, 12:116 | Cite as

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

  • Jialin WangEmail author
  • Tao Zhang
  • Xiaotao Shen
  • Jia Liu
  • Deli Zhao
  • Yawen Sun
  • Lu Wang
  • Yingjun Liu
  • Xiaoyun Gong
  • Yanxun Liu
  • Zheng-Jiang ZhuEmail author
  • Fuzhong XueEmail author
Original Article



Previous metabolomics studies have revealed perturbed metabolic signatures in esophageal squamous cell carcinoma (ESCC) patients, however, most of these studies included mainly late-staged ESCC patients due to the difficulties of collecting the early-staged samples from asymptotic ESCC subjects.


This study aims to explore the early-staged ESCC metabolic signatures and potential of serum metabolomics to diagnose ESCC at early stages.


Serum samples of 97 ESCC patients (stage 0, 39 cases; stage I, 17 cases; stage II, 11 cases, stage III, 30 cases) and 105 healthy controls (HC) were enrolled and randomly separated into training data (77 ESCCs, 84 HCs) and validation data (20 ESCCs, 21 HCs). Untargeted metabolomics was performed to identify ESCC-related metabolic signatures.


The global metabolomics profiles could clearly distinguish ESCC from HC in training data. 16 ascertained metabolites were found to be disturbed in the metabolic pathways characterized by dysregulated fatty acid biosynthesis, glycerophospholipid metabolism, choline metabolism in cancer and linoleic acid metabolism. The AUC value in validation data was 0.895, with sensitivity 85.0 % and specificity 90.5 %. Good diagnostic performances were also achieved for early stage ESCC, with the values of area under the curve (AUC) 0.881 for the ESCC patients in both stage 0 and I–II. In addition, six metabolites were found to discriminate ESCC stages. Among them, three biomarkers, dodecanoic acid, LysoPA(18:1), and LysoPC(14:0), exhibited clear trend for ESCC progression.


These findings suggest serum metabolomics, performed in a minimally noninvasive and convenient manner, may possess great potential for early diagnosis of ESCC patients.


Esophageal squamous cell carcinoma Metabolomics Early diagnosis UHPLC-QTOF/MS Biomarker 



This work was funded by National Natural Science Foundation of China (Grant Number 81573246, 81302514, 81573259, 21575151), Science and Technology Research Projects of Shandong Province (2014GSF118022), and National Natural Science Foundation of Shandong Province (RZ2014HP044). Z.-J. Z. is supported by Thousand Youth Talents Program from Chinese government and Agilent Technologies Thought Leader Award. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Human and animal rights

All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11306_2016_1050_MOESM1_ESM.docx (2.2 mb)
Supplementary material 1 (DOCX 2207 kb)


