Organic and Biological Analytical Chemistry Group, MolSys Research Unit, University of Liège, B6c, Agora District, 4000 Liège, Belgium.
Pneumology and Allergology, GIGA Research Group, CHU of Liège, University of Liege, B35, Hospital District, Liege, Belgium.
J Chromatogr B Analyt Technol Biomed Life Sci. 2019 May 1;1114-1115:146-153. doi: 10.1016/j.jchromb.2019.01.029. Epub 2019 Jan 31.
Lung cancer is the deadliest cancer in developed countries. To reduce its mortality rate, it is important to enhance our capability to detect it at earlier stages by developing early diagnostic methods. In that context, the analysis of exhaled breath is an interesting approach because of the simplicity of the medical act and its non-invasiveness. Thermal desorption comprehensive two-dimensional gas chromatography time of flight mass spectrometry (TD-GC × GC-TOFMS) has been used to characterize and compare the volatile content of human breath of lung cancer patients and healthy volunteers. On the sampling side, the contaminations induced by the bags membrane and further environmental migration of VOCs during and after the sampling have also been investigated. Over a realistic period of 6 h, the concentration of contaminants inside the bag can increase from 2 to 3 folds based on simulated breath samples. On the data processing side, Fisher ratio (FR) and random forest (RF) approaches were applied and compared in regards to their ability to reduce the data dimensionality and to extract the significant information. Both approaches allow to efficiently smooth the background signal and extract significant features (27 for FR and 17 for RF). Principal component analysis (PCA) was used to evaluate the clustering capacity of the different models. For both approaches, a separation along PC-1 was obtained with a variance score around 35%. The combined model provides a partial separation with a PC-1 score of 52%. This proof-of-concept study further confirms the potential of breath analysis for cancer detection but also underlines the importance of quality control over the full analytical procedure, including the processing of the data.
在发达国家，肺癌是最致命的癌症。为了降低其死亡率，通过开发早期诊断方法来提高早期检测能力非常重要。在这种情况下，分析呼气是一种很有前途的方法，因为这种方法操作简单且无创。热解吸全二维气相色谱飞行时间质谱（TD-GC×GC-TOFMS）已被用于分析和比较肺癌患者和健康志愿者呼气中的挥发性成分。在采样方面，还研究了采样过程中以及采样后袋子膜引起的污染和 VOC 进一步向环境中的迁移。在实际的 6 小时内，根据模拟的呼气样本，袋子内的污染物浓度可以增加 2 到 3 倍。在数据处理方面，应用了 Fisher 比（FR）和随机森林（RF）方法，并比较了它们减少数据维度和提取重要信息的能力。这两种方法都可以有效地平滑背景信号并提取显著特征（FR 为 27 个，RF 为 17 个）。主成分分析（PCA）用于评估不同模型的聚类能力。对于这两种方法，都可以沿着 PC-1 得到分离，方差得分约为 35%。组合模型提供了一个部分分离，PC-1 得分为 52%。这项概念验证研究进一步证实了呼气分析在癌症检测中的潜力，但也强调了在整个分析过程中包括数据处理的质量控制的重要性。
