Brabender Jan, Marjoram Paul, Salonga Dennis, Metzger Ralf, Schneider Paul M, Park Ji Min, Schneider Sylke, Hölscher Arnulf H, Yin Jing, Meltzer Stephen J, Danenberg Kathleen D, Danenberg Peter V, Lord Reginald V
Department of Visceral and Vascular Surgery, University of Cologne, 50931 Germany.
Oncogene. 2004 Jun 10;23(27):4780-8. doi: 10.1038/sj.onc.1207663.
In order to identify genes or combination of genes that have the power to discriminate between premalignant Barrett's esophagus and Barrett's associated adenocarcinoma, we analysed a panel of 23 genes using quantitative real-time RT-PCR (qRT-PCR, Taqman and bioinformatic tools. The genes chosen were either known to be associated with Barrett's carcinogenesis or were filtered from a previous cDNA microarray study on Barrett's adenocarcinoma. A total of 98 tissues, obtained from 19 patients with Barrett's esophagus (BE group) and 20 patients with Barrett's associated esophageal adenocarcinoma (EA group), were studied. Triplicate analysis for the full 23 gene of interest panel, and analysis of an internal control gene, was performed for all samples, for a total of more than 9016 single PCR reactions. We found distinct classes of gene expression patterns in the different types of tissues. The most informative genes clustered in six different classes and had significantly different expression levels in Barrett's esophagus tissues compared to adenocarcinoma tissues. Linear discriminant analysis (LDA) distinguished four genetically different groups. The normal squamous esophagus tissues from patients with BE or EA were not distinguishable from one another, but Barrett's esophagus tissues could be distinguished from adenocarcinoma tissues. Using the most informative genes, obtained from a logistic regression analysis, we were able to completely distinguish between benign Barrett's and Barrett's adenocarcinomas. This study provides the first non-array parallel mRNA quantitation analysis of a panel of genes in the Barrett's esophagus model of multistage carcinogenesis. Our results suggest that mRNA expression quantitation of a panel of genes can discriminate between premalignant and malignant Barrett's disease. Logistic regression and LDAs can be used to further identify, from the complete panel, gene subsets with the power to make these diagnostic distinctions. Expression analysis of a limited number of highly selected genes may have clinical usefulness for the treatment of patients with this disease.
为了识别出能够区分癌前巴雷特食管和巴雷特相关腺癌的基因或基因组合，我们使用定量实时逆转录聚合酶链反应（qRT-PCR，Taqman）和生物信息学工具分析了一组23个基因。所选基因要么已知与巴雷特癌变相关，要么是从先前关于巴雷特腺癌的cDNA微阵列研究中筛选出来的。共研究了98个组织，这些组织取自19例巴雷特食管患者（BE组）和20例巴雷特相关食管腺癌患者（EA组）。对所有样本进行了全部23个感兴趣基因的一式三份分析以及一个内参基因的分析，总共进行了超过9016次单PCR反应。我们在不同类型的组织中发现了不同类别的基因表达模式。最具信息量的基因聚为六个不同类别，与腺癌组织相比，在巴雷特食管组织中的表达水平有显著差异。线性判别分析（LDA）区分出四个基因不同的组。BE或EA患者的正常鳞状食管组织彼此无法区分，但巴雷特食管组织可与腺癌组织区分开来。使用从逻辑回归分析中获得的最具信息量的基因，我们能够完全区分良性巴雷特食管和巴雷特腺癌。本研究提供了在巴雷特食管多阶段癌变模型中对一组基因进行的首次非阵列平行mRNA定量分析。我们的结果表明，一组基因的mRNA表达定量可区分癌前和恶性巴雷特病。逻辑回归和LDA可用于从完整的基因组中进一步识别出具有进行这些诊断区分能力的基因子集。对有限数量的高度选择基因进行表达分析可能对该疾病患者的治疗具有临床实用性。
