Jul 12, 2016

Metabolomic-based biomarker discovery for non-invasive lung cancer screening: A case study

BioRxiv : the Preprint Server for Biology
Keiron O'SheaLuis A J Mur

Abstract

Background: Lung cancer (LC) is one of the leading lethal cancers worldwide, with an estimated 18.4% of all cancer deaths being attributed to the disease. Despite developments in cancer diagnosis and treatment over the previous thirty years, LC has seen little to no improvement in the overall five year survival rate after initial diagnosis. Methods: In this paper, we extended a recent study which profiled the metabolites in sputum from patients with lung cancer and age-matched volunteers smoking controls using flow infusion electrospray ion mass spectrometry. We selected key metabolites for distinguishing between different classes of lung cancer, and employed artificial neural networks and leave-one-out cross-validation to evaluate the predictive power of the identified biomarkers. Results: The neural network model showed excellent performance in classification between lung cancer and control groups with the area under the receiver operating characteristic curve of 0.99. The sensitivity and specificity of for detecting cancer from controls were 96% and 94% respectively. Furthermore, we have identified six putative metabolites that were able to discriminate between sputum samples derived from patients suffering small cell lung c...Continue Reading

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Mentioned in this Paper

Biological Markers
Metabolic Process, Cellular
Study
Classification
Lung
Human Volunteers
Cessation of Life
Non-Small Cell Lung Cancer Pathway
Small Cell Carcinoma of Lung
Medical Devices

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