Metabolomics in head and neck cancer

5 Metabolomics in head and neck cancer: A summary of findings Ravi Kasiappan 1, 2, Pachiyappan Kamarajan 1, Yvonne L. Kapila 1 1

Division of Periodontology, Department of Orofacial Sciences, University of California San Francisco, San Francisco, CA, United States 2 Department of Biochemistry, CSIR-Central Food Technological Research Institute, Mysore, Karnataka, India

Abstract Head and neck cancer (HNC) is a group of cancers that start in the mucosal lining of the upper aerodigestive tract and can metastasize to different sites. HNC affects more than half a million people worldwide annually. Recently, there has been a significant focus on finding new biomarkers for HNC by using “omics” approaches. In particular, metabolomic studies have been used to identify tumor-specific metabolite profiles by employing different platforms and technologies on different sample types. Although several independent reports on HNC metabolomics have been published, a comprehensive and current analysis of HNC metabolomic data has not been carried out, yet this is warranted to help better understand the metabolic contributions to HNC pathogenesis and to help in the development of new therapeutic targets for HNC. In this chapter, we summarize all published metabolomic data for HNC and identify significant metabolites that differentiate HNC from normal subjects based on analysis of different sample types. We also discuss the various tools used in metabolomics to identify important metabolites from tissue, serum, saliva, cell lines, and urine. This chapter presents important findings from metabolomic-based research focused on HNC and provides the foundation for metabolomicbased targeted therapies for HNC.

Introduction The estimated incidence of head and neck cancer (HNC) is approximately 600,000 cases, which accounts for 3% of all cancers.1 The estimated number

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of deaths from HNC is 38,000 cases annually. HNC is a group of cancers that arises in the mouth, nose, throat, salivary glands, and larynx and can metastasize to different anatomical sites.2 Approximately 75% of HNCs are diagnosed as oral cancers, and 90% of oral cancers are oral squamous cell carcinoma (OSCC).2,3 The major risk factors for HNC are smoking, alcohol consumption, infections with human papillomavirus (HPV), and EpsteineBarr virus.4,5 However, these risk factors alone do not explain the observed incidence and pathogenesis of HNC because some patients do not present these risk factors. Thus, other unknown risk factors play important roles in HNC tumorigenesis, tumor progression, and metastasis. Furthermore, despite technological advancements and novel therapeutic strategies, the survival rates for HNC have not improved in decades due to its recurrence and malignant properties.6,7 Hence, identification of novel biomarkers for early diagnosis of HNC are needed to improve patient survival rates. In recent years, there has been a significant focus on new biomarker discovery in oncological research with the use of “omics” technology, including metabolomics.8e12 Metabolomic profiling studies have gained importance because of their ability to characterize normal physiological processes and pathological conditions in various disease entities. Although several independent reports on HNC metabolomics have been published, a comprehensive and updated analysis of metabolomic data has not been realized. Such an overview is warranted to help better understand the metabolic contributions to HNC pathogenesis and to help in the development of new therapeutic targets for HNC.10,13 In this chapter, we summarize all published metabolomic data and identify significant metabolites that differentiate HNC from normal subjects based on different types of HNC samples (Fig. 5.1A). We also discuss the various tools used in metabolomics to identify important metabolites from tissue, serum, saliva, cell lines, and urine (Fig. 5.1B). The major metabolic profiling techniques include nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), and each exhibit distinctive analytical strengths and weaknesses, while delivering complementary information as shown in Table 5.1.14,15 Modern “omics” approaches, including metabolomics, can help provide a greater understanding of the underlying mechanisms governing cancer pathogenesis and provide new therapeutic strategies for the treatment of aggressive and malignant cancer types, such as HNC. In the following sections, we discuss the findings from published studies that used different biofluids and cell/tissue extracts to study HNC metabolomics. Biofluids, such as saliva, blood, and urine, can be obtained noninvasively or with minimally invasive methods20 and are used in many metabolomic-based studies.21e24

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(A)

(B)

