Esophageal Cancer Metabolite Biomarkers Detected by LC-MS and NMR Methods

Abstract

BackgroundEsophageal adenocarcinoma (EAC) is a rarely curable disease and is rapidly rising worldwide in incidence. Barret's esophagus (BE) and high-grade dysplasia (HGD) are considered major risk factors for invasive adenocarcinoma. In the current study, unbiased global metabolic profiling methods were applied to serum samples from patients with EAC, BE and HGD, and healthy individuals, in order to identify metabolite based biomarkers associated with the early stages of EAC with the goal of improving prognostication.Methodology/Principal FindingsSerum metabolite profiles from patients with EAC (n = 67), BE (n = 3), HGD (n = 9) and healthy volunteers (n = 34) were obtained using high performance liquid chromatography-mass spectrometry (LC-MS) methods. Twelve metabolites differed significantly (p<0.05) between EAC patients and healthy controls. A partial least-squares discriminant analysis (PLS-DA) model had good accuracy with the area under the receiver operative characteristic curve (AUROC) of 0.82. However, when the results of LC-MS were combined with 8 metabolites detected by nuclear magnetic resonance (NMR) in a previous study, the combination of NMR and MS detected metabolites provided a much superior performance, with AUROC = 0.95. Further, mean values of 12 of these metabolites varied consistently from healthy controls to the high-risk individuals (BE and HGD patients) and EAC subjects. Altered metabolic pathways including a number of amino acid pathways and energy metabolism were identified based on altered levels of numerous metabolites.Conclusions/SignificanceMetabolic profiles derived from the combination of LC-MS and NMR methods readily distinguish EAC patients and potentially promise important routes to understanding the carcinogenesis and detecting the cancer. Differences in the metabolic profiles between high-risk individuals and the EAC indicate the possibility of identifying the patients at risk much earlier to the development of the cancer.

Diagnostic Metabolomic Blood Tests for Endoluminal Gastrointestinal Cancer—A Systematic Review and Assessment of Quality

“…In this review, there was evidence that metabolite modulation was discernable at very early stages of GI cancer (30,46,52,(57)(58)(59). Several studies reported incremental metabolomic changes with invasive (41,43,58,59) and metastatic (46,70,71) progression. In addition, three studies in colorectal cancer that were excluded from the appraisal process owing to their design nonetheless demonstrated pertinent findings to metabolomic dynamics.…”

Early Diagnostic Biomarkers for Esophageal Adenocarcinoma—The Current State of Play

“…Their model based on Partial Least Square Discrimination Analysis was able to distinguish between different phenotypes by achieving area under receiver operating characteristics curve (AUROC) as high as 0.95. On the basis of candidate metabolites, they identified altered pathways associated with EAC development to be energy metabolism, fatty acid metabolism, and amino acid metabolism (143,144). Urine metabolomics could also distinguish between healthy, Barrett's esophagus, and EAC phenotypes.…”

Serum metabolomics for early diagnosis of esophageal squamous cell carcinoma by UHPLC-QTOF/MS

“…Metabolomics, the global semi-quantitative assessment of endogenous small molecule metabolites within a biological system, has been successfully utilized in cancer biomarker discovery (Spratlin et al 2009;Zhang et al 2013, b;Ke et al 2015). Up to now, a few ESCC metabolomics studies has been reported using specimen of serum (Djukovic et al 2010;Zhang et al 2011;Ikeda et al 2012;Zhang et al 2012aZhang et al , b, 2013Jin et al 2014;Mir et al 2015), plasma (Hasim et al 2012;Liu et al 2013;Xu et al 2013;Ma et al 2014), urine (Davis et al 2012;Hasim et al 2012), tissue (Wu et al 2009;Yakoub et al 2010;Wang et al 2013;Yang et al 2013;Lynam-Lennon et al 2014) or gastric content (Kumar et al 2012), and conducted on mass spectrometry (MS) and nuclear magnetic resonance (NMR) platforms. These studies revealed significantly perturbed metabolic expression in ESCC patients compared with healthy controls.…”