College of Biology Pharmacy and Food Engineering, Shangluo University, Shangluo 726000, People's Republic of China.
Research Center of Analytical Instrumentation, Key Laboratory of Synthetic and Natural Functional Molecule Chemistry of Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an 710069, People's Republic of China.
J Breath Res. 2024 Aug 5;18(4). doi: 10.1088/1752-7163/ad6474.
Lung cancer subtyping, particularly differentiating adenocarcinoma (ADC) from squamous cell carcinoma (SCC), is paramount for clinicians to develop effective treatment strategies. In this study, we aimed: (i) to discover volatile organic compound (VOC) biomarkers for precise diagnosis of ADC and SCC, (ii) to investigated the impact of risk factors on ADC and SCC prediction, and (iii) to explore the metabolic pathways of VOC biomarkers. Exhaled breath samples from patients with ADC (= 149) and SCC (= 94) were analyzed by gas chromatography-mass spectrometry. Both multivariate and univariate statistical analysis method were employed to identify VOC biomarkers. Support vector machine (SVM) prediction models were developed and validated based on these VOC biomarkers. The impact of risk factors on ADC and SCC prediction was investigated. A panel of 13 VOCs was found to differ significantly between ADC and SCC. Utilizing the SVM algorithm, the VOC biomarkers achieved a specificity of 90.48%, a sensitivity of 83.50%, and an area under the curve (AUC) value of 0.958 on the training set. On the validation set, these VOC biomarkers attained a predictive power of 85.71% for sensitivity and 73.08% for specificity, along with an AUC value of 0.875. Clinical risk factors exhibit certain predictive power on ADC and SCC prediction. Integrating these risk factors into the prediction model based on VOC biomarkers can enhance its predictive accuracy. This work indicates that exhaled breath holds the potential to precisely detect ADCs and SCCs. Considering clinical risk factors is essential when differentiating between these two subtypes.
肺癌亚型分类，特别是区分腺癌（ADC）和鳞状细胞癌（SCC），对临床医生制定有效的治疗策略至关重要。本研究旨在：（i）发现用于精确诊断 ADC 和 SCC 的挥发性有机化合物（VOC）生物标志物，（ii）研究风险因素对 ADC 和 SCC 预测的影响，以及（iii）探索 VOC 生物标志物的代谢途径。通过气相色谱-质谱法分析来自 ADC 患者（=149）和 SCC 患者（=94）的呼气样本。采用多元和单变量统计分析方法来识别 VOC 生物标志物。基于这些 VOC 生物标志物，开发并验证支持向量机（SVM）预测模型。研究了风险因素对 ADC 和 SCC 预测的影响。发现 13 种 VOC 存在明显差异。利用 SVM 算法，VOC 生物标志物在训练集上的特异性为 90.48%、灵敏度为 83.50%和 AUC 值为 0.958。在验证集上，这些 VOC 生物标志物的灵敏度预测力为 85.71%，特异性预测力为 73.08%，AUC 值为 0.875。临床风险因素对 ADC 和 SCC 预测具有一定的预测能力。将这些风险因素整合到基于 VOC 生物标志物的预测模型中可以提高其预测准确性。这项工作表明，呼气可能有潜力精确检测 ADC 和 SCC。在区分这两种亚型时，考虑临床风险因素至关重要。
