|Year : 2017 | Volume
| Issue : 2 | Page : 127-131
Current state of salivaomics and metabolomic profiling as novel diagnostics for biomarker research and applications in oral cancer and personalized medicine
Simi Thankappan1, N Sherin2
1 Department of Oral Medicine and Radiology, Kothiwal Dental College and Reasearch Centre, Moradabad, Uttar Pradesh, India
2 Department of Oral Pathology and Microbiology, Kothiwal Dental College and Reasearch Centre, Moradabad, Uttar Pradesh, India
|Date of Web Publication||14-Nov-2017|
Department of Oral Medicine and Radiology, Kothiwal Dental College and Reasearch Centre, Moradabad, Uttar Pradesh
Biomarkers are the main focus of current researches in biological characteristics that are easily measured in patients, and their discovery has laid the foundation for personalized medicine. Salivaomics and metabolomics are the rapidly emerging diagnostics focused on comprehensive profiling of metabolites in intracellular or biofluid sample. A systematic search of PubMed Central, Ebsco Host, Science Direct, and Scopus was done and the search terms included salivaomics, metabolomics, biomarkers, metabolites, oral cancer, proteomics, personalized medicine, and omics. This review article summarizes on the significance of salivary metabolites as diagnostic and prognostic biomarkers due to different biochemical pathways in oral cancer patients and healthy controls, even early detection of cancer in patients, thus emphasizing on the rigorous need of future research in this field.
Keywords: Biomarkers, metabolomics, oral cancer, personalized medicine and omics, proteomics, salivaomics
|How to cite this article:|
Thankappan S, Sherin N. Current state of salivaomics and metabolomic profiling as novel diagnostics for biomarker research and applications in oral cancer and personalized medicine. Trop J Med Res 2017;20:127-31
|How to cite this URL:|
Thankappan S, Sherin N. Current state of salivaomics and metabolomic profiling as novel diagnostics for biomarker research and applications in oral cancer and personalized medicine. Trop J Med Res [serial online] 2017 [cited 2018 May 23];20:127-31. Available from: http://www.tjmrjournal.org/text.asp?2017/20/2/127/218210
| Introduction|| |
The biggest boom in personalized medicine happened in 2003 on the completion of the human genome project. Personalized medicine and health care can detect the early stages of the disease, using specific molecular markers in an individual, even before signs and symptoms appear, and therefore, gives opportunity to focus on prevention and early intervention rather than succumbing to advanced stages of disease. The goals of personalized medicine are targeted for better understanding of disease process in an individual, efficient drug development, better medical prognosis, earlier interventions, and improved diagnosis. Biomarkers are the main focus of current researches in biological characteristics that are easily measured in patients, and their discovery has laid the foundation for personalized medicine.
The term “biomarker” refers to a broad category of objective indicators of a particular condition or pathology observed from outside the patient, so that they are accurately identified, measured, and recorded., According to the National Institutes of Health (NIH) and the NIH Biomarkers Definitions Working Group (1988), a biomarker is a “characteristic that is objectively measured and evaluated as an indicator of a normal biological process, pathogenic process, or pharmaceutical response to therapeutic intervention.” Another definition, given by a joint collaboration by the International Programme on Chemical Safety, under the World Health Organization (WHO), the United Nations, and the International Labor Organization, states the biomarker as “any substance, structure, or process that can be measured in the body or its products and influence or predict the incidence of outcome or disease” (WHO). It should be verified and validated in appropriate experiments and clinical trials, before it can be used in a clinical assay and can have useful impact or application in health risk assessment.,,,
All the information about genes, proteins, and molecules are assessed in the determination and identification of suitable biomarkers. “Omic” strategies as included in our medical lexicon for years, pertains to profiling technologies in genomics, transcriptomics, proteomics, metabolomics, metabonomics, microbiomics, and even methylomics and provides all the data in biomarker field research.,,
| Metabolomics: Insight Into Metabolites and Metabolic Pathways|| |
Metabolism is the complex life-sustaining chemical process occurring in biological cells, involving enzyme-catalyzing reactions and production of energy. The biochemical intermediates generated during their production and utilization are collectively referred to as metabolites. The set of metabolites synthesized by a biological system constitute its “metabolome.” It can provide an overview of the metabolic status, biochemical events associated with a cellular or biological system, and can provide measurement and interpretation of metabolites, thus facilitating the generation of profiles from biological samples. These metabolites provide a mirror into the biochemical activity of each individual cell.
