Metabolomic-based biomarker discovery for non-invasive lung cancer screening: a case study
File(s)
Accepted version
Author(s)
O'Shea, K
Cameron, SJ
Lewis, KE
Lu, C
Mur, LA
Type
Journal Article
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 cancer (SCLC) and non-small cell lung cancer. These metabolites achieved excellent cross validation performance with a sensitivity of 80% and specificity of 100% for predicting SCLC. CONCLUSIONS: These results indicate that sputum metabolic profiling may have potential for screening of lung cancer and lung cancer recurrence, and may greatly improve effectiveness of clinical intervention.
Date Issued
2016-07-14
Date Acceptance
2016-07-11
Citation
Biochimica et Biophysica Acta - General Subjects, 2016, 1860 (11, Part B), pp.2682-2687
ISSN
0006-3002
Publisher
Elsevier
Start Page
2682
End Page
2687
Journal / Book Title
Biochimica et Biophysica Acta - General Subjects
Volume
1860
Issue
11, Part B
Copyright Statement
© 2016 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Subjects
Artificial neural networks
Biomarkers
Lung cancer
Metabolomics
Small vs non-small cell lung cancer
Sputum
06 Biological Sciences
02 Physical Sciences
Publication Status
Published