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Metabolomics

, 15:22 | Cite as

Metabolomics study of oral cancers

  • Xun Chen
  • Dongsheng YuEmail author
Review Article

Abstract

Background

Oral cancer is one of the most frequently occurring cancers. Metabolic reprogramming is an important hallmark of cancer. Metabolomics characterizes all the small molecules in a biological sample, and a complete set of small molecules in such sample is referred as metabolome. Nuclear magnetic resonance spectroscopy and mass spectrometry are two widely used techniques in metabolomics studies. Increasing evidence demonstrates that metabolomics techniques can be used to explore the metabolic signatures in oral cancer. Elucidation of metabolic alterations in oral cancer is also important for the understanding of its pathological mechanisms.

Aim of review

In this paper, we summarize the latest progress of metabolomics study in oral cancer and provide the suggestions for the future studies.

Key scientific concepts of review

The metabolomics studies in saliva, serum, and tumor tissues revealed the existence of metabolic signatures in bio-fluids and tissues of oral cancer, and several tumor-specific metabolites identified in individual study could discriminate oral cancer from healthy controls or precancerous lesions, which are potential biomarkers for the screening or early diagnosis of oral cancer. Metabolomics study of oral cancers in the future should aim to establish a routine procedure with high sensitivity, profile intracellular metabolites to find out the metabolic characteristics of tumor cells, and investigate the mechanism behind metabolomic alterations and the metabolic response of cancer cells to chemotherapy.

Keywords

Oral cancer Oral squamous cell carcinoma Metabolomics Metabolome Metabolites 

Notes

Acknowledgements

This study was funded by the National Natural Science Foundation of China (No. 81873711 and No. 31670788) and Open Fund of Guangdong Key Laboratory of Pharmaceutical Functional Genes (No.2014B030301028 and No.2017B030314021).

Author contributions

DY conceived and designed review. XC and DY wrote, read and approved the manuscript. XC drew figure.

Compliance with ethical standards

Conflict of interest

The authors confirm that there are no conflicts of interest.

