He Ping, Varticovski Lyuba, Bowman Elise D, Fukuoka Junya, Welsh Judith A, Miura Koh, Jen Jin, Gabrielson Edward, Brambilla Elisabeth, Travis William D, Harris Curtis C
Laboratory of Human Carcinogenesis, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892-4255, USA.
Hum Pathol. 2004 Oct;35(10):1196-209. doi: 10.1016/j.humpath.2004.06.014.
Pulmonary neuroendocrine tumors vary dramatically in their malignant behavior. Their classification, based on histological examination, is often difficult. In search of molecular and prognostic markers for these tumors, we used cDNA microarray analysis of human transcripts against reference RNA from a well-characterized immortalized bronchial epithelial cell line, BEAS-2B. Tumor cells were isolated by laser-capture microdissection from primary tumors of 17 typical carcinoids, small cell lung cancers, and large cell neuroendocrine carcinomas. An unsupervised, hierarchical clustering algorithm resulted in a precise classification of each tumor subtype according to the proposed histological classification. Selection of genes, using supervised analysis, resulted in the identification of 198 statistically significant genes (P <.004) that also accurately discriminated between 3 predefined tumor subtypes. Two-by-two comparisons of these genes identified classifier genes that distinguished each tumor subtype from the others. Changes in expression of selected differentially expressed genes for each tumor subtype were internally validated by real-time reverse-transcription polymerase chain reaction. Expression of 2 potential classifier gene products, carboxypeptidase E (CPE) and gamma-glutamyl hydrolase (GGH), was validated by immunohistochemistry and cross-validated on additional archival samples of pulmonary neuroendocrine tumors. Kaplan-Meier survival analysis revealed that immunostaining for CPE was a statistically significant predictor of good prognosis, whereas GGH expression correlated with poor prognosis. Thus, cDNA microarray analysis led to the identification of 2 novel biomarkers that should facilitate molecular diagnosis and further study of pulmonary neuroendocrine tumors.
肺神经内分泌肿瘤的恶性行为差异极大。基于组织学检查对其进行分类往往很困难。为了寻找这些肿瘤的分子和预后标志物,我们使用针对来自特征明确的永生化支气管上皮细胞系BEAS-2B的参考RNA的人类转录本进行cDNA微阵列分析。通过激光捕获显微切割从17例典型类癌、小细胞肺癌和大细胞神经内分泌癌的原发性肿瘤中分离肿瘤细胞。一种无监督的层次聚类算法根据提议的组织学分类对每种肿瘤亚型进行了精确分类。使用监督分析选择基因,结果鉴定出198个具有统计学意义的基因(P <.004),这些基因也能准确区分3种预定义的肿瘤亚型。对这些基因进行两两比较,确定了区分每种肿瘤亚型与其他亚型的分类基因。通过实时逆转录聚合酶链反应对每种肿瘤亚型中选定的差异表达基因的表达变化进行了内部验证。通过免疫组织化学验证了2种潜在分类基因产物羧肽酶E(CPE)和γ-谷氨酰水解酶(GGH)的表达,并在肺神经内分泌肿瘤的其他存档样本上进行了交叉验证。Kaplan-Meier生存分析显示,CPE免疫染色是良好预后的统计学显著预测指标,而GGH表达与不良预后相关。因此,cDNA微阵列分析导致鉴定出2种新型生物标志物,这应有助于肺神经内分泌肿瘤的分子诊断和进一步研究。