  1. Aboagye, E. O., & Bhujwalla, Z. M. (1999). Malignant transformation alters membrane choline phospholipid metabolism of human mammary epithelial cells. Cancer Research, 59, 80–84.PubMedGoogle Scholar
  2. Banni, S., Angioni, E., Casu, V., et al. (1999). Decrease in linoleic acid metabolites as a potential mechanism in cancer risk reduction by conjugated linoleic acid. Carcinogenesis, 20, 1019–1024.CrossRefPubMedGoogle Scholar
  3. Chow, C. K. (2009). Fatty acid composition of plasma phospholipids and risk of prostate cancer. Am J Clin Nutr, 89, 1946. author reply 1946–7.CrossRefPubMedGoogle Scholar
  4. Crowe, F. L., Allen, N. E., Appleby, P. N., et al. (2008). Fatty acid composition of plasma phospholipids and risk of prostate cancer in a case-control analysis nested within the European Prospective Investigation into Cancer and Nutrition. American Journal of Clinical Nutrition, 88, 1353–1363.PubMedGoogle Scholar
  5. Davis, V. W., Schiller, D. E., Eurich, D., & Sawyer, M. B. (2012). Urinary metabolomic signature of esophageal cancer and Barrett’s esophagus. World J Surg Oncol, 10, 271.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Dawsey, S. M., Fleischer, D. E., Wang, G. Q., et al. (1998). Mucosal iodine staining improves endoscopic visualization of squamous dysplasia and squamous cell carcinoma of the esophagus in Linxian, China. Cancer, 83, 220–231.CrossRefPubMedGoogle Scholar
  7. Dawsey, S. M., Lewin, K. J., Wang, G. Q., et al. (1994). Squamous esophageal histology and subsequent risk of squamous cell carcinoma of the esophagus. A prospective follow-up study from Linxian, China. Cancer, 74, 1686–1692.CrossRefPubMedGoogle Scholar
  8. 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.CrossRefPubMedGoogle Scholar
  9. Dong, Z., Tang, P., Li, L., & Wang, G. (2002). The strategy for esophageal cancer control in high-risk areas of China. Japanese Journal of Clinical Oncology, 32(Suppl), S10–S12.CrossRefPubMedGoogle Scholar
  10. Dunn, W. B., Broadhurst, D., Begley, P., et al. (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6, 1060–1083.CrossRefPubMedGoogle Scholar
  11. Eliyahu, G., Kreizman, T., & Degani, H. (2007). Phosphocholine as a biomarker of breast cancer: molecular and biochemical studies. International Journal of Cancer, 120, 1721–1730.CrossRefPubMedGoogle Scholar
  12. Fang, J. L., Vaca, C. E., Valsta, L. M., & Mutanen, M. (1996). Determination of DNA adducts of malonaldehyde in humans: effects of dietary fatty acid composition. Carcinogenesis, 17, 1035–1040.CrossRefPubMedGoogle Scholar
  13. Glunde, K., Bhujwalla, Z. M., & Ronen, S. M. (2011). Choline metabolism in malignant transformation. Nature Reviews Cancer, 11, 835–848.PubMedPubMedCentralGoogle Scholar
  14. Glunde, K., Jacobs, M. A., & Bhujwalla, Z. M. (2006). Choline metabolism in cancer: implications for diagnosis and therapy. Expert Review Molecular Diagnostics, 6, 821–829.CrossRefGoogle Scholar
  15. Guanrei, Y., & Songliang, Q. (1987). Endoscopic surveys in high-risk and low-risk populations for esophageal cancer in China with special reference to precursors of esophageal cancer. Endoscopy, 19, 91–95.CrossRefPubMedGoogle Scholar
  16. Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: the next generation. Cell, 144, 646–674.CrossRefPubMedGoogle Scholar
  17. Hasim, A., Ma, H., Mamtimin, B., et al. (2012). Revealing the metabonomic variation of EC using (1)H-NMR spectroscopy and its association with the clinicopathological characteristics. Molecular Biology Reports, 39, 8955–8964.CrossRefPubMedGoogle Scholar
  18. Ikeda, A., Nishiumi, S., Shinohara, M., et al. (2012). Serum metabolomics as a novel diagnostic approach for gastrointestinal cancer. Biomedical Chromatography, 26, 548–558.CrossRefPubMedGoogle Scholar
  19. Iorio, E., Mezzanzanica, D., Alberti, P., et al. (2005). Alterations of choline phospholipid metabolism in ovarian tumor progression. Cancer Research, 65, 9369–9376.CrossRefPubMedGoogle Scholar