References

  1. Asai, Y., et al. (2018). Elevated polyamines in saliva of pancreatic cancer. Cancers, 10, E43.  https://doi.org/10.3390/cancers10020043.CrossRefPubMedGoogle Scholar
  2. Backshall, A., Sharma, R., Clarke, S. J., & Keun, H. C. (2011). Pharmacometabonomic profiling as a predictor of toxicity in patients with inoperable colorectal cancer treated with capecitabine. Clinical Cancer Research, 17, 3019–3028.  https://doi.org/10.1158/1078-0432.CCR-10-2474.CrossRefPubMedGoogle Scholar
  3. Bag, S., et al. (2015). NMR ((1)H and (13)C) based signatures of abnormal choline metabolism in oral squamous cell carcinoma with no prominent Warburg effect. Biochemical and Biophysical Research Communications, 459, 574–578.  https://doi.org/10.1016/j.bbrc.2015.02.149.CrossRefPubMedGoogle Scholar
  4. Bag, S., et al. (2016). NanoLC MALDI MS/MS based quantitative metabolomics reveals the alteration of membrane biogenesis in oral cancer. Rsc Advances, 6, 62420–62433.  https://doi.org/10.1039/c6ra07001a.CrossRefGoogle Scholar
  5. Bezabeh, T., et al. (2005). Prediction of treatment response in head and neck cancer by magnetic resonance spectroscopy. AJNR American Journal of Neuroradiology, 26, 2108–2113.PubMedGoogle Scholar
  6. Cardoso, M. R., Santos, J. C., Ribeiro, M. L., Talarico, M. C. R., Viana, L. R., & Derchain, S. F. M. (2018). A metabolomic approach to predict breast cancer behavior and chemotherapy response. International Journal of Molecular Sciences, 19, E617.  https://doi.org/10.3390/ijms19020617.CrossRefPubMedGoogle Scholar
  7. Chen, H. W., Zhou, W., Liao, Y., Hu, S. C., Chen, T. L., & Song, Z. C. (2018). Analysis of metabolic profiles of generalized aggressive periodontitis. Journal of Periodontal Research, 53, 894–901.  https://doi.org/10.1111/jre.12579.CrossRefPubMedGoogle Scholar
  8. Chen, X., & Zhao, Y. (2017). Human papillomavirus infection in oral potentially malignant disorders and cancer. Archives of Oral Biology, 83, 334–339.  https://doi.org/10.1016/j.archoralbio.2017.08.011.CrossRefPubMedGoogle Scholar
  9. Cooke, M., Leeves, N., & White, C. (2003). Time profile of putrescine, cadaverine, indole and skatole in human saliva. Archives of Oral Biology, 48, 323–327.CrossRefGoogle Scholar
  10. El-Sayed, S., et al. (2002). An ex vivo study exploring the diagnostic potential of 1H magnetic resonance spectroscopy in squamous cell carcinoma of the head and neck region. Head and Neck, 24, 766–772.  https://doi.org/10.1002/hed.10125.CrossRefPubMedGoogle Scholar
  11. Gerner, E. W., & Meyskens, F. L. Jr. (2004). Polyamines and cancer: old molecules, new understanding. Nature Reviews Cancer, 4, 781–792.  https://doi.org/10.1038/nrc1454.CrossRefPubMedGoogle Scholar
  12. Glunde, K., Bhujwalla, Z. M., & Ronen, S. M. (2011). Choline metabolism in malignant transformation. Nature Reviews Cancer, 11, 835–848.  https://doi.org/10.1038/nrc3162.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Goldberg, S., Kozlovsky, A., Gordon, D., Gelernter, I., Sintov, A., & Rosenberg, M. (1994). Cadaverine as a putative component of oral malodor. Journal of Dental Research, 73, 1168–1172.  https://doi.org/10.1177/00220345940730060701.CrossRefPubMedGoogle Scholar
  14. Gupta, A., Gupta, S., & Mahdi, A. A. (2015). (1)H NMR-derived serum metabolomics of leukoplakia and squamous cell carcinoma. Clinica Chimica Acta, 441, 47–55.  https://doi.org/10.1016/j.cca.2014.12.003.CrossRefGoogle Scholar
  15. Hirayama, A., et al. (2015). Effects of processing and storage conditions on charged metabolomic profiles in blood. Electrophoresis, 36, 2148–2155.  https://doi.org/10.1002/elps.201400600.CrossRefPubMedGoogle Scholar
  16. Ishikawa, S., et al. (2016). Identification of salivary metabolomic biomarkers for oral cancer screening. Scientific Reports, 6, 31520.  https://doi.org/10.1038/srep31520.CrossRefPubMedPubMedCentralGoogle Scholar
