Please use this identifier to cite or link to this item: http://hdl.handle.net/10668/11307
Title: Exhaled breath condensate biomarkers for the early diagnosis of lung cancer using proteomics.
Authors: López-Sánchez, Laura M
Jurado-Gámez, Bernabé
Feu-Collado, Nuria
Valverde, Araceli
Cañas, Amanda
Fernández-Rueda, José L
Aranda, Enrique
Rodríguez-Ariza, Antonio
Keywords: noninvasive technique;proteome;pulmonary disease
metadata.dc.subject.mesh: Adenocarcinoma
Biomarkers
Breath Tests
Carcinoma, Neuroendocrine
Carcinoma, Squamous Cell
Early Detection of Cancer
Exhalation
Female
Humans
Lung Neoplasms
Male
Middle Aged
Proteome
Proteomics
Small Cell Lung Carcinoma
Issue Date: 15-Jun-2017
Abstract: We explored whether the proteomic analysis of exhaled breath condensate (EBC) may provide biomarkers for noninvasive screening for the early detection of lung cancer (LC). EBC was collected from 192 individuals [49 control (C), 49 risk factor-smoking (S), 46 chronic obstructive pulmonary disease (COPD) and 48 LC]. With the use of liquid chromatography and tandem mass spectrometry, 348 different proteins with a different pattern among the four groups were identified in EBC samples. Significantly more proteins were identified in the EBC from LC compared with other groups (C: 12.4 ± 1.3; S: 15.3 ± 1; COPD: 14 ± 1.6; LC: 24.2 ± 3.6; P = 0.0001). Furthermore, the average number of proteins identified per sample was significantly higher in LC patients, and receiver operating characteristic curve (ROC) analysis showed an area under the curve of 0.8, indicating diagnostic value. Proteins frequently detected in EBC, such as dermcidin and hornerin, along with others much less frequently detected, such as hemoglobin and histones, were identified. Cytokeratins (KRTs) were the most abundant proteins in EBC samples, and levels of KRT6A, KRT6B, and KRT6C isoforms were significantly higher in samples from LC patients (P = 0.0031, 0.0011, and 0.0009, respectively). Moreover, the amount of most KRTs in EBC samples from LC patients showed a significant positive correlation with tumor size. Finally, we used a random forest algorithm to generate a robust model using EBC protein data for the diagnosis of patients with LC where the area under the ROC curve obtained indicated a good classification (82%). Thus this study demonstrates that the proteomic analysis of EBC samples is an appropriated approach to develop biomarkers for the diagnosis of lung cancer.
URI: http://hdl.handle.net/10668/11307
metadata.dc.identifier.doi: 10.1152/ajplung.00119.2017
Appears in Collections:Producción 2020

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