Figure 5.1 Overlapping metabolites in head and neck cancer (HNC) identified from different sample types and by different methods. The Venn diagrams show the number of unique and differentially expressed HNC metabolites identified in (A) saliva, blood, urine, cells, and tissues and by different detection methods (B) HPLC/GC/MS, NMR/MAS, MRS, and others. Reddincreased levels; blueddecreased levels; greend increased and decreased levels. Abbreviations: Ala, alanine; Asp, aspartate; Bet, betaine; Cit, citrate; Cr, creatinine; Cre, creatine; Cho, choline; Glu, glutamate; Gluc, glucose; Gln, glutamine; Glut, glutathione; Gly, glycine; GPC, glycerophosphocholine; His, histidine; Ile, isoleucine; Lac, lactate; Leu, leucine; Lys, lysine; Mal, malate; PCho, phosphocholine; Phe, phenylalanine; Pro, proline; Pyr, pyruvate; Tau, taurine; Thr, threonine; Tg, triglyceride; Tyr, tyrosine; Val, valine.

Saliva metabolomics Saliva is a complex biological fluid that contains proteins, electrolytes, trace metals, lipids, and other biochemicals. Saliva is a critical factor for speech, taste, digestion of foods, tissue lubrication, tooth mineralization, toxin neutralization, and as an antiviral and antimicrobial.25,26 Collection of saliva is simple and noninvasive and readily available. Hence, saliva has been a popular medium for “omics” based research studies.27,28 There are two types of saliva that are used for metabolomic studies: stimulated and unstimulated whole saliva. Because the chemical composition of these two saliva types vary, it is important to note the particular type of saliva that is being used for study.28e30 Salivary metabolites are clinically important because they are transferred into saliva from the salivary glands, blood, and various cells within the oral cavity22 and are thereby readouts of the status of these tissues and local or systemic circulation. The majority of HNC types are OSCC, and these are associated with a poor 5-year survival rate and high morbidity.6,31 Several investigators have proposed the use of salivary metabolomics to differentiate precancerous from malignant HNC lesions, including OSCC to help improve the diagnosis and prognosis of HNC. For instance, Yan and coworkers identified metabolites

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Table 5.1 Advantages and disadvantages of metabolite identification methods. Methods

Advantages

Disadvantages

HPLC16





Amenable to diverse analyte sample types Precise and highly reproducible quantitative analysis





Lack of an ideal universal detector Less separation efficiently than capillary GC

NMR14






Highly reproducible, high resolution, nondestructive Simultaneously detects all metabolites No derivatization method






Low sensitivity (mM to mM concentration) More than one peak per component Limited use of libraries due to complex matrix

GC17







Highly sensitive, detects down to 100 ppm High resolution, reliable, and relatively simple Highly accurate quantitative analysis Fast analysis (minutes even seconds)







Limited to volatile samples Unable to detect thermally labile samples Requires MS for confirmation of peak identity Destructive detector

MS18





Unique spectra and easily interpreted Molecular weight can be determined from a very small sample and measures isotopic ratios Detection limits are 3 times higher than other techniques




Unable to distinguish between isomers of a compound having the same charge-to-mass ratio 2e3 times more costly than other instruments Interference effects may occur Drift can be as high as 5%e10% hour

Highly sensitive and robust Suitable for analysis of low molecular weight hydrophobic compounds and mixtures Volatile compounds can be directly analyzed Gives large linear range













Suitable for analysis of relatively polar compounds with low, moderate, or high molecular weights Thermo-unstable compounds can be analyzed No derivatization method Highly sensitive, flexible, automation






Highly sensitive, fast, and low cost Requires low sample volumes Automation and multidimensional







GC-MS17







LC-MS17

CE-MS19
















Unable to detect nonvolatile and thermo-unstable compounds Often requires derivatization and it can mask the result

Different types of adducts can be produced Time consuming and sensitive toward interfering compounds Restricted toward mass range Fragmentation patterns are poorly reproducible Unable to detect larger than 20 kD proteins

that could clearly differentiate OSCC from precancerous lesions, namely oral lichen planus (OLP) and oral leukoplakia (OLK), by using hierarchical principal component analysis (PCA) and discriminate analysis algorithms32 (Table 5.2). However, these metabolites could not distinguish between OLP and OLK groups in the PCA plot.32 Almadori and coworkers demonstrated that salivary glutathione, but not uric acid, was markedly increased in oral and pharyngeal SCC cases that could clearly differentiate from healthy

