emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery

Accepted Manuscript

Title: Emerging Field of Metabolomics: Big Promise forCancer Biomarker Identification and Drug Discovery

Author: Seema Patel Shadab Ahmed

PII: S0731-7085(14)00626-8DOI: http://dx.doi.org/doi:10.1016/j.jpba.2014.12.020Reference: PBA 9858

To appear in: Journal of Pharmaceutical and Biomedical Analysis

Received date: 25-9-2014Revised date: 7-12-2014Accepted date: 14-12-2014

Please cite this article as: S. Patel, S. Ahmed, Emerging Field ofMetabolomics: Big Promise for Cancer Biomarker Identification and DrugDiscovery, Journal of Pharmaceutical and Biomedical Analysis (2014),http://dx.doi.org/10.1016/j.jpba.2014.12.020

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Graphical Abstract1

Highlights3

Mass spectrometry, Nuclear magnetic resonance and chemometrics have enabled cancer biomarker 4

discovery.5

Metabolomics can non-invasively identify biomarkers for diagnosis, prognosis and treatment of cancer6

All major types of cancers and their biomarkers didcovered by metabolomics have been discussed.7

This review sheds light on the pitfalls and potentials of metabolomics with respect to oncology.8

Emerging Field of Metabolomics: Big Promise for Cancer Biomarker Identification and Drug Discovery10111213141516171819

Seema Patela* and Shadab Ahmedb2021

aBioinformatics and Medical Informatics Research Center, San Diego State University, San Diego 92182, USA22bInstitute of Bioinformatics and Biotechnology, Savitribai Phule Pune University, Pune 411007, India23

24252627282930313233

Short running title: Metabolomics for cancer target discovery3435

Key words: Metabolomics, cancer, biomarker, nuclear magnetic resonance, mass spectrometry36373839404142434445

*Corresponding author and address for correspondence:464748

Dr. Seema Patel49Bioinformatics and Medical Informatics Research Center50San Diego State University515500 Campanile Dr San Diego, CA 9218252Email: seemabiotech83@gmail.com53

545556575859606162

Abstract66

Most cancers are lethal and metabolic alterations are considered a hallmark of this deadly disease.67

Genomics and proteomics have contributed vastly to understand cancer biology. Still there are missing links as 68

downstream to them molecular divergence occurs. Metabolomics, the omic science that furnishes a dynamic 69

portrait of metabolic profile is expected to bridge these gaps and boost cancer research. Metabolites being the 70

end products are more stable than mRNAs or proteins. Previous studies have shown the efficacy of 71

metabolomics in identifying biomarkers associated with diagnosis, prognosis and treatment of cancer. 72

Metabolites are highly informative about the functional status of the biological system, owing to their proximity 73

to organismal phenotypes. Scores of publications have reported about high-throughput data generation by74

cutting-edge analytic platforms (mass spectrometry and nuclear magnetic resonance). Further sophisticated 75

statistical softwares (chemometrics) have enabled meaningful information extraction from the metabolomic 76

data. Metabolomics studies have demonstrated the perturbation in glycolysis, tricarboxylic acid acycle, choline 77

and fatty acid metabolism as traits of cancer cells. This review discusses the latest progress in this field, the 78

future trends and the deficiencies to be surmounted for optimally implementation in oncology. The authors 79

scoured through the most recent, high-impact papers archived in Pubmed, ScienceDirect, Wiley and Springer 80

databases to compile this review to pique the interest of researchers towards cancer metabolomics.81

Introduction96

Cancer is characterized by uncontrolled proliferation of cells and eventual transformation into 97

malignancy. It is a leading cause of death worldwide, and going by its alarming spread, it can amplify mortality 98

rate if not restrained. Cancer can develop in any part of the body, chiefly prostate gland, lungs, colon, breast, 99

brain, cervix, kidney, blood, liver, pancreas, ovary, uterus, skin, mouth, thyroids and testis. It is generally 100

triggered by xenobiotics viz. heterocyclic amines, genetic aberrations (microsatellite instability, somatic copy 101

number alterations), radiations, virus (hepatitis B virus, Epstein-Barr virus, papillomavirus) etc; however, many 102

unknown causes are still to be identified. The predominant mechanisms for cancer are impairment of DNA 103

repair pathways, the conversion of normal genes into oncogenes and the malfunction of tumour suppressor gene.104

