Skip to main content
Log in

Candidate serum metabolite biomarkers for differentiating gastroesophageal reflux disease, Barrett’s esophagus, and high-grade dysplasia/esophageal adenocarcinoma

  • Original Article
  • Published:
Metabolomics Aims and scope Submit manuscript

Abstract

Introduction/objectives

Incidence of esophageal adenocarcinoma (EA), an often fatal cancer, has increased sharply over recent decades. Several important risk factors (reflux, obesity, smoking) have been identified for EA and its precursor, Barrett’s esophagus (BE), but a key challenge remains in identifying individuals at highest risk, since most with reflux do not develop BE, and most with BE do not progress to cancer. Metabolomics represents an emerging approach for identifying novel biomarkers associated with cancer development.

Methods

We used targeted liquid chromatography-mass spectrometry (LC-MS) to profile 57 metabolites in 322 serum specimens derived from individuals with gastroesophageal reflux disease (GERD), BE, high-grade dysplasia (HGD), or EA, drawn from two well-annotated epidemiologic parent studies.

Results

Multiple metabolites differed significantly (P < 0.05) between BE versus GERD (n = 9), and between HGD/EA versus BE (n = 4). Several top candidates (FDR q ≤ 0.15), including urate, homocysteine, and 3-nitrotyrosine, are linked to inflammatory processes, which may contribute to BE/EA pathogenesis. Multivariate modeling achieved moderate discrimination between HGD/EA and BE (AUC = 0.75), with less pronounced separation for BE versus GERD (AUC = 0.64).

Conclusion

Serum metabolite differences can be detected between individuals with GERD versus BE, and between those with BE versus HGD/EA, and may help differentiate patients at different stages of progression to EA.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Abbassi-Ghadi, N., et al. (2013). Metabolomic profiling of oesophago-gastric cancer: A systematic review. European Journal of Cancer, 49, 3625–3637.

    Article  CAS  PubMed  Google Scholar 

  • Allameh, A., et al. (2009). Immunohistochemical analysis of selected molecular markers in esophagus precancerous, adenocarcinoma and squamous cell carcinoma in Iranian subjects. Cancer Epidemiology, 33, 79–84.

    Article  CAS  PubMed  Google Scholar 

  • Asiago, V. M., et al. (2010). Early detection of recurrent breast cancer using metabolite profiling. Cancer Research, 70, 8309–8318.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Baniasadi, H., et al. (2013). Targeted metabolic profiling of hepatocellular carcinoma and hepatitis C using LC-MS/MS. Electrophoresis, 34, 2910–2917.

    CAS  PubMed  Google Scholar 

  • Bobe, G., et al. (2010). Serum adiponectin, leptin, C-peptide, homocysteine, and colorectal adenoma recurrence in the Polyp Prevention Trial. Cancer Epidemiology, Biomarkers and Prevention: a Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology, 19, 1441–1452.

    Article  CAS  Google Scholar 

  • Carroll, P. A., et al. (2015). Deregulated myc requires mondoa/mlx for metabolic reprogramming and tumorigenesis. Cancer Cell, 27, 271–285.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cook, M. B., et al. (2014). Gastroesophageal reflux in relation to adenocarcinomas of the esophagus: A pooled analysis from the Barrett’s and Esophageal adenocarcinoma consortium (BEACON). PLoS One, 9, e103508.

    Article  PubMed  PubMed Central  Google Scholar 

  • Danese, S., et al. (2005). Homocysteine triggers mucosal microvascular activation in inflammatory bowel disease. The American journal of gastroenterology, 100, 886–895.

    Article  CAS  PubMed  Google Scholar 

  • Dave, U., et al. (2004). In vitro 1 H-magnetic resonance spectroscopy of Barrett’s esophageal mucosa using magic angle spinning techniques. European Journal of Gastroenterology and Hepatology, 16, 1199–1205.

    Article  PubMed  Google Scholar 

  • Davis, V. W., Schiller, D. E., Eurich, D., & Sawyer, M. B. (2012). Urinary metabolomic signature of esophageal cancer and Barrett’s esophagus. World Journal of Surgical Oncology, 10, 271.

    Article  PubMed  PubMed Central  Google Scholar 

  • Denkert, C., et al. (2006). Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors. Cancer Research, 66, 10795–10804.

    Article  CAS  PubMed  Google Scholar 

  • Djukovic, D., Baniasadi, H. R., Kc, R., Hammoud, Z., & Raftery, D. (2010). Targeted serum metabolite profiling of nucleosides in esophageal adenocarcinoma. Rapid Communications in Mass Spectrometry, 24, 3057–3062.

