| Project/Area Number |
15209044
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Digestive surgery
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| Research Institution | Kinki University |
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Principal Investigator |
SHIOZAKI Hitoshi Kinki University, Sch.of Med., Prof., 医学部, 教授 (70144475)
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| Co-Investigator(Kenkyū-buntansha) |
OKUNO Kiyotaka Kinki University, Sch.of Med., Prof., 医学部, 教授 (30169239)
IMAMOTO Haruhiko Kinki University, Sch.of Med., Assoc.Prof., 医学部, 助教授 (80351609)
HIDA Jin-ichi Kinki University, Sch.of Med., Assist.Prof., 医学部, 講師 (20238306)
西村 訓弘 (株)ラボ, 主任研究員
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| Project Period (FY) |
2003 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥22,230,000 (Direct Cost: ¥17,100,000、Indirect Cost: ¥5,130,000)
Fiscal Year 2005: ¥5,720,000 (Direct Cost: ¥4,400,000、Indirect Cost: ¥1,320,000)
Fiscal Year 2004: ¥5,720,000 (Direct Cost: ¥4,400,000、Indirect Cost: ¥1,320,000)
Fiscal Year 2003: ¥10,790,000 (Direct Cost: ¥8,300,000、Indirect Cost: ¥2,490,000)
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| Keywords | cDNA array / gene expression analysis / neural network / colorectal cancer / liver metastasis / 食道癌 / 大腸癌肝転移 / ニューラルネットワーク解析 |
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| Research Abstract |
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
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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
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