  17. Ishikawa, S., et al. (2017). Effect of timing of collection of salivary metabolomic biomarkers on oral cancer detection. Amino Acids, 49, 761–770.  https://doi.org/10.1007/s00726-017-2378-5.CrossRefPubMedGoogle Scholar
  18. Ji, E. H., et al. (2017). Metabolomic analysis of human oral cancer cells with adenylate kinase 2 or phosphorylate glycerol kinase 1 inhibition. Journal of Cancer, 8, 298–304.  https://doi.org/10.7150/jca.17521.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Kawanishi, N., et al. (2018). Effects of inter-day and intra-day variation on salivary metabolomic profiles. Clinica Chimica Acta, 489, 41–48.  https://doi.org/10.1016/j.cca.2018.11.030.CrossRefGoogle Scholar
  20. Kong, X., et al. (2015). Analysis of plasma metabolic biomarkers in the development of 4-nitroquinoline-1-oxide-induced oral carcinogenesis in rats. Oncology Letters, 9, 283–289.  https://doi.org/10.3892/ol.2014.2619.CrossRefPubMedGoogle Scholar
  21. Kuboniwa, M., Sakanaka, A., Hashino, E., Bamba, T., Fukusaki, E., & Amano, A. (2016). Prediction of periodontal inflammation via metabolic profiling of saliva. Journal of Dental Research, 95, 1381–1386.  https://doi.org/10.1177/0022034516661142.CrossRefPubMedGoogle Scholar
  22. Lau, C., et al. (2013). Role of pancreatic cancer-derived exosomes in salivary biomarker development. Journal of Biological Chemistry, 288, 26888–26897.  https://doi.org/10.1074/jbc.M113.452458.CrossRefPubMedGoogle Scholar
  23. Liu, R., 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.  https://doi.org/10.3390/ijms14058899.CrossRefPubMedPubMedCentralGoogle Scholar
  24. Markley, J. L., et al. (2017). The future of NMR-based metabolomics. Current Opinion in Biotechnology, 43, 34–40.  https://doi.org/10.1016/j.copbio.2016.08.001.CrossRefPubMedGoogle Scholar
  25. Mikkonen, J. J., Singh, S. P., Herrala, M., Lappalainen, R., Myllymaa, S., & Kullaa, A. M. (2016). Salivary metabolomics in the diagnosis of oral cancer and periodontal diseases. Journal of Periodontal Research, 51, 431–437.  https://doi.org/10.1111/jre.12327.CrossRefPubMedGoogle Scholar
  26. Mukherjee, P. K., et al. (2017). Metabolomic analysis identifies differentially produced oral metabolites, including the oncometabolite 2-hydroxyglutarate, in patients with head and neck squamous cell carcinoma. BBA Clinical, 7, 8–15.  https://doi.org/10.1016/j.bbacli.2016.12.001.CrossRefPubMedGoogle Scholar
  27. Mukherji, S. K., Schiro, S., Castillo, M., Kwock, L., Muller, K. E., & Blackstock, W. (1997). Proton MR spectroscopy of squamous cell carcinoma of the extracranial head and neck: in vitro and in vivo studies. AJNR American Journal of Neuroradiology, 18, 1057–1072.PubMedGoogle Scholar
  28. Musharraf, S. G., Shahid, N., Naqvi, S. M., Saleem, M., Siddiqui, A. J., & Ali, A. (2016). Metabolite profiling of preneoplastic and neoplastic lesions of oral cavity tissue samples revealed a biomarker pattern. Science Reports, 6, 38985.  https://doi.org/10.1038/srep38985.CrossRefGoogle Scholar
  29. Nakagawa, H., Hayata, Y., Kawamura, S., Yamada, T., Fujiwara, N., & Koike, K. (2018). Lipid metabolic reprogramming in hepatocellular carcinoma. Cancers, 10, E447.  https://doi.org/10.3390/cancers10110447.CrossRefPubMedGoogle Scholar
  30. Nicholson, J. K., & Lindon, J. C. (2008). Systems biology: metabonomics. Nature, 455, 1054–1056.  https://doi.org/10.1038/4551054a.CrossRefPubMedGoogle Scholar
  31. Ogawa, T., Washio, J., Takahashi, T., Echigo, S., & Takahashi, N. (2014). Glucose and glutamine metabolism in oral squamous cell carcinoma: insight from a quantitative metabolomic approach. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, 118, 218–225.  https://doi.org/10.1016/j.oooo.2014.04.003.CrossRefGoogle Scholar
  32. Ohshima, M., Sugahara, K., Kasahara, K., & Katakura, A. (2017). Metabolomic analysis of the saliva of Japanese patients with oral squamous cell carcinoma. Oncology Reports, 37, 2727–2734.  https://doi.org/10.3892/or.2017.5561.CrossRefPubMedGoogle Scholar
