Gene expression profiling of papillary thyroid carcinomas in Korean patients by oligonucleotide microarrays.
Gene expression profiling of papillary thyroid carcinomas in Korean patients by oligonucleotide microarrays.
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DOI：
10.4174/jkss.2012.82.5.271
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发表时间：
2012-05
影响因子：
--
通讯作者：
Kim SW
中科院分区：
文献类型：
--
作者：
Chung KW;Kim SW;Kim SW
甲状腺乳头状癌（PTC）的发病率在韩国迅速增加。分析PTCs的基因表达谱（GEP）将有助于诊断、鉴定和治疗新方法的出现。我们进行了这项研究，以找到韩国PTC的GEP。我们对19例PTC和7例正常甲状腺进行了寡核苷酸芯片分析。使用t检验选择差异表达的基因（|倍|> 3）和调整的Benjamini-Hochberg错误发现率P值<0.01。定量逆转录-聚合酶链反应（QRT-PCR）用于验证微阵列数据。利用支持向量机（SVM）算法建立了一种基于分子特征的PTC分类模型。根据筛选标准，共筛选出79个差异表达基因（70个上调，9个下调）。对5个基因（CDH3、NGEF、PROS1、TGFA、MET）的QRT-PCR验证了微阵列数据。基于GEP的分级聚类分析和SVM分类模型能够准确区分PTCs和正常甲状腺组织。疾病分类模型在诊断PTC方面表现出出色的准确性，从而显示了未来分子诊断的可能性。该GEP可作为进一步研究基于分子特征的PTC管理的基线数据。
The incidence of papillary thyroid carcinomas (PTCs) is rapidly increasing in Korea. Analyzing the gene expression profiling (GEP) of PTCs will facilitate the advent of new methods in diagnosis, prognostication, and treatment. We performed this study to find the GEP of Korean PTCs. We performed oligonucleotide microarray analysis with 19 PTCs and 7 normal thyroid glands. Differentially expressed genes were selected using a t-test (|fold| >3) and adjusted Benjamini-Hochberg false discovery rate P-value < 0.01. Quantitative reverse transcription-polymerase chain reaction (QRT-PCR) was used to validate microarray data. A classification model was developed by support vector machine (SVM) algorithm to diagnose PTCs based on molecular signatures. We identified 79 differentially expressed genes (70 up-regulated and 9 down-regulated) according to the criteria. QRT-PCR for five genes (CDH3, NGEF, PROS1, TGFA, MET) was confirmatory of the microarray data. Hierarchical cluster analysis and a classification model by the SVM algorithm accurately differentiated PTCs from normal thyroid gland based on GEP. A disease classification model showed excellent accuracy in diagnosing PTCs, thus showing the possibility of molecular diagnosis in the future. This GEP could serve as baseline data for further investigation in the management of PTCs based on molecular signatures.
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