Many gold standard clinical diagnostic and surveillance tests exist for screening cancer viz. biopsy, blood test, 105

blood cell count, image-based tests (computerized tomography (CT) scan, magnetic resonance imaging (MRI)),106

chest X-ray, liver function test, bone marrow test, mammography, colonoscopy and cystoscopy. Still, in some 107

cases cancer goes undetected (benign grade I tumour are low in sensitivity) and finally when identified108

(malignant stage), it becomes too late to treat. Further, the invasive procedures combined with high cost render109

it challenging diagnostic procedure. It necessitates superior detection strategies to improve prevent malignant 110

cancers. Identifying specific biomarkers to detect cancer at the onset to ensure patient survival appears very 111

promising.112

A cancer biomarker can be a metabolite (secreted by tumour, metabolic pathway or process) that may be 113

employed to diagnose cancer, predict patient response towards therapies and monitor recurrence. Though 114

proteins are the key markers, they can be as diverse as molecular, biochemical, physiological or anatomical [1].115

Markers can be employed for diagnosis (to identify early stage), prognosis (assess the lethality) and prediction 116

(of patient’s response to treatment) of cancer. The markers can be detected in body fluids (blood, urine, serum, 117

stool, saliva), or tissues (tissue samples or biopsies of the cancer). Recently, it has been shown that cancer 118

volatile organic compounds (VOC) markers can be detected in breath odour [2]. However, detecting the 119

markers is an intricate process. 120

Omic technologies have contributed vastly in this pursuit. Genomics, transcriptomics and proteomics 121

have been around since quite a long time now and have immensely aided to cancer research. The latest in this 122

league is metabolomics, the study of metabolites, which seems competent in cancer biomarker discovery [3]. 123

Among genome, transcriptome, proteome and metabolome, the latter is the closest representative of the 124

phenotype[4]. Metabolome being the end-product and most stable among all four functional levels mirrors the 125

activities of cell. Exploring the cancer metabolome seems to be an effective way to understand the phenotypic 126

changes associated with cancer. Screening biomarkers by recruiting an array of analytical techniques is being 127

emphasized[5]. Rather than a single metabolite, a pattern is believed to be more indicative of cancer status.128

Metabolomic approach makes it feasible to detect an array of metabolites in a single assay. The principal 129

analytical tools recruited for metabolome analysis are mass spectrometry (MS) and nuclear magnetic resonance 130

spectroscopy (NMR). MS can be coupled with a separation technique such as gas chromatography (GC-MS),131

liquid chromatography (LC-MS) or capillary electrophoresis (CE-MS) depending on the application. LC-MS 132

can be further ramified into high performance (HPLS-MS) or ultra performance (UPLC-MS). Also, MS can be 133

performed using different mass analysers (e.g. quadrupole (Q), matrix-assisted laser desorption/ionization 134

(MALDI), time of flight (TOF), magnetic sector, electrostatic) for the separation of ions depending on the type 135

of experiment. Variations of NMR include 1H-NMR and high resolution magic angle spinning (HR-MAS-136

NMR). Different approaches in metabolomics include (i) fingerprinting (the global screening for all detectable 137

metabolites from within the system under investigation) (ii) footprinting (analysis of metabolites from the 138

environment around the system under investigation which reveals information about metabolic exchange) (iii) 139

metabolome profiling (screening of a particular class of chemicals) (iv) flux analysis ( tracing of one compound, 140

usually isotope labelled carbon, through a particular pathway or set of pathways to determine the fate of the 141

compound) (v) target analysis (comparison of one or a few closely related target metabolites whose 142

concentrations may change depending on the experimental conditions). Data collection entails pre-processing 143

and subsequent statistical clustering. Univariate analysis is often deficient in discriminating metabolites between 144

complex systems. So, multivariate analysis such as principle component analysis (PCA), partial least square145

discriminant analysis (PLS-DA), orthogonal partial least square discriminant analysis (OPLS-DA), and 146

hierarchical clustering analysis (HCA) are used for complex data interpretation and subsequent visualization. 147

XCMS is a popular tool for processing MS data for metabolite profiling using nonlinear peak alignment, 148

matching, and identification[6]. MZmine 2 is a modular framework for processing, visualizing, and analyzing 149