  33. Okamura, M., Kobayashi, M., Suzuki, F., Shimada, J., & Sakagami, H. (2007). Induction of cell death by combination treatment with cisplatin and 5-fluorouracil in a human oral squamous cell carcinoma cell line. Anticancer Research, 27, 3331–3337.PubMedGoogle Scholar
  34. Rai, V., Mukherjee, R., Ghosh, A. K., Routray, A., & Chakraborty, C. (2018). “Omics” in oral cancer: new approaches for biomarker discovery. Archives of Oral Biology, 87, 15–34.  https://doi.org/10.1016/j.archoralbio.2017.12.003.CrossRefPubMedGoogle Scholar
  35. Romano, F., et al. (2018). Analysis of salivary phenotypes of generalized aggressive and chronic periodontitis through nuclear magnetic resonance-based metabolomics. Journal of Periodontology, 89, 1452–1460.  https://doi.org/10.1002/Jper.18-0097.CrossRefPubMedGoogle Scholar
  36. Roodhart, J. M., et al. (2011). Mesenchymal stem cells induce resistance to chemotherapy through the release of platinum-induced fatty acids. Cancer Cell, 20, 370–383.  https://doi.org/10.1016/j.ccr.2011.08.010.CrossRefPubMedGoogle Scholar
  37. Sakanaka, A., Kuboniwa, M., Hashino, E., Bamba, T., Fukusaki, E., & Amano, A. (2017). Distinct signatures of dental plaque metabolic byproducts dictated by periodontal inflammatory status. Science Reports, 7, 42818.  https://doi.org/10.1038/srep42818.CrossRefGoogle Scholar
  38. Sant’Anna-Silva, A. C. B., Santos, G. C., Campos, S. P. C., Oliveira Gomes, A. M., Perez-Valencia, J. A., & Rumjanek, F. D. (2018). Metabolic profile of oral squamous carcinoma cell lines relies on a higher demand of lipid metabolism in metastatic cells. Frontiers Oncology, 8, 13.  https://doi.org/10.3389/fonc.2018.00013.CrossRefGoogle Scholar
  39. Shankar, A. A., Alex, S., & Routray, S. (2014). Incorporation of salivary metabolomics in oral cancer diagnostics. Oral Oncology, 50, e53–e54.  https://doi.org/10.1016/j.oraloncology.2014.07.013.CrossRefPubMedGoogle Scholar
  40. Shin, J. M., Kamarajan, P., Fenno, J. C., Rickard, A. H., & Kapila, Y. L. (2016). Metabolomics of head and neck cancer: a mini-review. Frontiers in Physiology, 7, 526.  https://doi.org/10.3389/fphys.2016.00526.CrossRefPubMedPubMedCentralGoogle Scholar
  41. Somashekar, B. S., et al. (2011). Magic angle spinning NMR-based metabolic profiling of head and neck squamous cell carcinoma tissues. Journal of Proteome Research, 10, 5232–5241.  https://doi.org/10.1021/pr200800w.CrossRefPubMedPubMedCentralGoogle Scholar
  42. Srivastava, S., Roy, R., Gupta, V., Tiwari, A., Srivastava, A. N., & Sonkar, A. (2011). Proton HR-MAS MR spectroscopy of oral squamous cell carcinoma tissues: an ex vivo study to identify malignancy induced metabolic fingerprints. Metabolomics, 7, 278–288.  https://doi.org/10.1007/s11306-010-0253-4.CrossRefGoogle Scholar
  43. Star-Lack, J. M., et al. (2000). In vivo 1H MR spectroscopy of human head and neck lymph node metastasis and comparison with oxygen tension measurements. AJNR American Journal of Neuroradiology, 21, 183–193.PubMedGoogle Scholar
  44. Sugimoto, M., Wong, D. T., Hirayama, A., Soga, T., & Tomita, M. (2010). Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics, 6, 78–95.  https://doi.org/10.1007/s11306-009-0178-y.CrossRefPubMedGoogle Scholar
  45. Sun, L. C., Suo, C. X., Li, S. T., Zhang, H. F., & Gao, P. (2018). Metabolic reprogramming for cancer cells and their microenvironment: beyond the Warburg effect. Biochimica et Biophysica Acta, 1870, 51–66.  https://doi.org/10.1016/j.bbcan.2018.06.005.CrossRefPubMedGoogle Scholar
  46. Suzuki, R., Matsuno, S., Sakagami, H., Okada, Y., & Shirataki, Y. (2014). Search of new cytotoxic crude materials against human oral squamous cell carcinoma using 1H NMR-based metabolomics. Anticancer Research, 34, 4117–4120.PubMedGoogle Scholar
