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Proteome analysis with the aid of the information oftians aiptome analysis for cancer diagnosis

借助 aiptome 分析信息进行蛋白质组分析用于癌症诊断

基本信息

批准号：
18500345
负责人：
KISHIMOTO Michimasa
金额：
$ 2.53万
依托单位：
Kyoto Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18500345/
关键词：
Proteome analysis Hepatic cancer PMF method Transcriptome analysis Neural net work 肝癌組織 αフェトプロテイン二次元電気泳動銀染色

项目摘要

From proteome analysis data, we succeeded to find the 4 gene expression which are strongly related with the cancer progression and recurrence ratio by using the neural net work ratio. Then we tried to study the expression level by proteome analysis (PMF method) with the aid of the information of the transcriptome analysis.However, the proteins of the gene expression could not be detected clearly. Especially in the case of AFP (Anti feto protein), which is the most strongly related with the cancer recurrence ration in out study and very common as a marker of the hepatic cancer, the protein spot location is almost same as that of albumin. The albumin content of cancer tissue is very high and the AFP protein spot is covered perfectly by the albumin spot.Then we tried to use the polyclonal antibody for the detection of the AFP protein. We make sure the existence of the AFP in the cancer tissue, but the content is very low and we consume several days and procedure for the preparation of polyclonal antibody is not easy.Next we developed the 2-DE analysis system for the improvement of the spot detection and the quantification. The analysis system is based on the Watershed transformantion, and Rolling ball method, and can be analyze the time course of the protein spot volume and spot image during the developing process. This software can analyze the overlapping protein spots, and detect the spot of small amount protein, because of the ability of the late time imega which has many spot overlapping. Then we can detect more than 1000 protein spots in a gel and identify the proteins of 150 spots. This developed technique are evaluated in the annual meeting of American Electro Society, and we got the 3rd Place Poster Award.However, our developed the technique could not be detect the protein spot of very small amount of protein, for example, AFP, signal proteins. We will continue the improvement of the proteome analysis proteins with the aid of antibody engineering.

从蛋白质组分析数据中，我们成功地利用神经网络比率找到了与肿瘤进展和复发率密切相关的4个基因的表达。然后，我们借助转录组分析的信息，尝试用蛋白质组分析(PMF方法)来研究基因的表达水平，但未能清楚地检测到基因表达的蛋白质。尤其是在本研究中与肿瘤复发率关系最为密切的、最常见的肝癌标志物AFP中，其蛋白质斑点位置与白蛋白几乎相同。肿瘤组织的白蛋白含量很高，白蛋白斑点完全覆盖了AFP蛋白斑点。然后，我们尝试使用多克隆抗体来检测AFP蛋白。我们确定了癌组织中AFP的存在，但含量很低，而且多克隆抗体的制备过程不容易，耗时数天。接下来，我们开发了双向凝胶电泳分析系统，以改进斑点检测和定量。该分析系统基于分水岭变换和滚球法，可以分析蛋白质斑点体积和斑点图像在开发过程中的时间过程。该软件可以对重叠的蛋白质斑点进行分析，并能检测出少量蛋白质的斑点，这是因为后期的iMega具有多个斑点重叠的能力。然后我们可以检测一个凝胶中的1000多个蛋白质点，并识别150个点的蛋白质。这项技术在美国电子学会年会上进行了评估，我们获得了第三名海报奖，但我们开发的技术不能检测出非常少量的蛋白质，如AFP，信号蛋白。我们将继续借助抗体工程来改进蛋白质组分析蛋白。