Biade S, Marinucci M, Schick J, Roberts D, Workman G, Sage E H, O'Dwyer P J, Livolsi V A, Johnson S W
Department of Pharmacology, University of Pennsylvania Cancer Center, BRB II/III- Room 1020, 421 Curie Building, Philadelphia, PA, USA.
Br J Cancer. 2006 Oct 23;95(8):1092-100. doi: 10.1038/sj.bjc.6603346. Epub 2006 Sep 12.
There is currently a lack of reliable diagnostic and prognostic markers for ovarian cancer. We established gene expression profiles for 120 human ovarian tumours to identify determinants of histologic subtype, grade and degree of malignancy. Unsupervised cluster analysis of the most variable set of expression data resulted in three major tumour groups. One consisted predominantly of benign tumours, one contained mostly malignant tumours, and one was comprised of a mixture of borderline and malignant tumours. Using two supervised approaches, we identified a set of genes that distinguished the benign, borderline and malignant phenotypes. These algorithms were unable to establish profiles for histologic subtype or grade. To validate these findings, the expression of 21 candidate genes selected from these analyses was measured by quantitative RT-PCR using an independent set of tumour samples. Hierarchical clustering of these data resulted in two major groups, one benign and one malignant, with the borderline tumours interspersed between the two groups. These results indicate that borderline ovarian tumours may be classified as either benign or malignant, and that this classifier could be useful for predicting the clinical course of borderline tumours. Immunohistochemical analysis also demonstrated increased expression of CD24 antigen in malignant versus benign tumour tissue. The data that we have generated will contribute to a growing body of expression data that more accurately define the biologic and clinical characteristics of ovarian cancers.
目前卵巢癌缺乏可靠的诊断和预后标志物。我们建立了120例人类卵巢肿瘤的基因表达谱，以确定组织学亚型、分级和恶性程度的决定因素。对最具变异性的表达数据集进行无监督聚类分析，产生了三个主要肿瘤组。一组主要由良性肿瘤组成，一组主要包含恶性肿瘤，另一组由交界性和恶性肿瘤的混合物组成。使用两种监督方法，我们鉴定出一组区分良性、交界性和恶性表型的基因。这些算法无法建立组织学亚型或分级的图谱。为了验证这些发现，使用一组独立的肿瘤样本，通过定量RT-PCR测量从这些分析中选择的21个候选基因的表达。这些数据的层次聚类产生了两个主要组，一组良性，一组恶性，交界性肿瘤散布在两组之间。这些结果表明，交界性卵巢肿瘤可分类为良性或恶性，并且这种分类器可能有助于预测交界性肿瘤的临床进程。免疫组织化学分析还显示，与良性肿瘤组织相比，恶性肿瘤组织中CD24抗原的表达增加。我们所生成的数据将有助于增加越来越多的表达数据，从而更准确地定义卵巢癌的生物学和临床特征。