Metabolomics is the emerging diagnostics focused on comprehensive profiling of metabolites in a sample that can be intracellular or biofluids. Metabolomics has become a powerful approach that has been widely adopted for clinical diagnostics. It is a relatively new field in “omics” research. Metabolomic profiles give the immediate biological state of a sample and thus serves as unique chemical fingerprints and act as biomarkers. These profiles are altered in diseases and are detectable in biofluids, such as saliva, tissue, blood, urine, and others. The most important fact is that metabolic profile alteration can be detected before symptoms of any disease appear in a patient. For this reason, metabolomics has potential as a reliable method for an early diagnosis of diseases through biomarker identification. Since metabolites are directly linked to cellular biochemistry, they are, therefore, easier to correlate with phenotype, unlike genes, and proteins, whose function is subject to epigenetic regulation and posttranslational modifications, respectively.,,,, This ability of metabolomics to measure specific phenotypes gives it great power in the field of oncology to further understand what is happening in cancer cells. However, the process involved is complex and involves gene regulation, posttranscriptional interactions.
| Metabolomics in Carcinogenesis|| |
Carcinogenesis is often correlated with an altered glucose metabolism, and the link between cancer and altered metabolism is not new. In the 1920s, Otto Warburg observed that cancer cells, unlike normal cells in the body, opt for glycolysis rather than mitochondrial respiration, even in the presence of oxygen. His concept was that the aerobic glycolysis phenotype was due to the irreversibly dysfunctional mitochondria in the cancer cell. These particular dysfunctional mitochondria, in a series of reactions and pathways, lead to biochemical events that eventually result in carcinogenesis. Most tumor cells have a high rate of aerobic glycolysis, and this was referred to as the Warburg effect. The end product of glycolysis was increased lactate production. A biochemist, Weinhouse, pointed to the Crabtree effect, which had a completely different view of the whole process and disagreed with Warburg's view. He argued that the reverse was true for cancer initiation, cancer cells have reduced mitochondrial activity as a consequence of heightened glycolytic flux, which is known to inhibit mitochondria.,,, Even though there was ongoing debate for many decades regarding Warburg and Crabtree effect, there is conclusive evidence for proving Warburg effect to be true. Many common cell mutations have been shown to support the Warburg effect as reported by several authors.,,,,, A research group from University of Michigan recently performed global metabolic profiling of metabolites in head and neck squamous cell carcinoma subjects and came to a conclusion that the increase in specific metabolites seen in head and neck cancers could be related to the Warburg effect.
The reliance of cancer cells on increased glucose uptake not only proved to be beneficial to metabolomics but also in tumor detection and monitoring, with this phenotype serving as the basis for clinical [18F] fluorodeoxyglucose positron emission tomography imaging.,
| Saliva Metabolome|| |
Saliva is the most easily accessible, safe, noninvasive, and readily obtained biofluid and is researched widely as one of the diagnostic media. Salivary diagnostics has been implemented for more than 2000 years according to some traditional health-care systems such as Chinese medicine. Advances in molecular biology and biotechnology have contributed largely to research in all fields of omics-genomics, transcriptomics, proteomics, metabolomics, and metagenomics leading to the identification and characterization of salivary components that are termed omics-based biomarkers., The term salivaomics, coined in the last decade, was used by several authors, to bring together all the diagnostic tools based on salivary biomarkers.,11-14,
There are multiple analytical techniques for quantifying salivary metabolome. It includes nuclear magnetic resonance spectroscopy, gas chromatography mass spectrometry, direct flow injection/liquid chromatography mass spectrometry, plasma mass spectrometry, and high-performance liquid chromatography. All these techniques have contributed to a big leap in salivaomics, and a study done recently in human saliva could quantify/identify 308 salivary metabolites or metabolite species.,
| Salivary Metabolomics in Oral Cancer|| |
The role of specially expressed genes and proteins in oral squamous cell carcinoma (OSCC) and precancerous lesions has been extensively studied in the field of genomics and proteomics. There are also changes in the concentration of endogenous metabolites.,,, Lactic acid, an end product of glycolysis, was observed at a higher level in saliva of OSCC patients in various studies and was explained by the series of biochemical events. Increased lactic acid is associated with the decreased pyruvate entering into tricarboxylic acid (TCA) cycle. The impaired TCA production due to the insufficient pyruvate supply is, therefore, supplemented by adjuvant metabolic pathways such as branched chain amino acids (BCAAs) entering into the TCA cycle when there is a shortage in energy supply. The catabolism of BCAAs (nonproteinogenic BCAAs) together with other amino acids valine, leucine, and isoleucine (proteinogenic BCAAs) leads to divergence of metabolic pathway, producing many intermediates to be consumed in TCA cycle. Some authors have observed that valine, leucine, and isoleucine were all at relatively decreased levels in saliva of OSCC patients, presumably due to the changed metabolic pathways in TCA cycle in cancer cells as explained above., Wei et al. observed decreased salivary level of phenylalanine in the OSCC group in a study. From the study, We showed that lactic acid and valine are the best predictors for distinguishing OSCC from healthy control, and lactic acid, valine, and phenylalanine for OSCC from premalignant lesions such as leukoplakia. Hirayama et al. also have reported significantly higher levels of amino acids. Thus, utility of salivary metabolome diagnostics for OSCC can be successfully correlated in various studies and these results suggest that metabolomics approach complements the clinical detection of OSCC.
