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The strategy based on gene expression profiling using microarray for tailor-made treatment in patients with lung cancer

基于基因表达谱的微阵列策略为肺癌患者提供量身定制的治疗

基本信息

批准号：
17591458
负责人：
IYODA Akira
金额：
$ 2.24万
依托单位：
Chiba University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2005
资助国家：
日本
起止时间：
2005 至 2006
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-17591458/
关键词：
Lung cancer Microarray Prognosis tailor-made treatment

项目摘要

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.

目的淋巴结转移和肿瘤复发是影响肺癌预后的主要因素，但其分子机制尚不清楚。本研究的目的是寻找肺癌组织和正常肺组织之间差异表达的基因，并对100例肺癌的基因表达谱进行研究，筛选出一组反映肺癌临床特征的基因表达谱。实验设计采用寡核苷酸微阵列技术获得基因表达谱，通过对所选基因表达水平的统计意义进行预测集的构建。结果在候选基因的搜索中，530个基因在腺癌中有差异表达，519个基因在鳞癌中有差异表达。有94个基因仅在有淋巴结转移的癌组织中有明显的表达模式，60个基因与肿瘤复发有关。使用最合适的一组基因，就有可能预测肺癌患者的临床未来。在独立病例中，预测腺癌的准确率为90.9%，预测鳞癌的准确率为91.5%，预测淋巴结转移的准确率为71.4%，预测肿瘤复发的准确率为84.6%。结论基因表达分析和统计学分析相结合成功地区分了组织病理学类型和淋巴结转移、复发等临床特征。本研究结果表明，根据病理诊断和分子分类相结合的方法对肺癌进行亚型是可能的。