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Table 5.2 Summary of metabolites identified in saliva of head and neck cancer (HNC) subjects. Number of examined subjects

HNC type (sample)

Metabolomic findings

Instrument used (reference)

HNSCC (saliva)

50 HNSCC and 77 healthy

Increased: glutathione

HPLC33

OSCC OLP OLK (saliva)

20 OSCC, 20 OLP, 7 OLK and 11 healthy

Metabolic profiling data distinguished between OSCC, OLP, and OLK

HPLC/MS CE-TOF-MS32

OSCC (saliva)

69 OSCC and 87 healthy

28 differentially expressed metabolites were detected and were used to predict oral cancer outcome

CE-TOF-MS38

OSCC (saliva)

41 OSCC and 30 healthy control

Increased: transferrin

MALDI-TOF-MS34

OSCC, OLK (saliva)

37 OSCC, 32 OLK, and 34 healthy

41 metabolites distinguished OSCC from control, 61 distinguished OSCC from OLK, and 27 distinguished OLK from control

UPLC-Q-TOF-MS39

OSCC (saliva and tissues)

44 OSCC (of these patients, 18 provided both saliva and tumor tissues) and 20 healthy

Increased: lactate, arginine, ornithine, S-adenosylmethionine, pipecolate Decreased: glyceraldehyde 3-phosphate, phosphoenolpyruvate

CE-TOF-MS40

OSCC (saliva)

22 OSCC and 21 healthy control

25 metabolites were differentially expressed and discriminate OSCC from healthy

CE-MS41

controls.33 This study indicates that high glutathione levels may be an epidemiological marker for identifying patients with a high risk for OSCC. Jou and coworkers used two-dimensional gel electrophoresis followed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) analyses to demonstrate that transferrin levels were elevated in oral cancer subjects compared with controls. The elevated transferrin levels were validated by western blotting and immunoassays. Furthermore, the size and stage of the tumor were correlated with transferrin expression levels, suggesting that salivary transferrin could serve as a biomarker for early detection of oral cancer.34

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Polyamines are small organic cations that are vital for normal cell growth and development in eukaryotes.35,36 Elevated levels of polyamines have been associated with decreased apoptosis, increased cell proliferation, and increased expression of genes, which are responsible for tumor invasion and metastasis.37 Thus, high levels of polyamines may be important for cancer processes. For example, Sugimoto and colleagues used capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS) to identify metabolites that clearly distinguish oral cancers from healthy control subjects.38 Out of 28 differentially expressed metabolites, the levels of polyamines were significantly elevated in oral cancer compared with breast and pancreatic cancer and normal healthy controls,38 indicating that polyamines can distinguish not only oral cancer and healthy cases but also between cancer types (Table 5.2). Using ultra-performance liquid chromatography coupled with quadrupole/ time-of-flight spectrometry (UPLC-Q-TOF-MS) analysis, Wei and coinvestigators found a panel of five salivary metabolites, including g-aminobutyric acid, phenylalanine, valine, n-eicosanoic acid, and lactic acid that could differentiate OSCC from healthy controls.35 These data suggest that metabolites identified through metabolomic approaches can complement the clinical detection of OSCC for improved diagnosis and prognosis.39 Using CE-TOF-MS, Ishikawa and colleagues profiled both saliva and OSCC tissue metabolites and identified biomarkers for oral cancer screening.40 In this study, 85 and 45 metabolites showed significant differences between tumor and matched control samples, and between salivary samples from oral cancer and controls, respectively. Among these, 17 metabolites showed consistent differences in both saliva and tissue samples. Out of 17 metabolites, a combination of two biomarkers, namely S-adenosylmethionine (SAM) and pipecolate, were significantly increased in saliva and tumor tissues of OSCC, suggesting that these salivary metabolites could serve as biomarkers to screen for or detect OSCC.40 A recent study in a Japanese population showed that 25 saliva metabolites could differentiate between OSCC patients and healthy controls.41 Seven of these metabolites (taurine, valine, leucine, isoleucine, choline, cadaverine, and tryptophan) were consistent with those from an earlier study by Sugimoto and coworkers.38 Recently, oral metabolites were analyzed in presurgical oral washes and HNSCC tumor tissues with adjacent normal tissues from surgical resections using LC/LC/MS and GC/MS. Among 12 and 23 metabolites from oral washes in normal and HNSCC patients, respectively, phosphate and lactate were the most abundant metabolites. The levels of b-alanine, a-hydroxyisovalerate, tryptophan, and hexanoylcarnitine were elevated in HNSCC oral washes compared with healthy controls. In addition, eight metabolites, including two TCA cycle analogs, 2-hydroxyglutarate and glycerol-3-monophosphate, were significantly increased in HNSCC tumor

