journal article Open Access Dec 13, 2016

Metabolite Profiling of Preneoplastic and Neoplastic Lesions of Oral Cavity Tissue Samples Revealed a Biomarker Pattern

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Abstract
AbstractOral cancer is a major health challenge in the Indian subcontinent and a dreadful form of cancers worldwide. The current study is focused on the identification of distinguished metabolites of oral cancer tissue samples in comparison with precancerous and control tissue samples using gas chromatography coupled with triple quadrupole tandem mass spectrometry and chemometric analyses. Metabolites obtained were identified through National Institute of Standards and Technology (NIST) mass spectral (Wiley registry) library. Mass Profiler Professional (MPP) software was used for the alignment and for all the statistical analysis. 31 compounds out of 735 found distinguishing among oral cancer, precancerous and control group samples using p-value ≤ 0.05. Partial Least Square Discriminant Analysis (PLSDA) model was generated using statistically significant metabolites gave an overall accuracy of 90.2%. Down-regulated amino acid levels appear to be the result of enhanced energy metabolism or up-regulation of the appropriate biosynthetic pathways, and required cell proliferation in cancer tissues. These results suggest that tissue metabolic profiles have great potential in detecting oral cancer and may aid in understanding its underlying mechanisms.
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References
30
[1]
Rao, S. K., Mejia, G., Roberts-Thomson, K. & Logan, R. Epidemiology of oral cancer in Asia in the past decade-an update (2000–2012). Asian Pac J Cancer Prev 14, 5567–5577 (2013). 10.7314/apjcp.2013.14.10.5567
[2]
Organization, W. H. International classification of diseases for oncology (ICD-O)–3rd edition, 1st revision (2013).
[3]
Jemal, A., Siegel, R., Xu, J. & Ward, E. Cancer Statistics, 2010. CA: A Cancer Journal for Clinicians 60, 277–300 (2010).
[4]
Wei, J. et al. Salivary metabolite signatures of oral cancer and leukoplakia. International Journal of Cancer 129, 2207–2217 (2011). 10.1002/ijc.25881
[5]
Alamgir, M., Jamal, Q., Jafarey, N. & Mirza, T. Clinicopathological parameters of 50 oral squamous cell carcinoma cases in Karachi. Pakistan Journal of Medicine and Dentistry 2, 3–8 (2013).
[6]
Gupta, M., Mishra, P., Shrivastava, K., Singh, N. & Singh, P. Oral Submucous Fibrosis-Current Concepts of Aetiology & its Management. Journal Of Applied Dental and Medical Sciences 1, 1 (2015).
[7]
More, C. B., Gupta, S., Joshi, J. & Varma, S. N. Classification system for oral submucous fibrosis. J Indian Acad Oral Med Radiol 24, 24–29 (2012). 10.5005/jp-journals-10011-1254
[8]
Lingen, M. W., Kalmar, J. R., Karrison, T. & Speight, P. M. Critical Evaluation of Diagnostic Aids for the Detection of Oral Cancer. Oral oncology 44, 10–22 (2008). 10.1016/j.oraloncology.2007.06.011
[9]
Messadi, D. V. Diagnostic aids for detection of oral precancerous conditions. International journal of oral science 5, 59–65 (2013). 10.1038/ijos.2013.24
[10]
Masthan, K., Babu, N. A., Dash, K. C. & Elumalai, M. Advanced diagnostic aids in oral cancer. Asian Pacific Journal of Cancer Prevention 13, 3573–3576 (2012). 10.7314/apjcp.2012.13.8.3573
[11]
Shubhalakshmi, Marol, A., Ravishankar, B. & Krishnamoorthy, A. Biomarkers - A novel tool in oral cancer prevention and cure. e-Journal of Dentistry 4, 282–287 (2012).
[12]
Yonezawa, K. et al. Serum and tissue metabolomics of head and neck cancer. Cancer Genomics-Proteomics 10, 233–238 (2013).
[13]
Lindon, J. C., Nicholson, J. K. & Holmes, E. Metabonomics and metabolomics techniques and their applications in mammalian systems. In The Handbook of Metabonomics and Metabolomics (eds Lindon, J. C., Nicholson, J. K. & Holmes, E. ) (Elsevier Science, London, UK, 2006). 10.1016/b978-044452841-4/50002-3
[14]
Kelly, A. D. et al. Metabolomic Profiling from Formalin-Fixed, Paraffin-Embedded Tumor Tissue Using Targeted LC/MS/MS: Application in Sarcoma. PLoS ONE 6, e25357 (2011). 10.1371/journal.pone.0025357
[15]
van Berlo, R. J. et al. Predicting metabolic fluxes using gene expression differences as constraints. IEEE/ACM transactions on computational biology and bioinformatics/IEEE, ACM 8, 206–216 (2011). 10.1109/tcbb.2009.55
[16]
Ward, P. S. & Thompson, C. B. Metabolic reprogramming: a cancer hallmark even warburg did not anticipate. Cancer cell 21, 297–308 (2012). 10.1016/j.ccr.2012.02.014
[17]
Hallmarks of Cancer: The Next Generation

