| Project/Area Number |
18500345
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Biomedical engineering/Biological material science
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| Research Institution | Kyoto Institute of Technology |
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Principal Investigator |
KISHIMOTO Michimasa Kyoto Institute of Technology, Graduate Schoolof Seicnce and Technology, Professor (00144436)
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| Co-Investigator(Kenkyū-buntansha) |
SATO Seiji Akita University, Graduate School of Medicine, Associate Professor (00303834)
NOMURA Akinari Kyoto University, Graduate School of Medicine, Assistant Professor (20402901)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,960,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥360,000)
Fiscal Year 2007: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2006: ¥2,400,000 (Direct Cost: ¥2,400,000)
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| Keywords | 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 |
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| Research Abstract |
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.
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