Increasing the Number of Thyroid Lesions Classes in Microarray Analysis Improves the Relevance of Diagnostic Markers
Increasing the Number of Thyroid Lesions Classes in Microarray Analysis Improves the Relevance of Diagnostic Markers
复制标题
DOI：
10.1371/journal.pone.0007632
复制
发表时间：
2009-10-29
期刊：
影响因子：
3.7
通讯作者：
Savagner, Frederique
中科院分区：
文献类型：
--
作者：
Fontaine, Jean-Fred;Mirebeau-Prunier, Delphine;Savagner, Frederique
背景：迄今为止，由于通常考虑的甲状腺病变类别很少，通过微阵列分析识别的甲状腺癌遗传标记物提供的预测准确性有限。为了提高诊断相关性，我们同时分析了来自 6 个公共数据集的微阵列数据，涵盖总共 347 个甲状腺组织样本，代表滤泡病变和正常甲状腺组织的 12 个组织学类别。我们自己的数据集包含大约一半的甲状腺组织样本，包括所有类别的甲状腺病变。方法/主要发现：分类器预测受到类别之间的相似性以及训练集中类别数量的强烈影响。在每个数据集中，通过根据类相似性将样本分为三组来改进样本预测。差异基因的交叉验证揭示了四个具有功能富集的簇。对 49 个新样本中其中 6 个基因（APOD、APOE、CLGN、CRABP1、SDHA 和 TIMP1）的分析显示，基因和蛋白质谱与观察到的类别相似性一致。我们重点关注滤泡性肿瘤的四个亚类，通过对其他 32 个新样本进行实时定量 RT-PCR，探讨了 12 个选定标记物（CASP10、CDH16、CLGN、CRABP1、HMGB2、ALPL2、ADAMTS2、CABIN1、ALDH1A3、USP13、NR2F2、KRTHB5）的诊断潜力。参考 Pax8-PPAR gamma、TSHR、GNAS 和 NRAS 基因的突变状态检查滤泡性肿瘤的基因表达谱。结论/意义：我们表明，当使用大量样本和组织类别时，基于微阵列数据定义的诊断工具更相关。考虑到甲状腺肿瘤病理之间的关系，以及所涉及的主要生物学功能和途径，提高了样本的诊断准确性。我们的方法与微滤泡腺瘤的分类特别相关。
Background: Genetic markers for thyroid cancers identified by microarray analysis have offered limited predictive accuracy so far because of the few classes of thyroid lesions usually taken into account. To improve diagnostic relevance, we have simultaneously analyzed microarray data from six public datasets covering a total of 347 thyroid tissue samples representing 12 histological classes of follicular lesions and normal thyroid tissue. Our own dataset, containing about half the thyroid tissue samples, included all categories of thyroid lesions.Methodology/Principal Findings: Classifier predictions were strongly affected by similarities between classes and by the number of classes in the training sets. In each dataset, sample prediction was improved by separating the samples into three groups according to class similarities. The cross-validation of differential genes revealed four clusters with functional enrichments. The analysis of six of these genes (APOD, APOE, CLGN, CRABP1, SDHA and TIMP1) in 49 new samples showed consistent gene and protein profiles with the class similarities observed. Focusing on four subclasses of follicular tumor, we explored the diagnostic potential of 12 selected markers (CASP10, CDH16, CLGN, CRABP1, HMGB2, ALPL2, ADAMTS2, CABIN1, ALDH1A3, USP13, NR2F2, KRTHB5) by real-time quantitative RT-PCR on 32 other new samples. The gene expression profiles of follicular tumors were examined with reference to the mutational status of the Pax8-PPAR gamma, TSHR, GNAS and NRAS genes.Conclusion/Significance: We show that diagnostic tools defined on the basis of microarray data are more relevant when a large number of samples and tissue classes are used. Taking into account the relationships between the thyroid tumor pathologies, together with the main biological functions and pathways involved, improved the diagnostic accuracy of the samples. Our approach was particularly relevant for the classification of microfollicular adenomas.