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New classification of digestive system cancers based on gene expression analysis.

基于基因表达分析的消化系统癌症新分类。

基本信息

批准号：
15209044
负责人：
SHIOZAKI Hitoshi
金额：
$ 14.23万
依托单位：
Kinki University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (A)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2005
项目状态：
已结题

项目摘要

Gene expression analysis with total RNA extracted from colon and rectal cancer was performed by using of cDNA filter arrays spotted with 1,300 genes relating to cancer and immunity. The number of samples was 51 including six samples overlapped. This study was aimed at the establishment of a new molecular-level prognosis method. We investigated following two points, that is, 1) establishment of prognosis method by constructing the liver metastasis predicting model based on gene expression profiling and 2) extraction of a group of genes relating to liver metastasis. We had chosen a neural network method to build a classifier and a genetic algorithm for its optimization, and then developed the system based on the method with our partner, MediBIC Co.Ltd. (Tokyo). The accuracy of our system was verified by using of the public data, published in Nature Medicine. As a result, an average rate of correct answer was 97.8%. These results suggest that our system and model were preferable for predi … More cting the possibility of metastasis. Then, we applied the system to the construction of liver metastasis forecasting model by using of clinical data. The accuracy of the system was sufficient level also in our case regarding the liver metastasis. Major molecules previously reported to relate to the liver metastases had not unfortunately found in the list of genes extracted based on our model. However, there is still a possibility to find a novel biomarker by investigating the extracted genes further. Because we recognized problems on the total RNA sample preparation, we have left it open to be investigated whether the biomarker candidates are included in the gene list. In this research, we were focusing on the classifier discriminating whether the liver metastasis occurs or not. We are aware however that liver metastasis should to be analyzed by separating into two groups, one is synchronous metastasis and the other is metachronous metastasis. We have started a series of study taking triple classifier approaches, namely, synchronous liver metastasis (5samples), metachronous liver metastasis (5samples) and liver metastasis free (5samples). We are checking the quality of all samples that will be used in the series of the study, at present. Less

使用点样了1,300个与癌症和免疫相关的基因的cDNA过滤阵列，对从结肠癌和直肠癌中提取的总RNA进行基因表达分析。样本数量为51，包括6个重叠样本。本研究旨在建立一种新的分子水平的预后方法。本研究主要从以下两个方面进行研究：1）通过构建基于基因表达谱的肝转移预测模型，建立预后方法; 2）提取一组与肝转移相关的基因。我们选择了神经网络方法来构建分类器，并选择遗传算法对其进行优化，然后与我们的合作伙伴MediBIC Co.Ltd.（东京）开发了基于该方法的系统。我们的系统的准确性通过使用发表在Nature Medicine上的公开数据进行验证。结果，平均正确率为97.8%。这些结果表明，我们的系统和模型是较好的预测， ...更多信息转移的可能性。然后，我们将该系统应用于临床数据的肝转移预测模型的构建。该系统的准确性是足够的水平，也在我们的情况下关于肝转移。不幸的是，在基于我们的模型提取的基因列表中没有发现先前报道的与肝转移相关的主要分子。然而，仍然有可能通过进一步研究提取的基因来发现新的生物标志物。因为我们认识到总RNA样品制备的问题，我们留下了开放的研究是否生物标志物候选者包括在基因列表中。在本研究中，我们的重点是区分是否发生肝转移的分类器。然而，我们意识到肝转移应分为两组进行分析，一组是同步转移，另一组是异时转移。我们采用三分类法，即同时性肝转移（5例）、异时性肝转移（5例）和无肝转移（5例），开始了一系列研究。目前，我们正在检查将用于该系列研究的所有样本的质量。少