    Article  CAS  PubMed  Google Scholar 

  • Doran, S. T., et al. (2003). Pathology of Barrett’s esophagus by proton magnetic resonance spectroscopy and a statistical classification strategy. American Journal of Surgery, 185, 232–238.

    Article  PubMed  Google Scholar 

  • Edelstein, Z. R., Bronner, M. P., Rosen, S. N., & Vaughan, T. L. (2009). Risk factors for Barrett’s esophagus among patients with gastroesophageal reflux disease: a community clinic-based case-control study. The American Journal of Gastroenterology, 104, 834–842.

    Article  PubMed  PubMed Central  Google Scholar 

  • Edelstein, Z. R., Farrow, D. C., Bronner, M. P., Rosen, S. N., & Vaughan, T. L. (2007). Central adiposity and risk of Barrett’s esophagus. Gastroenterology, 133, 403–411.

    Article  PubMed  Google Scholar 

  • Fanidi, A., et al. (2015). A prospective study of one-carbon metabolism biomarkers and cancer of the head and neck and esophagus. International Journal of Cancer. Journal International du Cancer, 136, 915–927.

    Article  CAS  PubMed  Google Scholar 

  • Fini, M. A., Elias, A., Johnson, R. J., & Wright, R. M. (2012). Contribution of uric acid to cancer risk, recurrence, and mortality. Clin Transl Med, 1, 16.

    Article  PubMed  PubMed Central  Google Scholar 

  • Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33, 1–22.

    Article  PubMed  PubMed Central  Google Scholar 

  • Galipeau, P. C., et al. (2007). NSAIDs modulate CDKN2A, TP53, and DNA content risk for progression to esophageal adenocarcinoma. PLoS Medicine, 4, 342–353.

    Article  CAS  Google Scholar 

  • Glickman, M. E., Rao, S. R., & Schultz, M. R. (2014). False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. Journal of Clinical Epidemiology, 67, 850–857.

    Article  PubMed  Google Scholar 

  • Goldstein, S. R., Yang, G. Y., Chen, X., Curtis, S. K., & Yang, C. S. (1998). Studies of iron deposits, inducible nitric oxide synthase and nitrotyrosine in a rat model for esophageal adenocarcinoma. Carcinogenesis, 19, 1445–1449.

    Article  CAS  PubMed  Google Scholar 

  • Gowda, G., et al. (2008). Metabolomics-based methods for early disease diagnostics. Expert Review of Molecular Diagnostics, 8, 617–633.

    Article  CAS  PubMed  Google Scholar 

  • Gu, H., Gowda, G., & Raftery, D. (2012). Metabolic profiling: Are we en route to better diagnostic tests for cancer? Future Oncology, 8, 1207–1210.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jiménez, P., et al. (2005). Free radicals and antioxidant systems in reflux esophagitis and Barrett’s esophagus. World journal of gastroenterology: WJG, 11, 2697–2703.

    Article  PubMed  PubMed Central  Google Scholar 

  • Kandulski, A., & Malfertheiner, P. (2011). Gastroesophageal reflux disease—from reflux episodes to mucosal inflammation. Nature Reviews Gastroenterology & Hepatology, 9, 15–22.

    Article  Google Scholar 

  • Kim, K., et al. (2014). Mealtime, temporal, and daily variability of the human urinary and plasma metabolomes in a tightly controlled environment. PLoS One, 9. doi:10.1371/journal.pone.0086223.

  • Kolonel, L. N., Yoshizawa, C., Nomura, A. M., & Stemmermann, G. N. (1994). Relationship of serum uric acid to cancer occurrence in a prospective male cohort. Cancer Epidemiology, Biomarkers and Prevention: a Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology, 3, 225–228.

    CAS  Google Scholar 

  • Li, X., et al. (2015). Assessment of esophageal adenocarcinoma risk using somatic chromosome alterations in longitudinal samples in Barrett’s esophagus. Cancer Prev Res (Phila), doi:10.1158/1940-6207.CAPR-15-0130.

    Google Scholar 

  • Liesenfeld, D. B., Habermann, N., Owen, R. W., Scalbert, A., & Ulrich, C. M. (2013). Review of mass spectrometry-based metabolomics in cancer research. Cancer Epidemiology Biomarkers and Prevention, 22, 2182–2201.

    Article  CAS  Google Scholar 

  • Mayers, J. R., et al. (2014). Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nature Medicine, 20, doi:10.1038/nm.3686.

