Serum phosphatidylethanolamine levels distinguish benign from malignant solitary pulmonary nodules and represent a potential diagnostic biomarker for lung cancer

Abstract

BACKGROUND:

Recent computed tomography (CT) screening trials showed that it is effective for early detection of lung cancer, but were plagued by high false positive rates. Additional blood biomarker tests designed to complement CT screening and reduce false positive rates are highly desirable.

OBJECTIVE:

Identify blood-based metabolite biomarkers for diagnosing lung cancer.

MEHTODS:

Serum samples from subjects participating in a CT screening trial were analyzed using untargeted GC-TOFMS and HILIC-qTOFMS-based metabolomics. Samples were acquired prior to diagnosis(pre-diagnostic, n= 17), at-diagnosis (n= 25) and post-diagnosis (n= 19) of lung cancer and from subjects with benign nodules (n= 29).

RESULTS:

Univariate analysis identified 40, 102 and 30 features which were significantly different between subjects with malignant (pre-, at- and post-diagnosis) solitary pulmonary nodules (SPNs) and benign SPNs, respectively. Ten metabolites were consistently different between subjects presenting malignant (pre- and at-diagnosis) or benign SPNs. Three of these 10 metabolites were phosphatidylethanolamines (PE) suggesting alterations in lipid metabolism. Accuracies of 77%, 83% and 78% in the pre-diagnosis group and 69%, 71% and 67% in the at-diagnosis group were determined for PE(34:2), PE(36:2) and PE(38:4), respectively.

CONCLUSIONS:

This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant from benign SPNs. Further studies in larger pools of samples are warranted.

Keywords

Lung cancer metabolomics phospholipids biomarkers solitary pulmonary nodules

References

Siegel

R.L.

, Miller

K.D.

and Jemal

, Cancer statistics, 2015, CA Cancer J Clin 65 (2015), 5-29.

Ries

L.A.

, Influence of extent of disease, histology, and demographic factors on lung cancer survival in the SEER population-based data, Semin Surg Oncol 10 (1994), 21-30.

Kramer

B.S.

, Berg

C.D.

, Aberle

D.R.

and Prorok

P.C.

, Lung cancer screening with low-dose helical CT: results from the National Lung Screening Trial (NLST), J Med Screen 18 (2011), 109-11.

Greenberg

A.K.

, Lu

, Goldberg

J.D.

, Eylers

, Tsay

J.C.

, Yie

T.A.

, Naidich

, McGuinness

, Pass

, Tchou-Wong

K.M.

, Addrizzo-Harris

, Chachoua

, Crawford

and Rom

W.N.

, CT scan screening for lung cancer: risk factors for nodules and malignancy in a high-risk urban cohort, PLoS One 7 (2012), e39403.

Ghosh

and Poisson

L.M.

, ``Omics'' data and levels of evidence for biomarker discovery, Genomics 93 (2009), 13-6.

Claudino

W.M.

, Goncalves

P.H.

, di Leo

, Philip

P.A.

and Sarkar

F.H.

, Metabolomics in cancer: a bench-to-bedside intersection, Crit Rev Oncol Hematol 84 (2012), 1-7.

Serkova

N.J.

and Glunde

, Metabolomics of cancer, Methods Mol Biol 520 (2009), 273-95.

Spratlin

J.L.

, Serkova

N.J.

and Eckhardt

S.G.

, Clinical applications of metabolomics in oncology: a review, Clin Cancer Res 15 (2009), 431-40.

Fahrmann

J.F.

, Kim

, DeFelice

B.C.

, Taylor

S.L.

, Gandara

D.R.

, Yoneda

K.Y.

, Cooke

D.T.

, Fiehn

, Kelly

and Miyamoto

, Investigation of Metabolomic Blood Biomarkers for Detection of Adenocarcinoma Lung Cancer, Cancer Epidemiol Biomarkers Prev (2015).

10.

Wikoff

W.R.

, Hanash

, DeFelice

, Miyamoto

, Barnett

, Zhao

, Goodman

, Feng

, Gandara

, Fiehn

and Taguchi

, Diacetylspermine is a Novel Prediagnostic Serum Biomarker for Non-Small-Cell Lung Cancer and Has Additive Performance With Pro-Surfactant Protein B, J Clin Oncol (2015).

11.

Scholz

and Fiehn

, SetupX-a public study design database for metabolomic projects, Pac Symp Biocomput (2007), 169-80.

12.

Fiehn

, Wohlgemuth

and Scholz

, Setup and annotation of metabolomic experiments by integrating biological and mass spectrometric metadata, Data Integration in the Life Sciences, Proceedings 3615 (2005), 224-239.