  47. Takayama, T., et al. (2016). Diagnostic approach to breast cancer patients based on target metabolomics in saliva by liquid chromatography with tandem mass spectrometry. Clinica Chimica Acta, 452, 18–26.  https://doi.org/10.1016/j.cca.2015.10.032.CrossRefGoogle Scholar
  48. Takeda, I., et al. (2009). Understanding the human salivary metabolome. NMR in Biomedicine, 22, 577–584.  https://doi.org/10.1002/nbm.1369.CrossRefPubMedGoogle Scholar
  49. Tan, Y., et al. (2012). Metabolomics study of stepwise hepatocarcinogenesis from the model rats to patients: potential biomarkers effective for small hepatocellular carcinoma diagnosis. Molecular and Cell Proteomics, 11, M111 010694.  https://doi.org/10.1074/mcp.M111.010694.CrossRefGoogle Scholar
  50. Tanaka, S., Machino, M., Akita, S., Yokote, Y., & Sakagami, H. (2010). Changes in salivary amino acid composition during aging. In Vivo, 24, 853–856.PubMedGoogle Scholar
  51. Tiziani, S., Lopes, V., & Gunther, U. L. (2009). Early stage diagnosis of oral cancer using 1H NMR-based metabolomics. Neoplasia, 11, 269–276, 4p following 269.CrossRefGoogle Scholar
  52. Tomita, A., et al. (2018). Effect of storage conditions on salivary polyamines quantified via liquid chromatography-mass spectrometry. Science Reports, 8, 12075.  https://doi.org/10.1038/s41598-018-30482-x.CrossRefGoogle Scholar
  53. Tripathi, P., et al. (2012). Delineating metabolic signatures of head and neck squamous cell carcinoma: phospholipase A2, a potential therapeutic target. International Journal of Biochemistry and Cell Biology, 44, 1852–1861.  https://doi.org/10.1016/j.biocel.2012.06.025.CrossRefPubMedGoogle Scholar
  54. Urakami, K., Zangiacomi, V., Yamaguchi, K., & Kusuhara, M. (2013). Impact of 2-deoxy-D-glucose on the target metabolome profile of a human endometrial cancer cell line. Biomedical Research, 34, 221–229.CrossRefGoogle Scholar
  55. Wang, H., et al. (2015). (1)H nuclear magnetic resonance-based extracellular metabolomic analysis of multidrug resistant Tca8113 oral squamous carcinoma cells. Oncology Letters, 9, 2551–2559.  https://doi.org/10.3892/ol.2015.3128.CrossRefPubMedPubMedCentralGoogle Scholar
  56. Wang, J., et al. (2014a). Metabolomic profiling of anionic metabolites in head and neck cancer cells by capillary ion chromatography with Orbitrap mass spectrometry. Analytical Chemistry, 86, 5116–5124.  https://doi.org/10.1021/ac500951v.CrossRefPubMedGoogle Scholar
  57. Wang, Q., Gao, P., Wang, X., & Duan, Y. (2014b). The early diagnosis and monitoring of squamous cell carcinoma via saliva metabolomics. Science Reports, 4, 6802.  https://doi.org/10.1038/srep06802.CrossRefGoogle Scholar
  58. Wang, Q., Gao, P., Wang, X., & Duan, Y. (2014c). Investigation and identification of potential biomarkers in human saliva for the early diagnosis of oral squamous cell carcinoma. Clinica Chimica Acta, 427, 79–85.  https://doi.org/10.1016/j.cca.2013.10.004.CrossRefGoogle Scholar
  59. Wang, X., et al. (2018). Taurine, glutamic acid and ethylmalonic acid as important metabolites for detecting human breast cancer based on the targeted metabolomics. Cancer Biomarkers, 23, 255–268.  https://doi.org/10.3233/CBM-181500.CrossRefPubMedGoogle Scholar
  60. Wang, X., Kaczor-Urbanowicz, K. E., & Wong, D. T. (2017). Salivary biomarkers in cancer detection. Medical Oncology, 34, 7.  https://doi.org/10.1007/s12032-016-0863-4.CrossRefPubMedGoogle Scholar
  61. Warburg, O. (1956). On the origin of cancer cells. Science, 123, 309–314.CrossRefGoogle Scholar
  62. Weaver, Z., et al. (2012). Temporal molecular and biological assessment of an erlotinib-resistant lung adenocarcinoma model reveals markers of tumor progression and treatment response. Cancer Research, 72, 5921–5933.  https://doi.org/10.1158/0008-5472.CAN-12-0736.CrossRefPubMedPubMedCentralGoogle Scholar