MS-based molecular profile data[7]. SIMCA-P is implemented for multivariate analysis by principle component 150

analysis (PCA), partial least square (PLS) and orthogonal partial least square (OPLS) discriminant analysis[8]. 151

MetaboAnalyst is a web-based pipeline for metabolomic data processing, statistical analysis and subsequent 152

functional interpretation[9]. There exist a host of other tools to facilitate data analysis of spectrometric dataset.153

Many reviews shedding light on various aspects of this topic have been published in recent times.154

According to Spratlin et al[5], metabolome can serve as diagnostic and pharmacodynamic tool in oncology.155

Gupta and Chawla[10] reported that metabolomics expedite our comprehension of tumour biology by156

identifying diagnostic, prognostic and therapeutic targets. Abbassi-Ghadi et al.[11] reviewed the metabolomic 157

biomarkers in human oesophago-gastric cancer. Metabolites of glycolysis, tricarboxylic acid (TCA) cycle, 158

anaerobic respiration and protein/lipid metabolism were found to be significantly different between cancer and 159

control. Lactate and fumarate were the most commonly recognised cellular respiration biomarkers of cancer.160

Valine, glutamine and glutamate were the most commonly identified amino acid biomarkers, while saturated 161

and unsaturated free fatty acids, ketones and aldehydes and triacylglycerides were lipid metabolite 162

biomarkers[11]. Zhang et al.[12] reviewed how metabolomics can identify new prognostic and predictive 163

markers and discover new targets for future therapeutic interventions in breast cancer. Leung et al.[13] reviewed 164

the potential of MS for identification of novel diagnostic markers for ovarian cancer at the earliest to prolong 165

patient survival. Putri et al. [14] have discussed the latest applications of metabolomics in diverse fields, 166

including medical science. Wang et al.[15] reviewed the metabolomic techniques for hepatocellular carcinoma 167

biomarker discovery. Aboud and Weiss[16] reviewed several standard techniques for metabolite identification168

and drawbacks in the marker discovery. Vermeersch and Styczynski[17] reviewed role of metabolomics in 169

promoting cancer research. Khunger et al.[18] have discussed fundamental principles of cancer screening and 170

impediments in developing novel biomarkers. Struck-Lewicka et al.[19] reviewed on targeted and untargeted171

metabolomics of nucleosides (the RNA metabolites well known as potential cancer markers) and their analogues 172

(cis-diol metabolites) using HPLC-MS and discussed challenges in the pursuit. Zhang et al.[20] highlighted the 173

possible contribution of metabolomics to colorectal cancer diagnostic marker discovery. Armitage and 174

Barbas[21] discussed the metabolomic techniques employed for cancer biomarker discovery. Lu et al.[22]175

described LC-MS and GC-MS as analytical platforms for the comprehensive analysis of cellular metabolites, 176

polar as well as non-polar. Suzuki et al.[23] reviewed various MS techniques viz. LC-MS, GC-MS, CE-MS for 177

metabaolome-based biomarker discovery. Di Gangi et al.[24] reviewed the analytical technologies applied in 178

pancreatic cancer study and summarized the associated metabolites. Diagnostic marker potential of pteridines 179

(key cofactors in cell metabolism) in cancer, using HPLC and CE was reviewed [25]. 180

Overview on existing markers181

It is an established fact that cancer arises due to perturbation in metabolism, but the mechanisms 182

underlying the alterations leading to the malignancy is still poorly understood. Metabolism generates ROS, 183

which cause genetic mutations. Activated oncogenes and loss of tumour suppressors modify metabolism and 184

provoke aerobic glycolysis (Warburg effect). Together with glutamine, glucose via glycolysis supplies the 185

carbon skeletons, NADPH, and ATP to build new cancer cells, which persist in hypoxia. Excessive caloric 186

intake is associated with an increased risk for cancers, while caloric restriction is protective, perhaps through 187

clearance of mitochondria or mitophagy, thereby reducing oxidative stress. Hence, the links between 188

metabolism and cancer are multifaceted, spanning from the low incidence of cancer in large mammals with low 189

specific metabolic rates to altered cancer cell metabolism resulting from mutated enzymes or cancer 190

genes[26,27]. Bio-signature in the form of metabolite markers might throw light on this aspect and could solve 191

the puzzle.192

Many gene and protein-based biomarkers (more than 20 tumour markers are currently in use as per 193

