BACKGROUND: Volatile organic compounds (VOCs) biomarkers in breath provide a novel, noninvasive and quick approach to diagnosis lung cancer. The aim of the proposed study was to investigate the VOCs biomarkers in exhaled breath for lung cancer. METHOD: The VOCs in exhaled breath of 88 lung cancer patients, 70 lung benign disease and 85 healthy people were analyzed by Solid Phase Micro Extraction - Gas Chromatography Mass Spectrometry (SPME-GCMS). Three types of lung cancer cells and 18 lung cancer patients' tissues were cultured in vitro. The VOCs in the headspace of these cultivations were analyzed as an evidence of production mechanism of the VOCs in breath. Three lung cancer diagnosis models were constructed respectively in exhaled breath samples using Linear Discriminant Analysis (LDA). Leave one out cross validation was employed to evaluate these models. RESULTS: 23 VOCs, whose areas under curve (AUC) of receiver operating characteristic (ROC) > 0.60 and p < 0.01, were confirmed as the VOCs biomarkers for lung cancer. Three diagnostic models based on 23 VOCs could easily discriminate lung cancer patients from controls with 96.47% sensitivity and 97.47% specificity. However, the discrimination between early stage and later stage lung cancer was not very obvious.
BACKGROUND: Volatile organic compounds (VOCs) biomarkers in breath provide a novel, noninvasive and quick approach to diagnosis lung cancer. The aim of the proposed study was to investigate the VOCs biomarkers in exhaled breath for lung cancer. METHOD: The VOCs in exhaled breath of 88 lung cancerpatients, 70 lung benign disease and 85 healthy people were analyzed by Solid Phase Micro Extraction - Gas Chromatography Mass Spectrometry (SPME-GCMS). Three types of lung cancer cells and 18 lung cancerpatients' tissues were cultured in vitro. The VOCs in the headspace of these cultivations were analyzed as an evidence of production mechanism of the VOCs in breath. Three lung cancer diagnosis models were constructed respectively in exhaled breath samples using Linear Discriminant Analysis (LDA). Leave one out cross validation was employed to evaluate these models. RESULTS: 23 VOCs, whose areas under curve (AUC) of receiver operating characteristic (ROC) > 0.60 and p < 0.01, were confirmed as the VOCs biomarkers for lung cancer. Three diagnostic models based on 23 VOCs could easily discriminate lung cancerpatients from controls with 96.47% sensitivity and 97.47% specificity. However, the discrimination between early stage and later stage lung cancer was not very obvious.
Authors: Antonio De Vincentis; Giorgio Pennazza; Marco Santonico; Umberto Vespasiani-Gentilucci; Giovanni Galati; Paolo Gallo; Chiara Vernile; Claudio Pedone; Raffaele Antonelli Incalzi; Antonio Picardi Journal: Sci Rep Date: 2016-05-05 Impact factor: 4.379