Identification of a predictive gene expression signature of cervical lymph node metastasis in oral squamous cell carcinoma
Identification of a predictive gene expression signature of cervical lymph node metastasis in oral squamous cell carcinoma
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DOI：
10.1111/j.1349-7006.2007.00454.x
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发表时间：
2007-05-01
期刊：
影响因子：
5.7
通讯作者：
Miki, Yoshio
中科院分区：
文献类型：
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作者：
Nguyen, Su Tien;Hasegawa, Shogo;Miki, Yoshio
准确评估口腔癌颈淋巴结转移状况不仅有助于预测患者的预后，而且有助于外科医生进行适当的治疗。本研究利用基因芯片技术，对口腔鳞癌颈淋巴结转移与未转移原发灶的基因表达谱差异进行了研究，以期发现新的生物标志物，为口腔癌的诊断和治疗服务。为了设计这个实验，我们准备了两组：学习案例组30名患者和测试案例组13名患者。使用激光捕获显微切割进行所有组织样品以产生癌细胞，并且从纯化的癌细胞中分离RNA。为了鉴定预测性基因表达特征，分析了学习病例（n = 30）中具有和不具有转移的两组之间的不同基因表达，并选择了差异表达的85个基因。随后，为了构建更准确的预测模型，我们进一步使用AdaBoost算法从85个基因中选择具有高预测能力的基因。选择八个候选基因DCTD、IL-15、THBD、GSDML、SH 3GL 3、PTHLH、RP 5 - 1022 P6和C9 orf 46以实现最小错误率。进行定量逆转录-聚合酶链反应以验证所选基因。从这些统计方法，预测模型，包括8个基因的构建和该模型进行了评估，通过使用测试用例组。13例中12例（92.3%）预测正确。
An accurate assessment of the cervical lymph node metastasis status in oral cavity cancer not only helps predict the prognosis of patients, but also helps surgeons to perform the appropriate treatment. We investigated the utilization of microarray technology focusing on the differences in gene expression profiles between primary tumors of oral squamous cell carcinoma that had metastasized to cervical lymph nodes and those that had not metastasized in the hope of finding new biomarkers to serve for diagnosis and treatment of oral cavity cancer. To design this experiment, we prepared two groups: the learning case group with 30 patients and the test case group with 13 patients. All tissue samples were performed using laser captured microdissection to yield cancer cells, and RNA was isolated from purified cancer cells. To identify a predictive gene expression signature, the different gene expressions between the two groups with and without metastasis in the learning case (n = 30) were analyzed, and the 85 genes expressed differentially were selected. Subsequently, to construct a more accurate prediction model, we further selected the genes with a high power for prediction from the 85 genes using the AdaBoost algorithm. The eight candidate genes, DCTD, IL-15, THBD, GSDML, SH3GL3, PTHLH, RP5-1022P6 and C9orf46, were selected to achieve the minimum error rate. Quantitative reverse transcription-polymerase chain reaction was carried out to validate the selected genes. From these statistical methods, the prediction model was constructed including the eight genes and this model was evaluated by using the test case group. The results in 12 of 13 cases (similar to 92.3%) were predicted correctly.