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tissues compared with controls42 (Table 5.2). Based on the above published studies, these salivary metabolites may have potential as biomarkers for detection of HNC, which may improve diagnosis and prognosis of HNC.

Blood and urine metabolomics Blood and urine metabolites have been examined for their biological significance, abundance, and potential use as diagnostic markers.21,24 Blood has been extensively studied for metabolomic studies.43 Blood (both plasma and serum) contains a comprehensive panel of metabolites, including hormones, electrolytes, metabolic by-products, nutrients (carbohydrates, lipids, and amino acids), and organic waste.24 Studies suggest that the metabolite content of serum and plasma is the same within the aqueous phase.43 Studies have shown that the chemical and protein metabolic composition of blood is altered in several diseases, including cancer.24,44 Multiple studies reported different metabolic profiles in serum and plasma samples from HNC cases. Choline was one of the most frequently identified and highly expressed metabolites in HNC samples regardless of sample type (Fig. 5.1A). Numerous studies demonstrated that choline-containing compounds were increased in OSCC samples compared with normal healthy subjects.45e49 Choline is an important nutrient that is converted into phosphocholine by choline kinase then becomes incorporated into the cell membrane.50 Choline can be considered a biomarker for cancer cell survival, proliferation, and metastasis.51e53 Studies have suggested a strong association between cancer cell signaling and choline metabolism.51,52,54e56 Thus, choline metabolism is considered a hallmark of tumor initiation and metastasis.52 Several other studies also found differential metabolic profiles in the serum and plasma of HNC patients. For instance, Tiziani and colleagues used NMR and identified abnormal metabolic activity in lipolysis, the TCA cycle, and amino acid catabolism in OSCC patients49 (Table 5.3). They observed that the levels of ketone bodies were increased in OSCC patients, suggesting that increased ketone bodies, which are associated with lipolysis, may be a backup mechanism for energy production.49 This study further found that OSCC tumors depend significantly on glycolysis and lactic acid fermentation, indicating a reliance on the “Warburg effect” as a main energy source.49,57 Yonezawa and colleagues used GC/MS methods to profile serum and tissue metabolites in HNSCC relapse patients.58 Four metabolites, namely glucose, methionine, ribulose, and ketoisoleucine, were significantly altered in OSCC samples.58 The authors also found an inverse correlation between serum- and tissue-expressed metabolites in HNSCC patients. For instance, amino acids, including valine, tyrosine, serine, and methionine, were poorly expressed,

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Table 5.3 Summary of metabolites identified in blood and urine of head and neck cancer (HNC) subjects. HNC type (sample)

Number of examined subjects

Instrument used (reference)

Metabolomic findings

OSCC and OLK (blood)

33 OSCC, 5 OLK and 28 healthy

At least 17 metabolites were differentially expressed and differentiated OSCC from healthy

1

H NMR45

OSCC (blood)

15 OSCC and 10 healthy

Altered energy metabolism: lipolysis (increased levels of ketone bodies) TCA cycle (i.e., Ycitrate, succinate, formate) amino acid catabolism (i.e., [2-hydroxybutyrate, ornithine, asparagine)

1 D 1H and 2D 1H J-resolved NMR49

HNSCC (blood and tissues)

25 HNSCC (of these patients, 17 used for serum and 19 used for tissue analysis)

Serum:[ glycolysis, Y amino acids Tissues: [ amino acids, Y glycolysis

GC/MS58

OSCC and OLK (blood)

100 OSCC, 100 OLK, and 75 healthy control

Increased: propionate, acetone, choline, acetate Decreased: glutamine, valine, creatinine, threonine

1

OSCC and OLK (urine)