Douglas Hanahan, Robert A. Weinberg

Cell 2011 10.1016/j.cell.2011.02.013
[18]
Leichtle, A. B. et al. Serum amino acid profiles and their alterations in colorectal cancer. Metabolomics 8, 643–653 (2012). 10.1007/s11306-011-0357-5
[19]
Qiu, Y. et al. Serum metabolite profiling of human colorectal cancer using GC-TOFMS and UPLC-QTOFMS. Journal of proteome research 8, 4844–4850 (2009). 10.1021/pr9004162
[20]
Sugimoto, M., Wong, D. T., Hirayama, A., Soga, T. & Tomita, M. Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics 6, 78–95 (2010). 10.1007/s11306-009-0178-y
[21]
Goel, R. et al. Amino Acid Profile in Oral Submucous Fibrosis: A High Performance Liquid Chromatography (HPLC) Study. Journal of Clinical and Diagnostic Research: JCDR 8, ZC44–ZC48 (2014).
[22]
Somashekar, B. S. et al. Magic angle spinning NMR-based metabolic profiling of head and neck squamous cell carcinoma tissues. Journal of proteome research 10, 5232–5241 (2011). 10.1021/pr200800w
[23]
Duckwall, C. S., Murphy, T. A. & Young, J. D. Mapping cancer cell metabolism with 13 C flux analysis: Recent progress and future challenges. Journal of carcinogenesis 12, 13 (2013). 10.4103/1477-3163.115422
[24]
Ananieva, E. Targeting amino acid metabolism in cancer growth and anti-tumor immune response. World journal of biological chemistry 6, 281 (2015). 10.4331/wjbc.v6.i4.281
[25]
Stover, P. J. & MacFarlane, A. J. Mouse models to elucidate mechanisms of folate-related cancer pathologies. Nutrition reviews 66, S54–S58 (2008). 10.1111/j.1753-4887.2008.00069.x
[26]
Rahman, M. & Hasan, M. R. Cancer metabolism and drug resistance. Metabolites 5, 571–600 (2015). 10.3390/metabo5040571
[27]
Wise, D. R. & Thompson, C. B. Glutamine addiction: a new therapeutic target in cancer. Trends in biochemical sciences 35, 427–433 (2010). 10.1016/j.tibs.2010.05.003
[28]
DeBerardinis, R. J., Sayed, N., Ditsworth, D. & Thompson, C. B. Brick by brick: metabolism and tumor cell growth. Current opinion in genetics & development 18, 54–61 (2008). 10.1016/j.gde.2008.02.003
[29]
Eagle, H., Oyama, V. I., Levy, M., Horton, C. L. & Fleischman, R. The growth response of mammalian cells in tissue culture to L-glutamine and L-glutamic acid. Journal of Biological Chemistry 218, 607–616 (1956). 10.1016/s0021-9258(18)65826-0
[30]
Menendez, J. A., Lupu, R. & Colomer, R. Inhibition of tumor-associated fatty acid synthase hyperactivity induces synergistic chemosensitization of HER-2/neu-overexpressing human breast cancer cells to docetaxel (taxotere). Breast cancer research and treatment 84, 183–195 (2004). 10.1023/b:brea.0000018409.59448.60
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Published
Dec 13, 2016
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6(1)
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Syed Ghulam Musharraf, Najia Shahid, Syed Muhammad Ali Naqvi, et al. (2016). Metabolite Profiling of Preneoplastic and Neoplastic Lesions of Oral Cavity Tissue Samples Revealed a Biomarker Pattern. Scientific Reports, 6(1). https://doi.org/10.1038/srep38985