Prediction of lymph node metastasis by analysis of gene expression profiles in non-small cell lung cancer.
Prediction of lymph node metastasis by analysis of gene expression profiles in non-small cell lung cancer.
复制标题
DOI：
10.1016/j.jss.2004.06.002
复制
发表时间：
2004-11
期刊：
影响因子：
--
通讯作者：
M. Takada;M. Tada;Eiji Tamoto;Akiko Kawakami;Katsuhiko Murakawa;G. Shindoh;Ken-ichi Teramoto;A. Matsunaga;K. Komuro;M. Kanai;Y. Fujiwara;K. Shirata;Norihiro Nishimura;M. Miyamoto;S. Okushiba;S. Kondo;J. Hamada;H. Katoh;T. Yoshiki;T. Moriuchi
中科院分区：
文献类型：
--
作者：
M. Takada;M. Tada;Eiji Tamoto;Akiko Kawakami;Katsuhiko Murakawa;G. Shindoh;Ken-ichi Teramoto;A. Matsunaga;K. Komuro;M. Kanai;Y. Fujiwara;K. Shirata;Norihiro Nishimura;M. Miyamoto;S. Okushiba;S. Kondo;J. Hamada;H. Katoh;T. Yoshiki;T. Moriuchi
非小细胞肺癌（NSCLC）是世界上主要的死亡原因之一。淋巴结转移不仅是评估NSCLC范围和转移潜力的重要因素，而且也是预测患者预后的重要因素。术前预测淋巴结转移可能会极大地促进选择适当的手术和药物的选择在patientswith NSCLC.METHODS和PROTENTS使用cDNA阵列，我们分析了1,289个基因的表达谱在92例NSCLC癌组织（37例鳞状细胞癌和55例腺癌）。我们根据各种病理因素，如淋巴结转移和pT分期，将患者分为两组（类）。对于每一对类，我们搜索基因的最佳组合，以使用顺序向前选择算法从类之间的表达显示出显著差异的基因集开始对病例进行分类。我们使用留一法交叉验证的k-最近邻分类器顺序选择基因。使用优化的基因组，有可能对淋巴结转移的患者进行分层（PN-阶段）和PT-阶段，分别为，百分百（23个基因）和100%（55个基因）与鳞状细胞癌病例和94%的（43个基因）和92%结论：我们得出结论，使用特征选择的表达谱提供了一种强有力的分层手段在NSCLC患者的治疗选择中，特别是对于诸如淋巴结转移的因素（其放射学诊断目前是不完整的）的治疗选择（个性化）。
OBJECTIVENon-small cell lung carcinoma (NSCLC) is one of the leading causes of death in the world. Lymph node metastasis is not only an important factor in estimating the extent and the metastatic potential of an NSCLC but also in prognosticating the patient outcome. Preoperative prediction of lymph node metastasis might greatly facilitate the choice of appropriate surgical and medical options in patients with NSCLC.METHODS AND RESULTSUsing a cDNA array, we analyzed the expression profiles of 1,289 genes in 92 cancer tissues of NSCLC (37 squamous cell carcinomas and 55 adenocarcinomas). We divided the patients into two groups (classes) for each of various pathological factors, such as lymph node metastasis and pT-stage. For each pair of classes, we searched for an optimal combination of genes to classify the cases using a sequential forward selection algorithm starting from a gene set that showed significant difference in expression between the classes. We used the leave-one-out error cross-validation on a k-nearest neighbor classifier to sequentially choose the gene. Using the optimized set of genes, it was possible to stratify the patients for lymph node metastasis (pN-stage) and pT-stage at, respectively, 100% (23 genes) and 100% (55 genes) for cases with squamous cell carcinomas and 94% (43 genes) and 92% (35 genes) for those with adenocarcinomas.CONCLUSIONWe conclude that expression profiling using feature selection provides a powerful means of stratification (personalization) of NSCLC patients and choice in treatment options, particularly for factors such as lymph node metastasis whose radiological diagnosis is presently incomplete.