37 OSCC, 32 OLK and 34 healthy

Increased: alanine, tyrosine, valine, serine, and cysteine Decreased: hippurate and 6-hydroxynicotic acid Regression model based on valine and 6-hydroxynicotic acid yielded an accuracy of 98.9%, sensitivity of 94.4%, specificity of 91.4%, and positive predictive value of 91.9% in distinguishing OSCC from the controls

GC-MS60

H NMR59

whereas glycolytic pathway-associated metabolites were highly expressed in serum compared with tissues.58 Furthermore, serum metabolites could distinguish HNSCC relapse patients from those without relapse.58 An NMRbased study identified four serum metabolomic biomarkers, including glutamine, propionate, acetone, and choline, which differentiated OSCC from normal healthy cases with substantial sensitivity and specificity. In the same study, another four serum biomarkers (glutamine, acetone, acetate, and choline) could perfectly discriminate OLK from OSCC cases. Thus, serum metabolomic biomarkers can accurately differentiate not only OSCC and normal subjects but also OLK and OSCC.59 Urine is a widely used biofluid for biomarker discovery and development because it is easy to collect by noninvasive methods. Urine sampling enables the monitoring of a wide variety of physiological processes and diseases,

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including cancer.21,61 Although the use of urine samples for metabolomics research is common, only one study has analyzed urine for metabolites from HNC patients. Xie and coworkers found differentially expressed metabolites that can clearly discriminate between OSCC, OLK, and healthy control samples. By using multivariate statistical analysis, the authors identified valine and 6-hydroxynicotic acid as important metabolites, which yielded 94.4% sensitivity, 98.9% accuracy, and 91.4% specificity, in their ability to distinguish OSCC from the controls. In addition, 6-hydroxynicotic acid, cysteine, and tyrosine metabolites were able to discriminate OSCC from OLK samples with 85.0% sensitivity, 89.7% specificity, and 92.7% accuracy60 (Table 5.3). However, additional independent studies are warranted to examine metabolites in urine samples that can distinguish HNC patients from premalignant or healthy controls.

Cell and tissue metabolomics Diagnosis of HNC from histopathological evaluation of a tissue biopsy is the gold standard, yet this can be subjective, and the criteria to classify benign, premalignant, and malignant lesions may be differentially interpreted. Metabolomic profiling data yields a signature set of functional metabolites that precisely characterize disease phenotypes and are more amenable to interpretation. Using 1H and 13C NMR studies, Bag and colleagues reported decreased levels of choline along with increased levels of its metabolic breakdown product, trimethylamine N-oxide, in OSCC biopsy tissues. In addition, lactate status remained unchanged in the OSCC group compared with controls, suggesting that the “Warburg effect” was not prominent in OSCC.62 Recently, the same group demonstrated that the levels of lipid metabolites, such as triglyceride, phosphatidylinositol, phosphatidylcholine, phosphatidylinositol bisphosphate, glycerophospholipid, and cytidine diphosphate diacylglycerol, were altered in oral submucous fibrosis (OSF) and OSCC cases as measured by conventional nano-LC-MALDI MS/MS. This indicated that altered lipid metabolism was associated with membrane biogenesis in OSF and OSCC.63 In contrast, Ogawa et al. demonstrated enhanced glucose consumption and lactate production in OSCC tissues as measured by CE-TOF-MS, indicating that the “Warburg effect” was prominent in OSCC. The study further found that the concentration of glucose, 3-phosphoglycerate (3PG), and 2phosphoglycerate (2PG) was significantly decreased in OSCC tissues, whereas lactate was increased in OSCC compared with normal tissues.64 Indeed, others have also shown lower levels of glucose in tumors compared with healthy control tissues.43 Musharraf and colleagues showed that out of 735 tissue metabolites, 31 metabolites distinguished oral cancer from precancerous and control samples. The results demonstrated that amino acid

