Serum-based metabolic alterations in patients with papillary thyroid carcinoma unveiled by non-targeted 1H-NMR metabolomics approach

Document Type: Original Article


1 Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

2 Department of Basic Sciences, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

3 3 Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran

4 Department of Surgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran

5 Department of Basic Sciences, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran, Iran


Objective(s): As the most prevalent endocrine system malignancy, papillary thyroid carcinoma had a very fast rising incidence in recent years for unknown reasons besides the fact that the current methods in thyroid cancer diagnosis still hold some limitations. Therefore, the aim of this study was to improve the potential molecular markers for diagnosis of benign and malignant thyroid nodules to prevent unnecessary surgeries for benign tumors.
Materials and Methods: In this study, 1H-NMR metabolomics platform was used to seek the discriminating serum metabolites in malignant papillary thyroid carcinoma (PTC) compared to benign multinodular goiter (MNG) and healthy subjects and also to better understand the disease mechanisms using bioinformatics analysis. Multivariate statistical analysis showed that PTC and MNG samples could be successfully discriminated in PCA and OPLS-DA score plots.
Results: Significant metabolites that differentiated malignant and benign thyroid lesions included citrate, acetylcarnitine, glutamine, homoserine, glutathione, kynurenine, nicotinic acid, hippurate, tyrosine, tryptophan, β-alanine, and xanthine. The significant metabolites in the PTC group compared to healthy subjects also included scyllo- and myo-inositol, tryptophan, propionate, lactate, homocysteine, 3-methyl glutaric acid, asparagine, aspartate, choline, and acetamide. The metabolite sets enrichment analysis demonstrated that aspartate metabolism and urea cycle were the most important pathways in papillary thyroid cancer progression.
Conclusion: The study results demonstrated that serum metabolic fingerprinting could serve as a viable method for differentiating various thyroid lesions and for proposing novel potential markers for thyroid cancers. Obviously, further studies are needed for the validation of the results.


