代谢组学方法分析肺癌患者血清和尿液小分子代谢产物的初步研究

背景与目的肺癌是当今世界各国最常见的恶性肿瘤之一。目前尚没有寻找到理想的用于肺癌诊断的肿瘤标志物,因而尝试用各种新方法来探索新的生物学标志物已成为肺癌研究的热点。本研究采用代谢组学技术对肺癌患者和其它肺部疾病患者血清及尿液中的小分子代谢物质进行分析,以寻求潜在的肺癌肿瘤标志物。方法运用气相色谱/质谱法(gas chromatography/mass spectrometry,GC/MS)对19例肺癌与15例其它肺部疾病患者的血清及尿液样本进行代谢组学分析,采用正交偏最小二乘判别分析法(orthogonal to partial least squares discriminant analysis,OPLS-DA)进行建模,运用两样本的t检验寻找两组间差异性代谢产物。结果检测到血清中代谢产物共57种,尿液中代谢产物共38种,多变量统计结果显示肺癌患者与其它肺部疾病患者的代谢谱有明显差异,根据t检验结果寻找到血清相关的差异代谢产物13种,尿液相关的差异代谢产物7种。结论利用代谢组学方法能区分肺癌与其它肺部疾病患者,其结果在分子水平辅助肺癌的诊断、未来作为新技术应用于肺癌的诊断有一定的前景。

Preliminary Results of Metabolite in Serum and Urine of Lung Cancer Patients detected by Metabolomics

Background and objective Lung cancer is one of the most common cancers worldwide.Thus tar,good tumor markers for diagnosing this disease have not been found.Therefore,the discovery of novel biomarkers through the application of new methods has become a hotspot in lung cancer research.The aim of this study is to analyze low-molecular-weight metabolites in the serum and urine samples of lung cancer patients and patients with other lung diseases through metabolomics and to explore potential tumor markers further.Methods Both serum and urine samples from 19 lung cancer patients and 15 patients with other lung diseases were subjected to metabolomic analysis using gas chromatography mass spectrometry. Orthogonal to partial least squares discriminant analysis was performed for modeling.Two sample t-test was used to identify differences in metabolite concentrations.Results A total of 57 metabolites were found in the serum,and 38 metabolites were found in the urine.Multivariate statistical analysis yielded a significant distinction in the metabolic profiles between lung cancer patients and patients with other lung diseases.The t-test results indicated a total of 13 metabolites in the serum and 7 metabolites in the urine with statistically significant differences.Conclusion Metabolomics is useful in discriminating between lung cancer and other lung diseases.As a novel approach,it has potential in the diagnosis of lung cancer at molecular level.