Proteomic patterns of preinvasive bronchial lesions
Proteomic patterns of preinvasive bronchial lesions
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DOI：
10.1164/rccm.200502-274oc
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发表时间：
2005-12-15
影响因子：
24.7
通讯作者：
Massion, PP
中科院分区：
文献类型：
--
作者：
Rahman, SMJ;Shyr, Y;Massion, PP
目的：蛋白质组学的方法是必要的，以进一步阐明参与肺肿瘤的发展的分子步骤。我们问，我们是否可以根据其蛋白质组profile.Experimental设计分类气道上皮的预浸润病变：我们获得了基质辅助激光解吸/电离飞行时间质谱的配置文件从10 μ m的新鲜冷冻组织样本：25个正常肺，29个正常支气管上皮，和20个预浸润和36个浸润性肺肿瘤组织样本53例。蛋白质组学图谱在分析前进行校准、分组和标准化。我们进行了类比较，类预测和监督层次聚类分析。我们测试了一组歧视性的功能，在以前发表的数据集分类这一独立的一组正常，preinvasive，和invasive lung tissues.Results：我们发现了一个特定的蛋白质组学概况，允许超过90%的正常，preinvasive，和侵入性肺组织的整体预测准确性。这些组织的蛋白质组图谱在疾病连续体中彼此不同。我们训练我们的预测模型在以前发表的数据集，并在一个新的盲测试集进行测试，以达到一个整体的74%的准确率在分类肿瘤从正常tissue.Conclusions：我们发现特定的模式的气道上皮细胞的蛋白质表达，准确地分类支气管和肺泡组织与正常组织学从浸润性支气管病变和浸润性肺癌。虽然需要进一步的研究来验证这种方法并确定肿瘤发展的生物标志物，但这是迈向肺癌肿瘤发生人类模型的新蛋白质组学表征的第一步。
Purpose: A proteornics approach is warranted to further elucidate the molecular steps involved in lung tumor development. We asked whether we could classify preinvasive lesions of airway epithelium according to their proteomic profile.Experimental Design: We obtained matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiles from 10-mu m sections of fresh-frozen tissue samples: 25 normal lung, 29 normal bronchial epithelium, and 20 preinvasive and 36 invasive lung tumor tissue samples from 53 patients. Proteomic profiles were calibrated, binned, and normalized before analysis. We performed class comparison, class prediction, and supervised hierarchic cluster analysis. We tested a set of discriminatory features obtained in a previously published dataset to classify this independent set of normal, preinvasive, and invasive lung tissues.Results: We found a specific proteomic profile that allows an overall predictive accuracy of over 90% of normal, preinvasive, and invasive lung tissues. The proteomic profiles of these tissues were distinct from each other within a disease continuum. We trained our prediction model in a previously published dataset and tested it in a new blinded test set to reach an overall 74% accuracy in classifying tumors from normal tissues.Conclusions: We found specific patterns of protein expression of the airway epithelium that accurately classify bronchial and alveolar tissue with normal histology from preinvasive bronchial lesions and from invasive lung cancer. Although further study is needed to validate this approach and to identify biomarkers of tumor development, this is a first step toward a new proteomic characterization of the human model of lung cancer tumorigenesis.