Rodríguez-Aguilar Maribel, Díaz de León-Martínez Lorena, Gorocica-Rosete Patricia, Pérez-Padilla Rogelio, Domínguez-Reyes Carlos Alberto, Tenorio-Torres Juan Alberto, Ornelas-Rebolledo Omar, Mehta Garima, Zamora-Mendoza Blanca Nohemí, Flores-Ramírez Rogelio
Centro de Investigación Aplicada en Ambiente y Salud, CIACYT, Facultad de Medicina, Universidad Autónoma de San Luis Potosí. Av. Venustiano Carranza 2405, CP 78210, San Luis Potosí, SLP, Mexico.
Centro de Investigación Aplicada en Ambiente y Salud, CIACYT, Facultad de Medicina, Universidad Autónoma de San Luis Potosí. Av. Venustiano Carranza 2405, CP 78210, San Luis Potosí, SLP, Mexico.
Clin Chim Acta. 2021 Jul;518:83-92. doi: 10.1016/j.cca.2021.03.016. Epub 2021 Mar 22.
Analysis of volatile organic compounds (VOCs) in exhaled breath has been proposed as a screening method that discriminates between disease and healthy subjects, few studies evaluate whether these chemical fingerprints are specific when compared between diseases. We evaluated global VOCs and their discrimination capacity in chronic obstructive pulmonary disease (COPD), lung cancer, breast cancer and healthy subjects by chemoresistive sensors and chemometric analysis.
A cross-sectional study was conducted with the participation of 30 patients with lung cancer, 50 with breast cancer, 50 with COPD and 50 control subjects. Each participant's exhaled breath was analyzed with the electronic nose. A multivariate analysis was carried: principal component analysis (PCA) and, canonical analysis of principal coordinates (CAP). Twenty single-blind samples from the 4 study groups were evaluated by CAP.
A separation between the groups of patients to the controls was achieved through PCA with explanations of >90% of the data and with a correct classification of 100%. In the CAP of the 4 study groups, discrimination between the diseases was obtained with 2 canonical axes with a correct general classification of 91.35%. This model was used for the prediction of the single-blind samples resulting in correct classification of 100%.
The application of chemoresistive gas sensors and chemometric analysis can be used as a useful tool for a screening test for lung cancer, breast cancer and COPD since this equipment detects the set of VOCs present in the exhaled breath to generate a characteristic chemical fingerprint of each disease.
呼气中挥发性有机化合物(VOCs)的分析已被提议作为一种区分疾病患者和健康受试者的筛查方法,但很少有研究评估这些化学指纹在不同疾病之间进行比较时是否具有特异性。我们通过化学电阻传感器和化学计量学分析评估了慢性阻塞性肺疾病(COPD)、肺癌、乳腺癌患者及健康受试者的整体挥发性有机化合物及其鉴别能力。
进行了一项横断面研究,纳入30例肺癌患者、50例乳腺癌患者、50例慢性阻塞性肺疾病患者和50例对照受试者。使用电子鼻分析每位参与者的呼气。进行了多变量分析:主成分分析(PCA)和主坐标典型分析(CAP)。通过CAP对来自4个研究组的20个单盲样本进行了评估。
通过主成分分析实现了患者组与对照组之间的区分,数据解释率>90%,正确分类率为100%。在4个研究组的主坐标典型分析中,通过2个典型轴实现了疾病之间的区分,总体正确分类率为91.35%。该模型用于预测单盲样本,正确分类率为100%。
化学电阻气体传感器和化学计量学分析的应用可作为肺癌、乳腺癌和慢性阻塞性肺疾病筛查试验的有用工具,因为该设备可检测呼气中存在的挥发性有机化合物集,以生成每种疾病的特征化学指纹。