| Project/Area Number |
15591467
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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 |
Thoracic surgery
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| Research Institution | Chiba University |
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Principal Investigator |
IYODA Akira Chiba University Hospital, Assistant Professor, 医学部附属病院, 助手 (10302548)
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| Co-Investigator(Kenkyū-buntansha) |
FUJISAWA Takehiko Chiba University, Graduate School of Medicine, Professor, 大学院・医学研究院, 教授 (80110328)
NIMURA Yoshinori Chiba University, Graduate School of Medicine, Assistant Professor, 大学院・医学研究院, 助手 (50345021)
SEKI Naohiko Chiba University, Graduate School of Medicine, Associate Professor, 大学院・医学研究院, 助教授 (50345013)
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| Project Period (FY) |
2003 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥3,300,000 (Direct Cost: ¥3,300,000)
Fiscal Year 2004: ¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2003: ¥1,600,000 (Direct Cost: ¥1,600,000)
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| Keywords | Lung cancer / Microarray / Prognosis |
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| Research Abstract |
In this study, we investigate gene-expression profiles of 64 primary lung cancers by using an oligonucleotide microarray containing 10,000 genes and identify new molecular targets associated with clinicopathological factors. From July 2002 through December 2003, 64 pairs of lung cancer tissues and corresponding adjacent non-cancerous tissues were obtained with informed consent from patients who underwent surgical operation at Chiba University Hospital, Japan. Samples were collected using informed consent according to institutional guidelines and the study protocol approved by our institutional Research Ethics Board. We have analyzed gene expression profiles using oligonucleotide microarray, and hierarchical clustering analysis was performed with expression levels of the selected genes and statistical significance was calculated. Eighty-one genes showed differentially expressed in adenocarcinoma, 304 genes in squamous cell carcinoma. Thirty-four genes showed a distinct expression pattern exclusively in cancer tissues with lymph-node metastasis (P<0.01). Based on the expression analysis of 10,000 genes and clustering algorithm successfully distinguished histological types and clinical status such as lymph node metastasis. Isolated genes provide valuable information for eventually identifying predictive markers and novel therapeutic target molecules for lung cancer.
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