  20. Jin, H., Qiao, F., Chen, L., Lu, C., Xu, L., & Gao, X. (2014). Serum metabolomic signatures of lymph node metastasis of esophageal squamous cell carcinoma. Journal of Proteome Research, 13, 4091–4103.CrossRefPubMedGoogle Scholar
  21. Ke, C., Hou, Y., Zhang, H., et al. (2015). Large-scale profiling of metabolic dysregulation in ovarian cancer. International Journal of Cancer, 136, 516–526.PubMedGoogle Scholar
  22. Kuhl, C., Tautenhahn, R., Bottcher, C., Larson, T. R., & Neumann, S. (2012). CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Analytical Chemistry, 84, 283–289.CrossRefPubMedGoogle Scholar
  23. Kumar, S., Huang, J., Cushnir, J. R., Spanel, P., Smith, D., & Hanna, G. B. (2012). Selected ion flow tube-MS analysis of headspace vapor from gastric content for the diagnosis of gastro-esophageal cancer. Analytical Chemistry, 84, 9550–9557.PubMedGoogle Scholar
  24. Lin, Y., Totsuka, Y., He, Y., et al. (2013). Epidemiology of esophageal cancer in Japan and China. J Epidemiol, 23, 233–242.CrossRefPubMedGoogle Scholar
  25. Liu, R., Peng, Y., Li, X., et al. (2013). Identification of plasma metabolomic profiling for diagnosis of esophageal squamous-cell carcinoma using an UPLC/TOF/MS platform. International Journal of Molecular Sciences, 14, 8899–8911.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Lynam-Lennon, N., Connaughton, R., Carr, E., et al. (2014). Excess visceral adiposity induces alterations in mitochondrial function and energy metabolism in esophageal adenocarcinoma. BMC Cancer, 14, 907.CrossRefPubMedPubMedCentralGoogle Scholar
  27. Ma, H., Hasim, A., Mamtimin, B., Kong, B., Zhang, H. P., & Sheyhidin, I. (2014). Plasma free amino acid profiling of esophageal cancer using high-performance liquid chromatography spectroscopy. World Journal of Gastroenterology, 20, 8653–8659.CrossRefPubMedPubMedCentralGoogle Scholar
  28. Mir, S. A., Rajagopalan, P., Jain, A. P., et al. (2015). LC–MS-based serum metabolomic analysis reveals dysregulation of phosphatidylcholines in esophageal squamous cell carcinoma. Journal of Proteomics, 127, 96–102.CrossRefPubMedGoogle Scholar
  29. Morimoto, M., Nishiyama, K., Nakamura, S., et al. (2010). Significance of endoscopic screening and endoscopic resection for esophageal cancer in patients with hypopharyngeal cancer. Japanese Journal of Clinical Oncology, 40, 938–943.CrossRefPubMedGoogle Scholar
  30. Pearl, D. C. (2002). Proteomic patterns in serum and identification of ovarian cancer. Lancet, 360, 169–170. author reply 170–1.CrossRefPubMedGoogle Scholar
  31. Pennathur, A., Gibson, M. K., Jobe, B. A., & Luketich, J. D. (2013). Oesophageal carcinoma. Lancet, 381, 400–412.CrossRefPubMedGoogle Scholar
  32. Rice, T. W., Rusch, V. W., Ishwaran, H., & Blackstone, E. H. (2010). Cancer of the esophagus and esophagogastric junction: data-driven staging for the seventh edition of the American Joint Committee on Cancer/International Union Against Cancer Cancer Staging Manuals. Cancer, 116, 3763–3773.CrossRefPubMedGoogle Scholar
  33. Roshandel, G., Nourouzi, A., Pourshams, A., Semnani, S., Merat, S., & Khoshnia, M. (2013). Endoscopic screening for esophageal squamous cell carcinoma. Arch Iran Med, 16, 351–357.PubMedGoogle Scholar
  34. Shen, Z., Wu, M., Elson, P., et al. (2001). Fatty acid composition of lysophosphatidic acid and lysophosphatidylinositol in plasma from patients with ovarian cancer and other gynecological diseases. Gynecologic Oncology, 83, 25–30.CrossRefPubMedGoogle Scholar
  35. Spratlin, J. L., Serkova, N. J., & Eckhardt, S. G. (2009). Clinical applications of metabolomics in oncology: a review. Clinical Cancer Research, 15, 431–440.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Steinberg, D., Parthasarathy, S., Carew, T. E., Khoo, J. C., & Witztum, J. L. (1989). Beyond cholesterol. Modifications of low-density lipoprotein that increase its atherogenicity. New England Journal of Medicine, 320, 915–924.CrossRefPubMedGoogle Scholar