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levels were downregulated in OSCC tissues, indicating increased energy metabolism and upregulation of biosynthetic pathways, which are required for cell proliferation in cancer tissues.65 Oral metabolites related to energy metabolism were also elevated in HNSCC as analyzed by LC-MS and GC-MS.34 A study using wax physisorptionebased FTIR imaging of healthy keratinocytes and cancer cells demonstrated that methylene (CH2) and methyl group (CH3) stretching vibrations in the range of 3000e2800 cm distinguished OSCC cells from healthy keratinocytes. This study concluded that wax physisorptionebased kinetic FTIR imaging can be used for early screening of oral cancer lesions66 (Table 5.4). Cancer cells can shift to alternate energy sources, including shifting from glycolysis or lactic acid fermentation to glutaminolysis.12,72e74 Glutaminolysis leads to a glutamine addiction in some cancer cell systems, and the oncogene Myc can coordinate the expression of genes necessary for glutamine catabolism.72 LC-MS analyses of HNSCC cell lines revealed that glucose rather than glutamine was the dominant energy source required for proliferation and survival of HNSCC cells.75 Nonetheless, cancer cells can adapt to alternate energy sources depending on their genetic profile and substrate availability.74,76,77 Recent findings by Kamarajan and colleagues demonstrated that highly active glutaminolysis is involved in primary and metastatic HNSCC tissues and cells; this was marked by high glutamate and low glutamine levels as analyzed by modern ultrahigh performance liquid chromatographyetandem mass spectroscopy (UPLC-MS/MS) and GC-MS.12 The study further showed that glutamate is an important marker of cancer metabolism, and glutamate regulation via glutaminase works in concert with aldehyde dehydrogenase to mediate cancer stemness12 (Table 5.3). Thus, these studies suggest that both glycolysis and glutaminolysis are critically important energy determinants in HNSCC; however, glucose metabolism may be more important for survival and proliferation, whereas glutamine/ glutamate metabolism may be essential for subsequent aggressive transitions, including acquisition of stemness properties and metastatic potential.

Conclusions and future perspectives There are several methods to screen for and diagnose HNC; however, some methods lack specificity and sensitivity. Metabolomic approaches are new tools in the clinical armamentarium that can help address this shortcoming. Metabolomic approaches can also help us better understand the complex processes that underlie HNC pathogenesis and progression from precancer to cancer. Researchers have made significant progress by incorporating genomic, transcriptomic, and proteomic datasets to study cancer8; adding metabolomic approaches will enhance the study of cancer. For instance, Sepiashvili et al. compared the genomic, transcriptomic, and proteomic

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Table 5.4 Summary of metabolites identified in tissues and cells of head and neck cancer (HNC) subjects. HNC type (sample)

Number of examined subjects

Metabolomic findings

Instrument used (reference)

HNSCC (tissues and cells)

In vitro: 19 HNSCC, 13 healthy, 3 metastatic cervical lymph nodes, and SCC cell line In vivo: 7 HNSCC and 7 healthy

Mean choline/creatine ratio was higher in HNSCC samples. Several amino acids including alanine, isoleucine, glutathione, histidine, valine, lysine, and polyamine were differentially found in HNSCC samples

1

H MRS67

HNSCC (tissues)

85 HNSCC and 50 healthy

Increased: taurine, choline, glutamic acid, lactic acid, lipid

1

OSCC (tissues)

159 OSCC (tumor and neighboring margins and bed tissues)

Increased: acetate, glutamate, lactate, choline, phosphocholine, glycine, taurine, leucine, isoleucine, valine, lysine, and alanine Decreased: creatine, polyunsaturated fatty acids

HR-MAS NMR68

HNSCC (tissues)

22 HNSCC (matched samples divided into 18NAT, 18 tumors, and 7 LN-Met)

HNSCC and LN-Met tissues showed elevated levels of lactate, amino acids, and decreased levels of triglycerides

HR-MAS 1H NMR69

OSCC (tissues)

32 OSCC and surrounding normal tissues

Increased: glucose metabolism (lactate, fumarate, malate), amino acid metabolism (glutamate, aspartate, glycine, proline, cysteine, hydroxyproline, creatinine, and putrescine) Decreased: glucose metabolism (glucose, 3-phosphoglycerate (3PG) and 2-phosphoglycerate (2PG)), amino acid metabolism (creatine)

CE-TOF-MS64

OSCC (tissues)

18 OSCC and 12 healthy

Increased: choline breakdown product, trimethylamine N-oxide, malonate Decreased: choline

1H and 13C NMR62

OSCC and OSF (tissues)

15 of each OSCC, OSF, and healthy

Differentially expressed lipid metabolites like triglyceride, phosphatidylinositol, phosphatidylcholine, glycerophospholipid, cytidine diphosphate diacylglycerol, and phosphatidylinositol bisphosphate were detected and differentiated OSCC and OSFWD from healthy

