2005 Fiscal Year Final Research Report Summary
System for predicting metastasis of cancer by statistical pattern recognition with microarrays.
| Project/Area Number |
16500113
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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 |
Perception information processing/Intelligent robotics
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| Research Institution | Yamaguchi University |
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Principal Investigator |
HAMAMOTO Yoshihiko Yamaguchi University, Faculty of Engineering, Professor, 工学部, 教授 (90198820)
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| Co-Investigator(Kenkyū-buntansha) |
HIRABAYASHI Akira Yamaguchi University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (50272688)
UCHIMURA Shunji Yamaguchi University, Faculty of Engineering, Assistant, 工学部, 助手 (50203550)
OKA Masaaki Yamaguchi University, Faculty of Medicine, Professor, 医学部, 教授 (70144946)
IIZUKA Norio Yamaguchi University, Faculty of Medicine, Assistant, 医学部, 助手 (80332807)
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| Project Period (FY) |
2004 – 2005
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| Keywords | Microarray / Transcriptome Analysis / Pattern Recognition / Prognosis / Gene |
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| Research Abstract |
In 2004, we examined gene expression profiles of esophageal squamous carcinomas (ESCCs) with respect to degree of invasive depth and lymph node involvement. We used high-density oligonucleotide microarrays (Affymetrix) to examine expression of 22115 genes in 54 ESCCs and 11 non-cancerous esophageal tissues. Our results show that ESCCs with distinct invasive depths express different genes linked to lymph node metastasis, suggesting that the degree of invasive depth must be considered when predicting lymph node metastasis of ESCCs from gene expression profiles.
In 2005, we studied a comprehensive review for developing a predictive system with microarrays. In human genome analysis, we have few samples, compared with the genes to be examined. The small number of samples causes the practical difficulties in a predictive system. On the base of the many results obtained with hepatocellular carcinoma, we studied our strategy to overcome these difficulties. Most importantly, gene selection should be addressed, rather than classifier design. In particular, the way to cope with the variability due to the small sample size is critical. This leads to the robust gene selection. Moreover, we point out that the approach of using supervised learning is right when developing a predictive system.
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Research Products
(11 results)