  • Miller, J. W., et al. (2013). Homocysteine, cysteine, and risk of incident colorectal cancer in the Women’s Health Initiative observational cohort. The American Journal of Clinical Nutrition, 97, 827–834.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999). Metabonomics’: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica; The Fate of Foreign Compounds in Biological Systems, 29, 1181–1189.

    Article  CAS  PubMed  Google Scholar 

  • O’Connell, T. M. (2012). Recent advances in metabolomics in oncology. Bioanalysis, 4, 431–451.

    Article  PubMed  Google Scholar 

  • Phelan, J. J., et al. (2014). Differential expression of mitochondrial energy metabolism profiles across the metaplasia-dysplasia-adenocarcinoma disease sequence in Barrett’s oesophagus. Cancer Letters, 354, 122–131.

    Article  CAS  PubMed  Google Scholar 

  • Reid, B. J., Blount, P. L., & Rabinovitch, P. S. (2003). Biomarkers in Barrett’s esophagus. Gastrointestinal Endoscopy Clinics of North America, 13, 369–397.

    Article  PubMed  Google Scholar 

  • Reid, B. J., Li, X., Galipeau, P. C., & Vaughan, T. L. (2010). Barrett’s oesophagus and oesophageal adenocarcinoma: Time for a new synthesis. Nature Reviews Cancer, 10, 87–101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ross-Innes, C. S., et al. (2015). Evaluation of a minimally invasive cell sampling device coupled with assessment of trefoil factor 3 expression for diagnosing Barrett’s esophagus: A multi-center case-control study. PLoS Medicine, 12, e1001780.

    Article  PubMed  PubMed Central  Google Scholar 

  • Rubenstein, J. H., et al. (2013). Prediction of Barrett’s esophagus among men. The American Journal of Gastroenterology, 108, 353–362.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sampliner, R. E. (2002). Updated guidelines for the diagnosis, surveillance, and therapy of Barrett’s esophagus. American Journal of Gastroenterology, 97, 1888–1895.

    Article  PubMed  Google Scholar 

  • Sampson, J. N., et al. (2013). Metabolomics in epidemiology: Sources of variability in metabolite measurements and implications. Cancer Epidemiology Biomarkers and Prevention, 22, 631–640.

    Article  CAS  Google Scholar 

  • Sanchez-Espiridion, B., et al. (2015). Identification of serum markers of esophageal adenocarcinoma by global and targeted metabolic profiling. Clinical Gastroenterology and Hepatology: the Official Clinical Practice Journal of the American Gastroenterological Association. doi:10.1016/j.cgh.2015.05.023.

    Google Scholar 

  • Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005). ROCR: Visualizing classifier performance in R. Bioinformatics (Oxford, England), 21, 3940–3941.

    Article  CAS  Google Scholar 

  • Slupsky, C. M., et al. (2010). Urine metabolite analysis offers potential early diagnosis of ovarian and breast cancers. Clinical Cancer Research, 16, 5835–5841.

    Article  CAS  PubMed  Google Scholar 

  • Sperber, H., et al. (2015). The metabolome regulates the epigenetic landscape during naive-to-primed human embryonic stem cell transition. Nature Cell Biology, 17, 1523–1535.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Spratlin, J. L., Serkova, N. J., & Eckhardt, S. G. (2009). Clinical applications of metabolomics in oncology: A review. Clinical Cancer Research, 15, 431–440.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sreekumar, A., et al. (2009). Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature, 457, 910–914.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Strasak, A. M., et al. (2009). Use of penalized splines in extended Cox-type additive hazard regression to flexibly estimate the effect of time-varying serum uric acid on risk of cancer incidence: A prospective, population-based study in 78,850 men. Annals of Epidemiology, 19, 15–24.

    Article  PubMed  Google Scholar 

  • Suchorolski, M. T., Paulson, T. G., Sanchez, C. A., Hockenbery, D., & Reid, B. J. (2013). Warburg and crabtree effects in premalignant Barrett’s esophagus cell lines with active mitochondria. PLoS One, 8, e56884.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Suzuki, M., Nishiumi, S., Matsubara, A., Azuma, T., & Yoshida, M. (2014). Metabolome analysis for discovering biomarkers of gastroenterological cancer. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, 966, 59–69.

    Article  CAS  PubMed  Google Scholar 

  • Thrift, A. P., Kendall, B. J., Pandeya, N., Vaughan, T. L., & Whiteman, D. C. (2012). A clinical risk prediction model for Barrett esophagus. Cancer Prevention Research, 5, 1115–1123.