13.

Pluskal

, Castillo

, Villar-Briones

and Oresic

, MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data, BMC Bioinformatics 11 (2010), 395.

14.

Kind

, Liu

K.H.

, Lee do

, DeFelice

, Meissen

J.K.

and Fiehn

, LipidBlast in silico tandem mass spectrometry database for lipid identification, Nat Methods 10 (2013), 755-8.

15.

R Development Core Team. R: A language and environment for statistical computing, R Foundation for Statistical Computing (2011), ISBN 3-900051-900007-900050.

16.

Benjamini

and Hochberg

, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society. Series B (Methodological) (1995), 289-300.

17.

Grapov

, DeviumWeb: Dynamic Multivariate Data Analysis and Visualization Platform, 2014.

18.

Xia

, Sinelnikov

I.V.

, Han

and Wishart

D.S.

, MetaboAnalyst 3.0-making metabolomics more meaningful, Nucleic Acids Res (2015).

19.

Robin

, Turck

, Hainard

, Tiberti

, Lisacek

, Sanchez

J.C.

and Muller

, pROC: an open-source package for R and S+ to analyze and compare ROC curves, BMC Bioinformatics 12 (2011), 77.

20.

Blaak

, Gender differences in fat metabolism, Curr Opin Clin Nutr Metab Care 4 (2001), 499-502.

21.

Barzilai

, Huffman

D.M.

, Muzumdar

R.H.

and Bartke

, The critical role of metabolic pathways in aging, Diabetes 61 (2012), 1315-22.

22.

Chiolero

, Faeh

, Paccaud

and Cornuz

, Consequences of smoking for body weight, body fat distribution, and insulin resistance, Am J Clin Nutr 87 (2008), 801-9.

23.

Murphy

R.A.

, Bureyko

T.F.

, Mourtzakis

, Chu

Q.S.

, Clandinin

M.T.

, Reiman

and Mazurak

V.C.

, Aberrations in plasma phospholipid fatty acids in lung cancer patients, Lipids 47 (2012), 363-9.

24.

Marien

, Meister

, Muley

, Fieuws

, Bordel

, Derua

, Spraggins

, Van de Plas

, Dehairs

, Wouters

, Bagadi

, Dienemann

, Thomas

, Schnabel

P.A.

, Caprioli

R.M.

, Waelkens

and Swinnen

J.V.

, Non-small cell lung cancer is characterized by dramatic changes in phospholipid profiles, Int J Cancer 137 (2015), 1539-48.

25.

Zinrajh

, Horl

, Jurgens

, Marc

, Sok

and Cerne

, Increased phosphatidylethanolamine N-methyltr-ansferase gene expression in non-small-cell lung cancer tissue predicts shorter patient survival, Oncol Lett 7 (2014), 2175-2179.

26.

Gao

, Wen

, Wu

, Wen

and Ong

C.N.

, Metabolic profiling of plasma from benign and malignant pulmonary nodules patients using mass spectrometry-based metabolomics, Metabolites 3 (2013), 539-51.

27.

Huang

L.J.

, Chen

S.X.

, Luo

W.J.

, Jiang

H.H.

, Zhang

P.F.

and Yi

, Proteomic analysis of secreted proteins of non-small cell lung cancer, Ai Zheng 25 (2006), 1361-7.

28.

G.P.

, Chen

G.Q.

, Wu

, Shen

and Ji

, The expression of PEBP4 protein in lung squamous cell carcinoma, Tumour Biol 32 (2011), 1257-63.

29.

Wang

, Li

, Liu

, Sun

, Chen

, Li

, Qiu

, Zhang

, Wan

and Cao

, A novel human phosphatidyleth-anolamine-binding protein resists tumor necrosis factor alpha-induced apoptosis by inhibiting mitogen-activated protein kinase pathway activation and phosphatidylethanolamine externalization, J Biol Chem 279 (2004), 45855-64.

30.

Liu

, Mazzone

P.J.

, Cata

J.P.

, Kurz

, Bauer

, Mascha

E.J.

and Sessler

D.I.

, Serum free fatty acid biomarkers of lung cancer, Chest 146 (2014), 670-9.

31.

Haeggstrom

J.Z.

, Rinaldo-Matthis

, Wheelock

C.E.

and Wetterholm

, Advances in eicosanoid research, novel therapeutic implications, Biochem Biophys Res Commun 396 (2010), 135-9.

32.

Sim

Y.T.

and Poon

F.W.

, Imaging of solitary pulmonary nodule-a clinical review, Quant Imaging Med Surg 3 (2013), 316-26.