Loading…
Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease
•First report of metabolic differences COPD vs. different stages of lung cancer (LC).•Human serum NMR analysis.•NMR metabolic fingerprints were modelled using OPLS-DA.•Discriminant models allow to distinguish COPD vs. early and advanced LC patients.•Metabolite biomarkers may prove to be useful in di...
Saved in:
Published in: | Journal of pharmaceutical and biomedical analysis 2014-11, Vol.100, p.369-380 |
---|---|
Main Authors: | , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | •First report of metabolic differences COPD vs. different stages of lung cancer (LC).•Human serum NMR analysis.•NMR metabolic fingerprints were modelled using OPLS-DA.•Discriminant models allow to distinguish COPD vs. early and advanced LC patients.•Metabolite biomarkers may prove to be useful in distinguishing lung cancer states.
Chronic obstructive pulmonary disease (COPD) and lung cancer are widespread lung diseases. Cigarette smoking is a high risk factor for both the diseases. COPD may increase the risk of developing lung cancer. Thus, it is crucial to be able to distinguish between these two pathological states, especially considering the early stages of lung cancer. Novel diagnostic and monitoring tools are required to properly determine lung cancer progression because this information directly impacts the type of the treatment prescribed. In this study, serum samples collected from 22 COPD and 77 lung cancer (TNM stages I, II, III, and IV) patients were analyzed. Then, a collection of NMR metabolic fingerprints was modeled using discriminant orthogonal partial least squares regression (OPLS-DA) and further interpreted by univariate statistics. The constructed discriminant models helped to successfully distinguish between the metabolic fingerprints of COPD and lung cancer patients (AUC training=0.972, AUC test=0.993), COPD and early lung cancer patients (AUC training=1.000, AUC test=1.000), and COPD and advanced lung cancer patients (AUC training=0.983, AUC test=1.000). Decreased acetate, citrate, and methanol levels together with the increased N-acetylated glycoproteins, leucine, lysine, mannose, choline, and lipid (CH3(CH2)n) levels were observed in all lung cancer patients compared with the COPD group. The evaluation of lung cancer progression was also successful using OPLS-DA (AUC training=0.811, AUC test=0.904). Based on the results, the following metabolite biomarkers may prove useful in distinguishing lung cancer states: isoleucine, acetoacetate, and creatine as well as the two NMR signals of N-acetylated glycoproteins and glycerol. |
---|---|
ISSN: | 0731-7085 1873-264X |
DOI: | 10.1016/j.jpba.2014.08.020 |