For the incidental cancers such clear correlations could not be demonstrated.
机译：比较基因组杂交（CGH）是一种公认​​的遗传方法，可以对染色体失衡进行全基因组调查。对于每个染色体区域，人们都可以获取信息，即遗传物质的损失或增加，或者该位置是否没有变化。因此，大量的数据迅速积累，必须将它们放在逻辑顺序中。聚类分析可用于将个别病例（样本）分配给不同的病例群，这些病例群相似并且每个聚类可能与不同的肿瘤生物学相关。另一种方法是通过重写原始数据矩阵对染色体区域进行聚类，在这种情况下，个案以转置形式写为行，而染色体区域写为列。在本文中，我们应用了层次聚类分析以及自组织特征图的两种实现方式，作为经典和神经元工具，用于对从前列腺癌到此类转置数据集的CGH数据进行聚类分析。自组织图是人工神经网络，能够在无监督的学习规则的基础上形成集群。我们研究了一组48例偶发性癌，这是CGH之前尚未评估的肿瘤类别。此外，我们研究了一组50例pT2N0肿瘤和一组20例pT3N0癌。结果显示，在所有情况下，三组染色体区域均受（i）正常或最小程度受损失和收益影响的区域;（ii）损失多且收益少的区域;（iii）收益多而损失少的区域。此外，对于pT2N0-和pT3N0-组，可以显示区域6q，8p和13q都位于同一群集（与损耗相关），而区域9q和20q属于同一群集（与损耗相关）收益）。对于偶然的癌症，这种明显的相关性无法得到证实。

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