Deja Stanislaw, Porebska Irena, Kowal Aneta, Zabek Adam, Barg Wojciech, Pawelczyk Konrad, Stanimirova Ivana, Daszykowski Michal, Korzeniewska Anna, Jankowska Renata, Mlynarz Piotr
Faculty of Chemistry, Opole University, Pl. Kopernika 11a, 45-040 Opole, Poland.
Department and Clinic of Pulmonology and Lung Cancers, Wroclaw Medical University, Grabiszynska 105, 53-439 Wroclaw, Poland.
J Pharm Biomed Anal. 2014 Nov;100:369-380. doi: 10.1016/j.jpba.2014.08.020. Epub 2014 Aug 21.
Chronic obstructive pulmonary disease (COPD) and lung cancer are widespread lung diseases. Cigarette smoking is a high risk factor for both the diseases. COPD may increase the risk of developing lung cancer. Thus, it is crucial to be able to distinguish between these two pathological states, especially considering the early stages of lung cancer. Novel diagnostic and monitoring tools are required to properly determine lung cancer progression because this information directly impacts the type of the treatment prescribed. In this study, serum samples collected from 22 COPD and 77 lung cancer (TNM stages I, II, III, and IV) patients were analyzed. Then, a collection of NMR metabolic fingerprints was modeled using discriminant orthogonal partial least squares regression (OPLS-DA) and further interpreted by univariate statistics. The constructed discriminant models helped to successfully distinguish between the metabolic fingerprints of COPD and lung cancer patients (AUC training=0.972, AUC test=0.993), COPD and early lung cancer patients (AUC training=1.000, AUC test=1.000), and COPD and advanced lung cancer patients (AUC training=0.983, AUC test=1.000). Decreased acetate, citrate, and methanol levels together with the increased N-acetylated glycoproteins, leucine, lysine, mannose, choline, and lipid (CH3-(CH2)n-) levels were observed in all lung cancer patients compared with the COPD group. The evaluation of lung cancer progression was also successful using OPLS-DA (AUC training=0.811, AUC test=0.904). Based on the results, the following metabolite biomarkers may prove useful in distinguishing lung cancer states: isoleucine, acetoacetate, and creatine as well as the two NMR signals of N-acetylated glycoproteins and glycerol.
慢性阻塞性肺疾病（COPD）和肺癌是常见的肺部疾病。吸烟是这两种疾病的高风险因素。COPD可能会增加患肺癌的风险。因此，能够区分这两种病理状态至关重要，尤其是考虑到肺癌的早期阶段。需要新的诊断和监测工具来正确确定肺癌的进展情况，因为这些信息直接影响所开治疗的类型。在本研究中，分析了从22名COPD患者和77名肺癌患者（TNM分期I、II、III和IV期）采集的血清样本。然后，使用判别正交偏最小二乘回归（OPLS-DA）对一组核磁共振代谢指纹图谱进行建模，并通过单变量统计进一步解释。构建的判别模型有助于成功区分COPD患者和肺癌患者的代谢指纹图谱（训练集AUC=0.972，测试集AUC=0.993）、COPD患者和早期肺癌患者（训练集AUC=1.000，测试集AUC=1.000）以及COPD患者和晚期肺癌患者（训练集AUC=0.983，测试集AUC=1.000）。与COPD组相比，所有肺癌患者均观察到乙酸盐、柠檬酸盐和甲醇水平降低，同时N-乙酰化糖蛋白、亮氨酸、赖氨酸、甘露糖、胆碱和脂质（CH3-(CH2)n-）水平升高。使用OPLS-DA对肺癌进展的评估也取得了成功（训练集AUC=0.811，测试集AUC=0.904）。基于这些结果，以下代谢物生物标志物可能有助于区分肺癌状态：异亮氨酸、乙酰乙酸和肌酸以及N-乙酰化糖蛋白和甘油的两个核磁共振信号。