Sugimoto et al. in a study of oral cancer and healthy controls showed twenty-eight metabolites that have been discriminatory between subjects with oral cancer and healthy controls. In another study, fourteen metabolites were tentatively identified as potential biomarkers for early diagnosis of OSCC. These included lactic acid, hydroxyphenyllactic acid, N-nonanoylglycine, 5-hydroxymethyluracil, succinic acid, ornithine, hexanoylcarnitine, propionylcholine, carnitine, 4-hydroxy-L-glutamic acid, acetylphenylalanine, sphinganine, phytosphingosine, and S-carboxymethyl-L-cysteine. Among all the biomarkers, eight potential biomarkers were upregulated in saliva of OSCC patients (lactic acid, hydroxyphenyllactic acid, N-nonanoylglycine, 5-hydroxymethyluracil, succinic acid, ornithine, hexanoylcarnitine, and propionylcholine) and six potential biomarkers were downregulated (carnitine, 4-hydroxy-L-glutamic acid, acetylphenylalanine, sphinganine, phytosphingosine, and S-carboxymethyl-L-cysteine). The results also demonstrated that five salivary biomarkers (propionylcholine, acetylphenylalanine, sphinganine, phytosphingosine, and S-carboxymethyl-L-cysteine) in combination will improve the sensitivity and specificity for the early detection in Stage I and II of OSCC.
A study comparing salivary metabolite profile of male smokers and nonsmokers revealed citrate, lactate, pyruvate, and sucrose to be in higher concentrations, and formate to be lower in smokers compared with nonsmokers. In the same study, acetate, formate, glycine, lactate, methanol, propionate, propylene glycol, pyruvate, succinate, and taurine were significantly higher in concentration in male saliva compared to female saliva.
Glutamine also has been seen to be an energy source such as glucose and serves as an energetic substrate for the cells. Here, tumor cells obtain energy through glutaminolysis in addition to glycolysis. The process causes an increased utilization of gamma-aminobutyric acid (GABA) and this reflects as a decreased level of GABA in saliva., The glutaminolytic phenotype under hypoxic conditions has been associated with activation of the oncogenes Ras and Myc, and the concomitant loss of function of tumor suppressor such as p53. Another fluorescence spectroscopic study in OSCC has confirmed the presence of excess porphyrin in the saliva of oral cancer patients, probably due to disturbances and alterations in biosynthetic pathways in malignant cells.
Comprehensive list of salivary metabolomic biomarkers highlighted in various studies:
- Phenylalanine: Alpha amino acid, and precursor of tyrosine; levels are decreased in patients with OSCC. Potential biomarker for early diagnosis of OSCC,
- Acetyl phenyl alanine: The N-acetyl derivative of phenylalanine; levels are decreased in patients with OSCC. Can be used to improve the sensitivity and specificity in early detection of OSCC
- Pyrroline hydroxycarboxylic acid: Heterocyclic organic chemical compound can be used to discriminate between individuals with oral cancer and healthy controls
- Choline and betaine: A quaternary amine highly metabolized in tumors to phosphocholine and highly oxidized to betanine; hence, detection of low concentration of choline and high concentrations of phosphocholine and betaine levels in tumor cells than normal or premalignant cells acts as biomarker; levels of phosphocholine and glycerophosphocholine were increased in the saliva samples from OSCC patients; can be used to discriminating OSCC patients and healthy controls
- Tryptophan: Increased levels can be used to discriminate between individuals with oral cancer and healthy controls; direct marker for tumor development
- Ornithine: Markedly higher in patients with oral cancer
- Polyamines: Putrescine and cadaverine: Markedly higher in patients with oral cancer decreased in patients undergoing radiotherapy; it is used to monitor the effect of chemotherapy on oral cancer cells
- Leucine and isoleucine: Decreased levels can be used to discriminate between individuals with oral cancer and healthy controls
- Valine: Valine decreased in cancer patients and can be used to discriminate between individuals with oral cancer and healthy controls.
