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Open AccessArticle

A High-Performing Plasma Metabolite Panel for Early-Stage Lung Cancer Detection

Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada
BioMark Diagnostics Inc., Richmond, BC V6X 2W8, Canada
Cancer Care Manitoba, Winnipeg, MB R3E 0V9, Canada
Department of Internal Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3A 1R9, Canada
Department of Pharmacology & Therapeutics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T5, Canada
Asper Clinical Research Institute & Office of Clinical Research, St. Boniface Hospital, Winnipeg, MB R2H 2A6, Canada
Department of Pathology, University of Laval, Quebec, QC G1V 4G5, Canada
Medical Oncology Unit A.O. Papardo & Department of Human Pathology, University of Messina, 98158 Messina, Italy
Thoracic Medical Oncology Program Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, MD 21201, USA
Author to whom correspondence should be addressed.
Cancers 2020, 12(3), 622;
Received: 10 February 2020 / Revised: 2 March 2020 / Accepted: 5 March 2020 / Published: 7 March 2020
The objective of this research is to use metabolomic techniques to discover and validate plasma metabolite biomarkers for the diagnosis of early-stage non-small cell lung cancer (NSCLC). The study included plasma samples from 156 patients with biopsy-confirmed NSCLC along with age and gender-matched plasma samples from 60 healthy controls. A fully quantitative targeted mass spectrometry (MS) analysis (targeting 138 metabolites) was performed on all samples. The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and smoking history were used to determine optimal sets of biomarkers and optimal regression models for identifying different stages of NSCLC using the discovery sets. The same biomarkers and regression models were used and assessed on the validation models. Univariate and multivariate statistical analysis identified β-hydroxybutyric acid, LysoPC 20:3, PC ae C40:6, citric acid, and fumaric acid as being significantly different between healthy controls and stage I/II NSCLC. Robust predictive models with areas under the curve (AUC) > 0.9 were developed and validated using these metabolites and other, easily measured clinical data for detecting different stages of NSCLC. This study successfully identified and validated a simple, high-performing, metabolite-based test for detecting early stage (I/II) NSCLC patients in plasma. While promising, further validation on larger and more diverse cohorts is still required. View Full-Text
Keywords: lung cancer; early detection; cancer staging; metabolomics; LC-MS lung cancer; early detection; cancer staging; metabolomics; LC-MS
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Zhang, L.; Zheng, J.; Ahmed, R.; Huang, G.; Reid, J.; Mandal, R.; Maksymuik, A.; Sitar, D.S.; Tappia, P.S.; Ramjiawan, B.; Joubert, P.; Russo, A.; Rolfo, C.D.; Wishart, D.S. A High-Performing Plasma Metabolite Panel for Early-Stage Lung Cancer Detection. Cancers 2020, 12, 622.

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