    Article  PubMed  PubMed Central  Google Scholar 

  • Thrift, A. P., Kendall, B. J., Pandeya, N., & Whiteman, D. C. (2012). A model to determine absolute risk for Esophageal adenocarcinoma. Clinical Gastroenterology and Hepatology, 11, 138–44.e2.

    Article  PubMed  Google Scholar 

  • Vaninetti, N. M., et al. (2008). Inducible nitric oxide synthase, nitrotyrosine and p53 mutations in the molecular pathogenesis of Barrett’s esophagus and esophageal adenocarcinoma. Molecular Carcinogenesis, 47, 275–285.

    Article  CAS  PubMed  Google Scholar 

  • Vaughan, T. L. (2014). From genomics to diagnostics of esophageal adenocarcinoma. Nature Genetics, 46, 806–807.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Vaughan, T. L., & Fitzgerald, R. C. (2015). Precision prevention of oesophageal adenocarcinoma. Nature Reviews Gastroenterology and Hepatology, 12, 243–248.

    Article  PubMed  PubMed Central  Google Scholar 

  • Weaver, J. M. J. et al. (2014). Ordering of mutations in preinvasive disease stages of esophageal carcinogenesis. Nature genetics, 46, 837–843.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wikoff, W. R., et al. (2015). Diacetylspermine Is a novel prediagnostic serum biomarker for non-small-cell lung cancer and has additive performance with pro-surfactant protein B. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology. doi:10.1200/JCO.2015.61.7779.

    Google Scholar 

  • Xie, S.-H., & Lagergren, J. (2016). A model for predicting individuals’ absolute risk of esophageal adenocarcinoma: Moving towards tailored screening and prevention. International Journal of Cancer. Journal International du cancer. doi:10.1002/ijc.29988.

    Google Scholar 

  • Yakoub, D., Keun, H. C., Goldin, R., & Hanna, G. B. (2010). Metabolic profiling detects field effects in nondysplastic tissue from esophageal cancer patients. Cancer Research, 70, 9129–9136.

    Article  CAS  PubMed  Google Scholar 

  • Zhang, J., et al. (2011). Metabolomics study of esophageal adenocarcinoma. Journal of Thoracic and Cardiovascular Surgery, 141, 469–475.

    Article  CAS  PubMed  Google Scholar 

  • Zhang, J., et al. (2012). Esophageal cancer metabolite biomarkers detected by LC-MS and NMR methods. PLoS One, 7, e30181.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhou, J., & Austin, R. C. (2009). Contributions of hyperhomocysteinemia to atherosclerosis: Causal relationship and potential mechanisms. BioFactors (Oxford, England), 35, 120–129.

    Article  CAS  Google Scholar 

  • Zhu, J., et al. (2014). Colorectal cancer detection using targeted serum metabolic profiling. Journal of Proteome Research, 13, 4120–4130.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Terri Watson, Tricia Christopherson, Paul Hansen, Jessica Arnaudo, David Cowan, and Carissa Sanchez for their contributions in project management, organization of biospecimens/data, or assistance with data retrieval. This work was principally supported by NIH grant R21CA178621 (T.L.V. and D.R.) and NIH training grant T32CA009168 (T.L.V). Additional support was provided by NIH grants K05CA124911 (T.L.V.), P01CA091955 (B.J.R), and 5P30CA015704 (D.R.).

Author contributions

Conception and design: M.F.B., T.L.V., D.R. Subject recruitment: T.L.V., B.J.R. Data acquisition: H.G., D.D., J.Z. Analysis and interpretation of data: M.F.B, H.G., D.D., L.O., D.R., T.L.V. Drafting of the manuscript: M.F.B., T.L.V., H.G., D.R. Study supervision: D.R., T.L.V. All authors critically revised the manuscript for intellectual content.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Daniel Raftery or Thomas L. Vaughan.

Ethics declarations

Conflict of interest

D.R. holds equity and an executive role in Matrix‑Bio, Inc. (IN, USA). The authors have no other potential conflicts of interest to disclose.

Compliance with ethical requirements

All authors complied with ethical policies as described. Potential conflicts of interests were disclosed. This study included human participants and was approved by the Institutional Review Board at the Fred Hutchinson Cancer Research Center. Appropriate informed consent was obtained.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 201 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Buas, M.F., Gu, H., Djukovic, D. et al. Candidate serum metabolite biomarkers for differentiating gastroesophageal reflux disease, Barrett’s esophagus, and high-grade dysplasia/esophageal adenocarcinoma. Metabolomics 13, 23 (2017). https://doi.org/10.1007/s11306-016-1154-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11306-016-1154-y

Keywords

Navigation