- Threonine: Slightly elevated
- Pipecolic acid: Slightly elevated
- Glutamic acid: Slightly elevated
- Carnitine: Downregulated; Potential biomarkers for early diagnosis of OSCC
- 4-hydroxy-L-glutamic acid: Downregulated; Potential biomarkers for early diagnosis of OSCC
- Sphinganine: Downregulated; potential biomarkers for early diagnosis of OSCC
- Phytosphingosine: Downregulated; potential biomarkers for early diagnosis of OSCC
- S-carboxymethyl-L-cysteine: Downregulated; potential biomarkers for early diagnosis of OSCC
- Alanine: Increased in cancer patients
- Piperidine: Highly elevated
- Piperideine: Slightly elevated
- Taurine: Slightly elevated
- Hydroxyphenyl lactic acid: Upregulated in saliva; potential biomarkers for early diagnosis of OSCC
- N-nonanoylglycine: Upregulated in saliva; potential biomarkers for early diagnosis of OSCC,
- 5-hydroxymethyluracil: Upregulated in saliva; potential biomarkers for early diagnosis of OSCC
- Succinic acid: Upregulated in saliva; potential biomarkers for early diagnosis of OSCC
- Ornithine: Upregulated in saliva; potential biomarkers for early diagnosis of OSCC
- Hexanoylcarnitine: Up-regulated in saliva; potential biomarkers for early diagnosis of OSCC
- Propionylcholine: Upregulated in saliva; potential biomarkers for early diagnosis of OSCC
- C5H14N5: Highly increased in cancer patients
- Porphyrin: Elevated in cancer patients.
| Conclusion|| |
Salivaomics promises to be a fruitful and rapidly expanding field in the years to come. So far, research in this area is only in its infancy and already its clinical and diagnostic applications are enormous. From preemptive and early diagnosis of OSCC to monitoring the efficiency of treatment regimens, saliva-based metabolite assays find uses in almost all aspects of clinical oral oncology. However, further studies and much work still need to be done that addresses specific salivary biomarkers before many of these assays can move from experimental stages to clinical practice.
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Conflicts of interest
There are no conflicts of interest.
| References|| |
Eckhart AD, Beebe K, Milburn M. Metabolomics as a key integrator for “omic” advancement of personalized medicine and future therapies. Clin Transl Sci 2012;5:285-8.
Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin Pharmacol Ther 2001;69:89-95.
Strimbu K, Tavel JA. What are biomarkers? Curr Opin HIV AIDS 2010;5:463-6.
Ilyin SE, Belkowski SM, Plata-Salamán CR. Biomarker discovery and validation: Technologies and integrative approaches. Trends Biotechnol 2004;22:411-6.
Silberring J, Ciborowski P. Biomarker discovery and clinical proteomics. Trends Analyt Chem 2010;29:128.
Yakob M, Fuentes L, Wang MB, Abemayor E, Wong DT. Salivary biomarkers for detection of oral squamous cell carcinoma - current state and recent advances. Curr Oral Health Rep 2014;1:133-41.
Patti GJ, Yanes O, Siuzdak G. Innovation: Metabolomics: The apogee of the omics trilogy. Nat Rev Mol Cell Biol 2012;13:263-9.
Fiehn O. Metabolomics – The link between genotypes and phenotypes. Plant Mol Biol 2002;48:155-71.
Zhang A, Sun H, Wang X. Saliva metabolomics opens door to biomarker discovery, disease diagnosis, and treatment. Appl Biochem Biotechnol 2012;168:1718-27.
Li Y, St John MA, Zhou X, Kim Y, Sinha U, Jordan RC, et al .
Salivary transcriptome diagnostics for oral cancer detection. Clin Cancer Res 2004;10:8442-50.
Sidransky D. Emerging molecular markers of cancer. Nat Rev Cancer 2002;2:210-9.
Hollstein M, Sidransky D, Vogelstein B, Harris CC. p53 mutations in human cancers. Science 1991;253:49-53.
Liu T, Wahlberg S, Burek E, Lindblom P, Rubio C, Lindblom A. Microsatellite instability as a predictor of a mutation in a DNA mismatch repair gene in familial colorectal cancer. Genes Chromosomes Cancer 2000;27:17-25.