Main Subjects

1. Jia-XiangW, Jie-kai Y, Li W, Qiu-Liang L, Jiao Z, Shu Z. Application of serum protein fingerprint in diagnosis of papillary thyroid carcinoma. Proteomics 2006; 6:5344-5349.
2. Tian Y, Nie X, Xu S, Li Y, Huang T, Tang H, et al. Integrative metabonomics as potential method for diagnosis of thyroid malignancy. Sci Rep 2015; 14869:1-12.
3. Wojakowska A, Chekan M, Widlak P, Pietrowska M. Application of metabolomics in thyroid cancer research. Int J Endocrinol 2015; 2015:1-13.
4. Lupoli G, Fonderico F, Colarusso S, Panico A, Cavallo A, Micco LD, et al. Current management of differentiated thyroid carcinoma. Med Sci Monit 2005; 11:368-373.
5. Castro MR, Gharib H. Thyroid fine-needle aspiration biopsy: Progress, practice, and pitfalls. Endocr pract 2003; 9:128–136.
6. Ringel MD. Molecular diagnostic tests in the diagnosis and management of thyroid carcinoma. Rev Endocr Metab Disord 2000; 1:173–181.
7. Fischer S, Asa SL. Application of immunohistochemistry to thyroid neoplasms. Arch Pathol Lab Med 2008; 132:359-372.
8. Farrokhi Yekta R, Rezaie Tavirani M, Arefi Oskouie A, Mohajeri-Tehrani MR, Soroush AR. The metabolomics and lipidomics window into thyroid cancer research. Biomarkers 2017; 22:595-603.
9. Nobakht MGBF, Aliannejad R, Rezaei-Tavirani M, Taheri S, Oskouie AA. The metabolomics of airway diseases, including COPD, asthma and cystic fibrosis. Biomarkers 2015; 20:5-16.
10. Safaei A, Arefi Oskouie A, Mohebbi SR, Rezaei-Tavirani M, Mahboubi M, Peyvandi M, et al. Metabolomic analysis of human cirrhosis, hepatocellular carcinoma, non-alcoholic fatty liver disease and non-alcoholic steatohepatitis diseases. Gastroenterol Hepatol Bed Bench 2016; 9:158-173.
11. Ulrich E, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin Y, et al. BioMagResBank. Nucl Acids Res 2008; 36:D402-D408.
12. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, et al. HMDB 3.0--The human metabolome database in 2013. Nucleic Acids Res 2013; 41:D801-807.
13. Xia J, Sinelnikov IV, Han B, Wishart DS. MetaboAnalyst 3.0--making metabolomics more meaningful. Nucleic Acids Res 2015; 43:W251-257.
14. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13:2498-2504.
15. Karnovsky A, Weymouth T, Hull T, Tarcea VG, Scardoni G, Laudanna C, et al. Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 2012; 28:373-380.
16. Hu ZZ, Huang H, Wu CH, Jung M, Dritschilo A, Riegel AT, et al. Omics-based molecular target and biomarker identification. Methods Mol Biol 2011; 719:547-571.
17. Sugimoto M, Wong DT, Hirayama A, Soga T, Tomita M. Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics 2010; 6:78-95.
18. Liu SY, Zhang RL, Kang H, Fan ZJ, Du Z. Human liver tissue metabolic profiling research on hepatitis B virus-related hepatocellular carcinoma. World J Gastroenterol 2013; 19:3423-3432.
19. Yao Z, Yin P, Su D, Peng Z, Zhou L, Ma L, et al. Serum metabolic profiling and features of papillary thyroid carcinoma and nodular goiter. Mol BioSyst 2011; 7:2608–2614.
20. Otto AM. Warburg effect(s)—a biographical sketch of Otto Warburg and his impacts on tumor metabolism. Cancer & Metabolism 2016; 4:1-8.
21. Kubota A, Meguid MM, Hitch DC. Amino acid profiles correlate diagnostically with organ site in three kinds of malignant tumors. Cancer 1992; 69:2343-2348.
22. Nagamani SCS, Erez A. A metabolic link between the urea cycle and cancer cell proliferation. Mol Cell Oncol 2016; 3:e1127314.
23. Gu Y, Chen T, Fu S, Sun X, Wang L, Wang J, et al. Perioperative dynamics and significance of amino acid profiles in patients with cancer. J Transl Med 2015; 13:1-14.
24. Xu Y, Zheng X, Qiu Y, Jia W, Wang J, Yin S. Distinct metabolomic profiles of papillary thyroid carcinoma and benign thyroid adenoma. J Proteome Res 2015; 14:3315-3321.
25. Deja S, Dawiskiba T, Balcerzak W, Orczyk-Pawiłowicz M, Głód M, Pawełka D, et al. Follicular adenomas exhibit a unique metabolic profile. 1H NMR studies of thyroid lesions. PLoS ONE 2013; 8:e84637.
26. Torregrossa L, Shintu L, Chandran JN, Tintaru A, Ugolini C, Magalhães A, et al. Toward the Reliable Diagnosis of Indeterminate Thyroid Lesions: A HRMAS NMR-Based Metabolomics Case of Study. J Proteome Res 2012; 11:3317-3325.
27. Miccoli P, Torregrossa L, Shintu L, Magalhaes A, Chandran JN, Tintaru A, et al. Metabolomics approach to thyroid nodules: A high-resolution magic-angle spinning nuclear magnetic resonance–based study. Surgery 2012; 152:1118-1124.
28. Rodrigues D, Monteiro M, Jerónimo C, Henrique R, Belo L, Bastos ML, et al. Renal cell carcinoma: a critical analysis of metabolomic biomarkers emerging from current model systems. Transl Res 2016; 180:1-11.
29. Giskeødegård GF, Hansen AF, Bertilsson H, Gonzalez SV, Kristiansen KA, Bruheim P, et al. Metabolic markers in blood can separate prostate cancer from benign prostatic hyperplasia. Br J Cancer 2015; 113:1712-1719.
30. Russell P, Lean CL, Delbridge L, May GL, Dowd S, Mountford CE. Proton magnetic resonance and human thyroid neoplasia I: Discrimination between benign and malignant neoplasms. Am J Med 1994; 96:383-388.
31. Ferruzzi E, Franceschinia R, Cazzolatob G, Geronic C, Fowstc C, Pastorinod U, et al. Blood glutathione as a surrogate marker of cancer tissue glutathione S-transferase activity in non-small cell lung cancer and squamous cell carcinoma of the head and neck. Eur J Cancer 2003; 39:1019-1029.
32. Iyamu EW. The redox state of the glutathione/glutathione disulfide couple mediates intracellular arginase activation in HCT-116 colon cancer cells. Dig Dis Sci 2010; 55:2520-2528.
33. Eng CH, Yu K, Lucas J, White E, Abraham RT. Ammonia derived from glutaminolysis is a diffusible regulator of autophagy. Sci Signal 2010; 3:ra31.
34. Meng M, Chen S, Lao T, Liang D, Sang N. Nitrogen anabolism underlies the importance of glutaminolysis in proliferating cells. Cell Cycle 2010; 9:3921–3932.
35. Dang CV. Glutaminolysis: supplying carbon or nitrogen or both for cancer cells?. Cell Cycle 2010; 9:3884-3886.