A metabolomic approach to lung cancer
A metabolomic approach to lung cancer
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DOI：
10.1016/j.lungcan.2011.02.008
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发表时间：
2011-11-01
期刊：
影响因子：
5.3
通讯作者：
Yoshida, Masaru
中科院分区：
文献类型：
--
作者：
Hori, Suya;Nishiumi, Shin;Yoshida, Masaru
肺癌是世界上最常见的恶性肿瘤之一，但目前还没有发现能对肺癌进行早期诊断和预后预测的良好临床标志物，因此需要寻找新的临床标志物。在这项研究中，代谢组学分析肺癌患者进行了气相色谱质谱。使用来自29名健康志愿者和33名肺癌患者的血清样品，所述肺癌患者具有腺癌（n = 12）、鳞状细胞癌（n = 11）或小细胞癌（n = 10），范围为I期至IV期疾病，以及来自7名肺癌患者的肺组织样品，包括肿瘤组织及其周围正常组织。在血清中共检测到58种代谢物（57种单个代谢物），在肺组织中检测到71种代谢物。所有肺癌患者血清中58种代谢物中有23种的水平与健康志愿者相比发生了显著变化，71种代谢物中有48种的水平在肿瘤组织中与非肿瘤组织相比发生了显著变化。偏最小二乘判别分析，这是一种形式的多分类分析，使用血清样本数据进行，并确定在每个组织学亚型和疾病阶段具有特征性改变的代谢物。我们的研究结果表明，代谢物模式的变化是有用的评估肺癌的临床特征。我们的研究结果将有望导致新的诊断工具的建立。(C)2011爱思唯尔爱尔兰有限公司保留所有权利。
Lung cancer is one of the most common cancers in the world, but no good clinical markers that can be used to diagnose the disease at an early stage and predict its prognosis have been found. Therefore, the discovery of novel clinical markers is required. In this study, metabolomic analysis of lung cancer patients was performed using gas chromatography mass spectrometry. Serum samples from 29 healthy volunteers and 33 lung cancer patients with adenocarcinoma (n = 12), squamous cell carcinoma (n = 11), or small cell carcinoma (n = 10) ranging from stage I to stage IV disease and lung tissue samples from 7 lung cancer patients including the tumor tissue and its surrounding normal tissue were used. A total of 58 metabolites (57 individual metabolites) were detected in serum, and 71 metabolites were detected in the lung tissue. The levels of 23 of the 58 serum metabolites were significantly changed in all lung cancer patients compared with healthy volunteers, and the levels of 48 of the 71 metabolites were significantly changed in the tumor tissue compared with the non-tumor tissue. Partial least squares discriminant analysis, which is a form of multiple classification analysis, was performed using the serum sample data, and metabolites that had characteristic alterations in each histological subtype and disease stage were determined. Our results demonstrate that changes in metabolite pattern are useful for assessing the clinical characteristics of lung cancer. Our results will hopefully lead to the establishment of novel diagnostic tools. (C) 2011 Elsevier Ireland Ltd. All rights reserved.