  63. Wei, J., et al. (2011). Salivary metabolite signatures of oral cancer and leukoplakia. International Journal of Cancer, 129, 2207–2217.  https://doi.org/10.1002/ijc.25881.CrossRefPubMedGoogle Scholar
  64. Xie, G. X., et al. (2012). Urine metabolite profiling offers potential early diagnosis of oral cancer. Metabolomics, 8, 220–231.  https://doi.org/10.1007/s11306-011-0302-7.CrossRefGoogle Scholar
  65. Yakob, M., Fuentes, L., Wang, M. B., Abemayor, E., & Wong, D. T. (2014). Salivary biomarkers for detection of oral squamous cell carcinoma - current state and recent advances. Current Oral Health Reports, 1, 133–141.  https://doi.org/10.1007/s40496-014-0014-y.CrossRefPubMedPubMedCentralGoogle Scholar
  66. Yan, S. K., Wei, B. J., Lin, Z. Y., Yang, Y., Zhou, Z. T., & Zhang, W. D. (2008). A metabonomic approach to the diagnosis of oral squamous cell carcinoma, oral lichen planus and oral leukoplakia. Oral Oncology, 44, 477–483.  https://doi.org/10.1016/j.oraloncology.2007.06.007.CrossRefPubMedGoogle Scholar
  67. Yang, L. F., Venneti, S., & Nagrath, D. (2017). Glutaminolysis: A hallmark of cancer metabolism. Annual Review of Biomedical Engineering, 19, 163–194.  https://doi.org/10.1146/annurev-bioeng-071516044546.CrossRefPubMedGoogle Scholar
  68. Ye, G., et al. (2012). Analysis of urinary metabolic signatures of early hepatocellular carcinoma recurrence after surgical removal using gas chromatography-mass spectrometry. Journal of Proteome Research, 11, 4361–4372.  https://doi.org/10.1021/pr300502v.CrossRefPubMedGoogle Scholar
  69. Ye, G., et al. (2014). Study of induction chemotherapy efficacy in oral squamous cell carcinoma using pseudotargeted metabolomics. Journal of Proteome Research, 13, 1994–2004.  https://doi.org/10.1021/pr4011298.CrossRefPubMedGoogle Scholar
  70. Yonezawa, K., et al. (2013). Serum and tissue metabolomics of head and neck cancer. Cancer Genomics & Proteomics, 10, 233–238.Google Scholar
  71. Yu, L., Chen, X., Sun, X., Wang, L., & Chen, S. (2017). The glycolytic switch in tumors: How many players are involved? Journal of Cancer, 8, 3430–3440.  https://doi.org/10.7150/jca.21125.CrossRefPubMedPubMedCentralGoogle Scholar
  72. Yuvaraj, M., et al. (2014). Fluorescence spectroscopic characterization of salivary metabolites of oral cancer patients. Journal of Photochemistry and Photobiology B, 130, 153–160.  https://doi.org/10.1016/j.jphotobiol.2013.11.006.CrossRefGoogle Scholar
  73. Zaal, E. A., Wu, W., Jansen, G., Zweegman, S., Cloos, J., & Berkers, C. R. (2017). Bortezomib resistance in multiple myeloma is associated with increased serine synthesis. Cancer & Metabolism, 5, 7.  https://doi.org/10.1186/s40170-017-0169-9.CrossRefGoogle Scholar
  74. Zhang, R. X., Zhuang, X. Y., Zong, L., Liu, S., Liu, Z. Q., & Song, F. R. (2016). Investigations on the cell metabolomics basis of multidrug resistance from tumor cells by ultra-performance liquid chromatography-mass spectrometry. Analytical and Bioanalytical Chemistry, 408, 5843–5854.  https://doi.org/10.1007/s00216-016-9696-4.CrossRefPubMedGoogle Scholar
  75. Zhong, L. P., Cheng, F., Lu, X. Y., Duan, Y. X., & Wang, X. D. (2016). Untargeted saliva metabonomics study of breast cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations. Talanta, 158, 351–360.  https://doi.org/10.1016/j.talanta.2016.04.049.CrossRefPubMedGoogle Scholar
  76. Zhou, J., et al. (2009). 1H NMR-based metabonomic and pattern recognition analysis for detection of oral squamous cell carcinoma. Clinica Chimica Acta, 401, 8–13.  https://doi.org/10.1016/j.cca.2008.10.030.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Guanghua School of Stomatology, Guangdong Provincial Key Laboratory of StomatologySun Yat-sen UniversityGuangzhouPeople’s Republic of China
  2. 2.Department of Oral and Maxillofacial Surgery, Guanghua School of StomatologySun Yat-sen UniversityGuangzhouPeople’s Republic of China

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