| Project/Area Number |
17591458
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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)
SEKI Naohiko Chiba University, Graduate School of Medicine, Associate Professor, 大学院医学研究院, 助教授 (50345013)
SHIBUYA Kiyoshi Chiba University, Hospital, Associate Professor, 医学部附属病院, 講師 (20302565)
HIROSHIMA Kenzo Chiba University, Graduate School of Medicine, Associate Professor, 大学院医学研究院, 助教授 (80218833)
MOTOHASHI Shinichiro Chiba University, Graduate School of Medicine, Associate Professor, 大学院医学研究院, 産学官連携研究員 (60345022)
飯笹 俊彦 千葉大学, 大学院・医学研究院, 助教授 (10272303)
守屋 康充 千葉大学, 医学部附属病院, 医員 (90375692)
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| Project Period (FY) |
2005 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2006: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 2005: ¥2,200,000 (Direct Cost: ¥2,200,000)
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| Keywords | Lung cancer / Microarray / Prognosis / tailor-made treatment |
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| Research Abstract |
Purpose Lymph node metastasis and tumor recurrence are major factors associated with poor prognosis in the cancer, but little is known of their molecular mechanisms. The aim of this study was to identify genes differentially expressed between normal and cancerous lung tissues, and to investigate the gene-expression profiles of 100 primary lung cancers to select a set of gene predictors for the clinical features of lung cancer.
Experimental Design Gene expression profiles were obtained using an oligonucleotide microarray, and the construction of predictor sets was performed by evaluating the statistical significance of the expression levels of selected genes.
Results In the search for candidate genes, 530 genes showed differential expression in adenocarcinoma and 519 genes in squamous cell carcinoma. Ninety-four genes showed a distinct expression pattern exclusively in cancer tissues with lymph-node metastasis and 60 genes showed involvement with tumor recurrence. Using the most suitable set of genes, it was possible to predict the clinical futures of patients with lung cancer. The prediction scoring system yielded 90.9% accuracy for forecasting adenocarcinoma and 91.5% accuracy for squamous cell carcinoma, 71.4% accuracy for forecasting lymph node metastasis and 84.6% accuracy for tumor recurrence in independent cases.
Conclusions The gene expression analysis and the combination of statistical analysis successfully distinguished histopathological types and clinical features such as lymph node metastasis and recurrence. The findings of this study show the possibility of subgrouping lung cancer based on the combination of pathological diagnosis and molecular classification.
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