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Showing content from https://pubmed.ncbi.nlm.nih.gov/27007686/ below:

Identification of metabolomics panels for potential lung cancer screening by analysis of exhaled breath condensate

. 2016 Mar 23;10(2):026002. doi: 10.1088/1752-7155/10/2/026002. Identification of metabolomics panels for potential lung cancer screening by analysis of exhaled breath condensate

Affiliations

Affiliation

¹ Department of Analytical Chemistry, Annex Marie Curie Building, Campus of Rabanales, University of Córdoba, E-14014, Córdoba, Spain. Institute of Biomedical Research Maimónides (IMIBIC), Reina Sofía Hospital, University of Córdoba, E-14004, Córdoba, Spain.

PMID: 27007686
DOI: 10.1088/1752-7155/10/2/026002

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Identification of metabolomics panels for potential lung cancer screening by analysis of exhaled breath condensate

A Peralbo-Molina et al. J Breath Res. 2016.

. 2016 Mar 23;10(2):026002. doi: 10.1088/1752-7155/10/2/026002. Affiliation

¹ Department of Analytical Chemistry, Annex Marie Curie Building, Campus of Rabanales, University of Córdoba, E-14014, Córdoba, Spain. Institute of Biomedical Research Maimónides (IMIBIC), Reina Sofía Hospital, University of Córdoba, E-14004, Córdoba, Spain.

PMID: 27007686
DOI: 10.1088/1752-7155/10/2/026002

Item in Clipboard

Abstract

Exhaled breath condensate (EBC) is one of the less employed biofluids when searching for clinical markers, despite its non-invasive sampling and the potential relationship between its composition and respiratory disease phenotypes such as lung cancer. The advanced stage at which lung cancer is usually detected is the main reason for the high mortality rate of this carcinogenic disease. In this preliminary research, EBC was used as clinical sample to develop a screening tool for lung cancer discrimination from two control groups (with and without risk factor). Three panels of metabolites were configured using the PanelomiX tool to minimize false negatives (specificity) and false positives (sensitivity). The combination of five metabolites led to three panels providing a sensitivity above 77.9%, specificity above 67.5% and the area under the curve (AUC) above 77.5% for the three panels. An additional study was developed as a first approach to study the statistical significance of metabolites at different stages of lung cancer.

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