Warburg O. The metabolism of carcinoma cells. J Cancer Res 1925;9:148-63.
Warburg O. On the origin of cancer cells. Science 1956;123:309-14.
Weinhouse S. On respiratory impairment in cancer cells. Science 1956;124:267-9.
Weinhouse S. The Warburg hypothesis fifty years later. Cancer Res Clin Oncol 1976;87:115-26.
Sussman I, Erecinska M, Wilson DF. Regulation of cellular energy metabolism: The Crabtree effect. Biochim Biophys Acta 1980;591:209-23.
Crabtree HG. Observations on the carbohydrate metabolism of tumours. Biochem J 1929;23:536-45.
Cairns RA, Harris IS, Mak TW. Regulation of cancer cell metabolism. Nat Rev Cancer 2011;11:85-95.
Vermeersch KA, Styczynski MP. Applications of metabolomics in cancer research. J Carcinog 2013;12:9.
] [Full text]
Beger RD. A review of applications of metabolomics in cancer. Metabolites 2013;3:552-74.
Senyilmaz D, Teleman AA. Chicken or the egg: Warburg effect and mitochondrial dysfunction. F1000Prime Rep 2015;7:41.
Semenza GL. HIF-1: Upstream and downstream of cancer metabolism. Curr Opin Genet Dev 2010;20:51-6.
Elstrom RL, Bauer DE, Buzzai M, Karnauskas R, Harris MH, Plas DR, et al .
Akt stimulates aerobic glycolysis in cancer cells. Cancer Res 2004;64:3892-9.
Wong DT. 2007. Saliva-Based Translational Research & Clinical Applications (StarCA), Los Angeles. UCLA Dental Research Institute. 2007.
Cuevas-Córdoba B, Santiago-García J. Saliva: A fluid of study for OMICS. OMICS 2014;18:87-97.
Wong DT. Salivaomics. J Am Dent Assoc 2012;143(10 Suppl):19S-24S.
Dame ZT, Aziat F, Mandal R, Krishnamurthy R, Bouatra S, Borzouie S, et al
. The human saliva metabolome. Metabolomics 2015;11:1864-83.
Cheng YS, Rees T, Wright J. A review of research on salivary biomarkers for oral cancer detection. Clin Transl Med 2014;3:3.
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.
Yan SK, Wei BJ, Lin ZY, Yang Y, Zhou ZT, Zhang WD. A metabonomic approach to the diagnosis of oral squamous cell carcinoma, oral lichen planus and oral leukoplakia. Oral Oncol 2008;44:477-83.
Wei J, Xie G, Zhou Z, Shi P, Qiu Y, Zheng X, et al .
Salivary metabolite signatures of oral cancer and leukoplakia. Int J Cancer 2011;129:2207-17.
Shankar AA, Alex S, Routray S. Incorporation of salivary metabolomics in oral cancer diagnostics. Oral Oncol 2014;50:e53-4.
Hirayama A, Kami K, Sugimoto M, Sugawara M, Toki N, Onozuka H, et al .
Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry. Cancer Res 2009;69:4918-25.
Wang Q, Gao P, Wang X, Duan Y. The early diagnosis and monitoring of squamous cell carcinoma via saliva metabolomics. Sci Rep 2014;4:6802.
Takeda I, Stretch C, Barnaby P, Bhatnager K, Rankin K, Fu H, et al .
Understanding the human salivary metabolome. NMR Biomed 2009;22:577-84.
Dang CV. Glutaminolysis: Supplying carbon or nitrogen or both for cancer cells? Cell Cycle 2010;9:3884-6.
Young SZ, Bordey A. GABA's control of stem and cancer cell proliferation in adult neural and peripheral niches. Physiology (Bethesda) 2009;24:171-85.
Hanahan D, Weinberg RA. Hallmarks of cancer: The next generation. Cell 2011;144:646-74.
Yuvaraj M, Udayakumar K, Jayanth V, Prakasa Rao A, Bharanidharan G, Koteeswaran D, et al .
Fluorescence spectroscopic characterization of salivary metabolites of oral cancer patients. J Photochem Photobiol B 2014;130:153-60.
Okamura M, Kobayashi M, Suzuki F, Shimada J, Sakagami H. Induction of cell death by combination treatment with cisplatin and 5-fluorouracil in a human oral squamous cell carcinoma cell line. Anticancer Res 2007;27:3331-7.