Biostatistics and Bioinformatics Facility, Brown Cancer Center, University of Louisville, Louisville, KY, United States of America.
School of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY, United States of America.
PLoS One. 2022 Nov 30;17(11):e0277431. doi: 10.1371/journal.pone.0277431. eCollection 2022.
Early detection of lung cancer is a crucial factor for increasing its survival rates among the detected patients. The presence of carbonyl volatile organic compounds (VOCs) in exhaled breath can play a vital role in early detection of lung cancer. Identifying these VOC markers in breath samples through innovative statistical and machine learning techniques is an important task in lung cancer research. Therefore, we proposed an experimental approach for generation of VOC molecular concentration data using unique silicon microreactor technology and further identification and characterization of key relevant VOCs important for lung cancer detection through statistical and machine learning algorithms. We reported several informative VOCs and tested their effectiveness in multi-group classification of patients. Our analytical results indicated that seven key VOCs, including C4H8O2, C13H22O, C11H22O, C2H4O2, C7H14O, C6H12O, and C5H8O, are sufficient to detect the lung cancer patients with higher mean classification accuracy (92%) and lower standard error (0.03) compared to other combinations. In other words, the molecular concentrations of these VOCs in exhaled breath samples were able to discriminate the patients with lung cancer (n = 156) from the healthy smoker and nonsmoker controls (n = 193) and patients with benign pulmonary nodules (n = 65). The quantification of carbonyl VOC profiles from breath samples and identification of crucial VOCs through our experimental approach paves the way forward for non-invasive lung cancer detection. Further, our experimental and analytical approach of VOC quantitative analysis in breath samples may be extended to other diseases, including COVID-19 detection.
早期发现肺癌是提高检出患者生存率的关键因素。呼气中羰基挥发性有机化合物（VOCs）的存在在肺癌的早期检测中起着至关重要的作用。通过创新的统计和机器学习技术在呼吸样本中识别这些 VOC 标志物是肺癌研究中的一项重要任务。因此，我们提出了一种使用独特的硅微反应器技术生成 VOC 分子浓度数据的实验方法，并通过统计和机器学习算法进一步识别和表征对肺癌检测至关重要的关键相关 VOC。我们报告了几个有信息的 VOC，并测试了它们在多组患者分类中的有效性。我们的分析结果表明，七种关键 VOC（包括 C4H8O2、C13H22O、C11H22O、C2H4O2、C7H14O、C6H12O 和 C5H8O）足以检测出肺癌患者，其平均分类准确率（92%）更高，标准误差（0.03）更低，与其他组合相比。换句话说，这些 VOC 在呼气样本中的分子浓度能够区分肺癌患者（n=156）与健康吸烟者和非吸烟者对照组（n=193）以及良性肺结节患者（n=65）。通过我们的实验方法从呼吸样本中定量羰基 VOC 谱并识别关键 VOC 为非侵入性肺癌检测铺平了道路。此外，我们在呼吸样本中进行 VOC 定量分析的实验和分析方法可能会扩展到其他疾病，包括 COVID-19 的检测。
