Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease

Stanislaw Deja, Irena Porebska, Aneta Kowal, Adam Zabek, Wojciech Barg, Konrad Pawelczyk, Ivana Stanimirova, Michal Daszykowski, Anna Korzeniewska, Renata Jankowska, Piotr Mlynarz

Research output: Contribution to journalArticle

58 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)369-380
Number of pages12
JournalJournal of Pharmaceutical and Biomedical Analysis
Volume100
DOIs
StatePublished - Jan 1 2014
Externally publishedYes

Fingerprint

Pulmonary diseases
Metabolomics
Neoplasm Staging
Chronic Obstructive Pulmonary Disease
Lung Neoplasms
Area Under Curve
Glycoproteins
Nuclear magnetic resonance
Dermatoglyphics
Creatine
Isoleucine
Biomarkers
Mannose
Metabolites
Choline
Citric Acid
Leucine
Tobacco Products
Glycerol
Lysine

Keywords

  • H NMR spectroscopy
  • COPD-chronic obstructive pulmonary disease
  • Lung cancer
  • Metabolic fingerprinting
  • Metabolomics

ASJC Scopus subject areas

  • Analytical Chemistry
  • Pharmaceutical Science
  • Drug Discovery
  • Spectroscopy
  • Clinical Biochemistry

Cite this

Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease. / Deja, Stanislaw; Porebska, Irena; Kowal, Aneta; Zabek, Adam; Barg, Wojciech; Pawelczyk, Konrad; Stanimirova, Ivana; Daszykowski, Michal; Korzeniewska, Anna; Jankowska, Renata; Mlynarz, Piotr.

In: Journal of Pharmaceutical and Biomedical Analysis, Vol. 100, 01.01.2014, p. 369-380.

Research output: Contribution to journalArticle

Deja, S, Porebska, I, Kowal, A, Zabek, A, Barg, W, Pawelczyk, K, Stanimirova, I, Daszykowski, M, Korzeniewska, A, Jankowska, R & Mlynarz, P 2014, 'Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease', Journal of Pharmaceutical and Biomedical Analysis, vol. 100, pp. 369-380. https://doi.org/10.1016/j.jpba.2014.08.020
Deja, Stanislaw ; Porebska, Irena ; Kowal, Aneta ; Zabek, Adam ; Barg, Wojciech ; Pawelczyk, Konrad ; Stanimirova, Ivana ; Daszykowski, Michal ; Korzeniewska, Anna ; Jankowska, Renata ; Mlynarz, Piotr. / Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease. In: Journal of Pharmaceutical and Biomedical Analysis. 2014 ; Vol. 100. pp. 369-380.
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