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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，我们利用神经网络比率成功找到了与癌症进展和复发率密切相关的4个基因表达。然后我们试图借助转录组分析的信息，通过蛋白质组分析（PMF法）来研究基因的表达水平，但无法清楚地检测到基因表达的蛋白质。尤其是AFP（抗胎蛋白），它是目前研究中与癌症复发率相关性最强、作为肝癌标志物很常见的一种，其蛋白点位置几乎与白蛋白相同。癌组织中白蛋白含量很高，AFP蛋白点被白蛋白点完美覆盖。于是我们尝试用多克隆抗体来检测AFP蛋白。我们确定了癌组织中存在AFP，但含量很低，而且需要花费数天的时间，而且多克隆抗体的制备过程并不容易。接下来我们开发了2-DE分析系统，以改进点检测和定量。该分析系统基于Watershed变换和滚球法，可以分析显影过程中蛋白质斑点体积和斑点图像的时间过程。该软件可以分析重叠的蛋白质斑点，并检测少量蛋白质的斑点，因为后期imega的能力有很多斑点重叠。然后我们可以检测凝胶中的 1000 多个蛋白质点，并鉴定 150 个点的蛋白质。我们开发的技术在美国电子学会年会上进行了评估，我们获得了三等奖海报奖。但是，我们开发的技术无法检测极少量蛋白质的蛋白点，例如AFP、信号蛋白。我们将借助抗体工程继续改进蛋白质组分析蛋白。