Experimental trial for diagnosis of pancreatic ductal carcinoma based on gene expression profiles of pancreatic ductal cells
Experimental trial for diagnosis of pancreatic ductal carcinoma based on gene expression profiles of pancreatic ductal cells
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DOI：
10.1111/j.1349-7006.2005.00064.x
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发表时间：
2005-07-01
期刊：
影响因子：
5.7
通讯作者：
Mano, H
中科院分区：
文献类型：
--
作者：
Ishikawa, M;Yoshida, K;Mano, H
胰腺导管癌（PDC）仍然是最难治疗的人类恶性肿瘤之一，主要是因为缺乏敏感的检测方法。虽然基因表达谱的DNA微阵列分析是一个很有前途的工具，这种检测系统的发展，一个简单的比较胰腺组织可能会产生误导性的数据，反映只有细胞组成的差异。为了直接比较PDC细胞与正常胰腺导管细胞，我们从25名正常胰腺和24名PDC患者的胰液中纯化MUC 1阳性上皮细胞。这49份标本的基因表达谱用含有> 44000个探针组的DNA微阵列测定。Welch的方差分析和基于效应大小的选择的表达数据的应用导致在识别21个探针组对应于20个基因，其表达与临床诊断高度相关。此外，对应分析和3-D投影与这些探针集导致分离的胰腺导管细胞的转录组到不同的，但重叠的空间对应的两个临床类别。为了建立一个精确的基于转录组的PDC诊断系统，我们将监督类预测算法应用于我们的大数据集。仅用5个预测基因的表达谱，加权投票法对样本类别进行了诊断，准确率为81.6%。因此，纯化的胰腺导管细胞的微阵列分析提供了一个敏感的方法检测PDC的发展的基础。
Pancreatic ductal carcinoma (PDC) remains one of the most intractable human malignancies, mainly because of the lack of sensitive detection methods. Although gene expression profiling by DNA microarray analysis is a promising tool for the development of such detection systems, a simple comparison of pancreatic tissues may yield misleading data that reflect only differences in cellular composition. To directly compare PDC cells with normal pancreatic ductal cells, we purified MUC1-positive epithelial cells from the pancreatic juices of 25 individuals with a normal pancreas and 24 patients with PDC. The gene expression profiles of these 49 specimens were determined with DNA microarrays containing > 44000 probe sets. Application of both Welch's analysis of variance and effect size-based selection to the expression data resulted in the identification of 21 probe sets corresponding to 20 genes whose expression was highly associated with clinical diagnosis. Furthermore, correspondence analysis and 3-D projection with these probe sets resulted in separation of the transcriptomes of pancreatic ductal cells into distinct but overlapping spaces corresponding to the two clinical classes. To establish an accurate transcriptome-based diagnosis system for PDC, we applied supervised class prediction algorithms to our large data set. With the expression profiles of only five predictor genes, the weighted vote method diagnosed the class of samples with an accuracy of 81.6%. Microarray analysis with purified pancreatic ductal cells has thus provided a basis for the development of a sensitive method for the detection of PDC.