No abstract

Lung cancer (LC) incidence represents 11.5% of all new cancers, resulting in 1.72 million deaths worldwide in 2015. With the aim to investigate the capability of the electronic nose (e-nose) technology for detecting and differentiating complex mixtures of volatile organic compounds in biofluids ex-vivo, we enrolled 50 patients with suspected LC and 50 matching controls. Tissue biopsy was taken from suspicious lung mass for histopathological evaluation and blood, exhaled breath, and urine samples were collected from all participants and qualitatively processed using e-nose. Odor-print patterns were further analysed using the principal component analysis (PCA) and artificial neural network (ANN) analysis. Adenocarcinoma, non-small cell LC and squamous cell carcinoma were the predominant pathological types among LC patients. PCA cluster-plots showed a clear distinction between LC patients and controls for all biological samples; where the overall success ratios of classification for principal components #1 and #2 were: 95.46, 82.01, and 91.66% for blood, breath and urine samples, respectively. Moreover, ANN showed a better discrimination between LC patients and controls with success ratios of 95.74, 91.67 and 100% for blood, breath and urine samples, respectively. The e-nose is an easy noninvasive tool, capable of identifying LC patients from controls with great precision.

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miR-155 was highly overexpressed, yet it did not correlate with stages, while miR-486- 5p was extremely underexpressed and significantly correlated with stages of LC. Thus, their detection represents an excellent diagnostic/prognostic tool to support more established techniques linked to LC spread locally and systemically.

No abstract