  37. Vermeersch, K. A., & Styczynski, M. P. (2013). Applications of metabolomics in cancer research. Journal of Carcinogenesis, 12, 9.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Wang, L., Chen, J., Chen, L., et al. (2013). 1H-NMR based metabonomic profiling of human esophageal cancer tissue. Mol Cancer, 12, 25.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Wu, H., Xue, R., Lu, C., et al. (2009). Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry. Journal of Chromatography B Analytical Technologies Biomedical Life Sciences, 877, 3111–3117.CrossRefGoogle Scholar
  40. Xu, J., Chen, Y., Zhang, R., et al. (2013). Global and targeted metabolomics of esophageal squamous cell carcinoma discovers potential diagnostic and therapeutic biomarkers. Molecular and Cellular Proteomics, 12, 1306–1318.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 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.CrossRefPubMedGoogle Scholar
  42. Yang, Y., Wang, L., Wang, S., et al. (2013). Study of metabonomic profiles of human esophageal carcinoma by use of high-resolution magic-angle spinning 1H NMR spectroscopy and multivariate data analysis. Analytical and Bioanalytical Chemistry, 405, 3381–3389.CrossRefPubMedGoogle Scholar
  43. Yang, J., Wei, W. Q., Niu, J., Liu, Z. C., Yang, C. X., & Qiao, Y. L. (2012). Cost-benefit analysis of esophageal cancer endoscopic screening in high-risk areas of China. World Journal of Gastroenterology, 18, 2493–2501.CrossRefPubMedPubMedCentralGoogle Scholar
  44. Zhang, J., Bowers, J., Liu, L., et al. (2012a). Esophageal cancer metabolite biomarkers detected by LC–MS and NMR methods. PLoS ONE, 7, e30181.CrossRefPubMedPubMedCentralGoogle Scholar
  45. Zhang, H. Z., Jin, G. F., & Shen, H. B. (2012b). Epidemiologic differences in esophageal cancer between Asian and Western populations. Chinese Journal of Cancer, 31, 281–286.CrossRefPubMedPubMedCentralGoogle Scholar
  46. Zhang, J., Liu, L., Wei, S., et al. (2011). Metabolomics study of esophageal adenocarcinoma. The Journal Thoracic and Cardiovascular Surgery, 141, 469–475. 475.e1–4.CrossRefGoogle Scholar
  47. Zhang, T., Wu, X., Ke, C., et al. (2013a). Identification of potential biomarkers for ovarian cancer by urinary metabolomic profiling. Journal of Proteome Research, 12, 505–512.CrossRefPubMedGoogle Scholar
  48. Zhang, X., Xu, L., Shen, J., et al. (2013b). Metabolic signatures of esophageal cancer: NMR-based metabolomics and UHPLC-based focused metabolomics of blood serum. Biochimica et Biophysica Acta, 1832, 1207–1216.CrossRefPubMedGoogle Scholar
  49. Zhao, L., Wei, W. Q., Zhao, D. L., et al. (2012). Population-based study of DNA image cytometry as screening method for esophageal cancer. World Journal of Gastroenterology, 18, 375–382.CrossRefPubMedPubMedCentralGoogle Scholar
  50. Zock, P. L., & Katan, M. B. (1998). Linoleic acid intake and cancer risk: a review and meta-analysis. American Journal of Clinical Nutrition, 68, 142–153.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jialin Wang
    • 1
    • 4
    Email author
  • Tao Zhang
    • 2
  • Xiaotao Shen
    • 3
  • Jia Liu
    • 2
    • 5
  • Deli Zhao
    • 6
  • Yawen Sun
    • 1
    • 4
  • Lu Wang
    • 2
  • Yingjun Liu
    • 2
  • Xiaoyun Gong
    • 2
  • Yanxun Liu
    • 2
  • Zheng-Jiang Zhu
    • 3
    Email author
  • Fuzhong Xue
    • 2
    Email author
  1. 1.The Shandong Cancer Hospital Affiliated to Shandong UniversityJinanChina
  2. 2.Department of Epidemiology and Biostatistics, School of Public HealthShandong UniversityJinanChina
  3. 3.Interdisciplinary Research Center on Biology and Chemistry, and Shanghai Institute of Organic ChemistryChinese Academy of SciencesShanghaiChina
  4. 4.Shandong Cancer Hospital and InstituteShandong Academy of Medical SciencesJinanChina
  5. 5.Yanjing Medical CollegeCapital Medical UniversityBeijingChina
  6. 6.Tumor Preventative and Therapeutic Base of Shandong ProvinceFeicheng People’s HospitalFeichengChina

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