Nano-LC-MALDI MS/MS63

OSCC and OSF (tissues)

21 OSCC, 15 OSF, and 15 healthy

Amino acids decreased in OSF and OSCC compared with normal: alanine, glutamic acid, glutamine, glycine, lysine, norleucine, proline, serine, and threonine

GC/MS65

H MRS47

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Table 5.4 Summary of metabolites identified in tissues and cells of head and neck cancer (HNC) subjects.dcont'd HNC type (sample)

Number of examined subjects

Metabolomic findings

Instrument used (reference)

HNSCC (saliva and tissues)

7 HNSCC and 7 healthy control

23 differentially expressed metabolites and differentiated HNSCC from healthy controls. Increased: phosphate, lactate, b-alanine, a-hydroxyisovalerate, tryptophan, hexanoylcarnitine, TCA cycle analogs 2-hydroxyglutarate, and glycerol-3monophosphate

LC/MS/MS and GC/MS42

HNSCC (cells)

5 HNSCC cell lines, 3 primary normal human oral keratinocytes from patients

21 differentially expressed metabolites Increased: lactate, isoleucine, valine, alanine, glutamine, glutamate, aspartate, glycine, phenylalanine, tyrosine, choline-containing compounds, creatine, taurine, and glutathione Decreased: triglycerides

1

OSCC (cells)

2 cell lines (SCC15 and HSC-3) and 1 normal human oral keratinocytes

Increased methylene (CH2) and methyl group (CH3) stretching vibrations in the range of 3000e2800 cm

Wax physisorptione based kinetic FTIR imaging66

HNSCC (cells)

2 cell lines (HNSCC cells and stemlike cancer cells)

Changes in energy metabolism pathways: glycolysis and TCA cycle

Cap IC-MS71

H NMR70

profiles to delineate the molecular differences between HPVþ and HPV HNCs.78 The field of cancer metabolomics has gained attention and revealed new data related to cancer metabolic pathways after Warburg’s first hypothesis on cancer metabolism.57 The detection of novel metabolites has significantly increased with the advancement of modern MS and NMR technology. Thus, researchers can use published databanks for HNC that are derived from untargeted global metabolomic approaches to focus their targeting and mechanistic-based metabolomic studies.8 This review summarized and discussed the findings from metabolomic studies in different types of HNC and normal conditions (Fig. 5.2). Metabolomic data for HNC were arranged by sample types (saliva, blood, urine, tissues, and cell lines, Fig. 5.1A) and detection method (Fig. 5.1B). Recently, there has been significant attention to understanding the role of microbial metabolomics in different pathological conditions. Microbial communities in the human body exert dynamic functions, including playing roles in health and disease. Specifically, the gut and oral microbiota can influence the host response and promote disease. However, the influence of microbial metabolites on HNC has not been explored. This exploration may be important to

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Figure 5.2 Head and neck cancer (HNC) metabolism. A schematic overview of the differential expression of metabolites associated with carbohydrates, lipids, amino acids, and nucleotide metabolism altered in the HNC tumor microenvironment.

more fully understand the entirety of the human metabolome and all the factors that influence it.

Abbreviations Cap IC-MS CE-TOF/MS GC/MS 1 H-NMR HR-MAS 1 H-MRS HPLC MALDI-TOF-MS LC/GC NMR UPLC-Q-TOF-MS LN-Met

Capillary anion exchange ion chromatographyemass spectrometry Capillary electrophoresis time-of-flight mass spectrometry Gas chromatography/mass spectrometry Proton nuclear magnetic resonance High resolution magic angle spinning Proton magnetic resonance spectroscopy High-performance liquid chromatography Matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry Liquid chromatography/gas chromatography Nuclear magnetic resonance Ultra-performance liquid chromatography coupled with quadrupole/ time-of-flight spectrometry Lymph node metastasis

Acknowledgments This work was supported by a UCSF Chancellor’s award to YLK and a Department of Biotechnology (DBT), New Delhi, India, through DBT-Ramalingaswami fellowship project (BT/RLF/Re-entry/02/2014) to RK.

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Conflict of Interest Statement None.

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