National Cancer Institute) have been successfully used for cancer detection [28]. Markers detected in blood or 194

urine are α-fetoprotein (AFP), BCR-ABL, BRCA1/BRCA2, KIT, β-2-microglobulin (B2M), human chorionic 195

gonadotropin (HCG), bladder tumour antigen (BTA), CA 15-3, CA 19-9, CA 27-29, CA 125, calcitonin, 196

carcinoembryonic antigen (CEA), chromogranin A, immunoglobulins, free light chains, inhibin, lactate 197

dehydrogenase (LDH), neuron-specific enolase (NSE), NMP22, prostate-specific antigen (PSA), 198

thyroglobulin, soluble mesothelin-related peptides (SMRP), S-100 and prostatic acid phosphatase (PAP). 199

Markers detected in cancer tissue are anaplastic lymphoma kinase (ALK), BRAF oncogene (BRAFV600E), 200

epidermal growth factor receptor (EGFR), HER2 (or HER2/neu, erbB-2, or EGFR2), hormone receptors, 201

KRAS, S-100, Bmi-1, fibroblast activation protein (FAP) etc. A number of other markers are garnering 202

significant attention. Table 1. presents the list of conventional and emerging cancer markers.203

Roles of biomarkers in cancer therapy204

It is well known that cancer metabolism differs from that of normal tissue. Cancer cells rely on anaerobic 205

metabolism as the source for energy, even under physiological oxygen levels. Metabolomics could reveal novel206

cancer biomarkers that might expand our current understanding of the multi-factorial disease. The metabolome 207

consists of both endogenous (catabolised or anabolised by the biological system itself) and exogenous (extra-208

organism or extracellular) components. The best studied feature of cancer metabolism is central carbon 209

metabolism (CCM) and the relationship between glycolysis, the tricarboxylic acid (TCA) cycle and oxidative 210

phosphorylation. The major roles played by markers in facilitating cancer treatment are diagnosis, prognosis and 211

treatment. In several cancers, symptoms are subtle and nonspecific, which causes cancer to advance without 212

being detected. Early diagnosis of most cancer is life-saving, as observed in case of ovary, colon, kidney, gastric213

cancer (mostly for localised tumours) [29–32]. Cancer biomarkers can also be useful in establishing a specific 214

diagnosis, irrespective of tumours being of primary or secondary (metastatic) origin. Markers can predict the215

aggressiveness of an identified cancer as well as its likelihood of responding to the given treatment [33]. Serum 216

metabolomic profile could also be used for tumour staging (grading based on tumour size). Early identification 217

of metastases could be recommended for surgery, whereas more disseminated disease could be subjected to 218

palliative chemotherapy (when treatment is inadequate for major survival advantage)[34]. Markers can also be 219

used to determine the most effective treatment regime for a particular patient, paving the path for development 220

of personalized cancer care. Distinct genetic makeup of each patient results indifferent degree rate of drug 221

metabolism, requiring different drug dosing. So, metabolomics can provide knowledge regarding 222

pharmacodynamics and pharmacokinetics. Tumour cells undergoing apoptosis release specific cellular 223

components viz. cytochromes, nucleosomes, cleaved cytokeratin-18, E-cadherin that could be considered as 224

metrics of therapeutic efficacy. Also, aggressiveness of cancer and recurrence risks after surgical treatment 225

could be tracked. Halama et al. [35] suggested that metabolomics can be harnessed for finding specific 226

biomarkers of apoptosis for monitoring treatment efficacy (theranostics). Fig. 1. shows the potential drug targets 227

in cancer and role of metabolomics in diagnosis, prognosis and therapy.228

Possible markers revealed by metabolomics229

Head and neck230

Head and neck cancer usually develop in the squamous cells lining the inner mucosal surfaces of mouth, 231

salivary glands, tongue, nose, sinuses, throat, lymph nodes in the neck and larynx. Tiziani et al.[36] analyzed 232

blood samples of oral cancer patients and control using NMR. PCA and PLS-DA on the data showed a clear 233

discrimination between metabolites of the two groups. Pan et al.[37] exposed four brain cancer cell lines to 234

cisplatin drug and subjected to 1H-NMR. The result shed light on relation of glycosylated UDP compounds 235

(uridine diphospho-N-acetylglucosamine and uridine diphospho-N-acetylgalactosamine) to cancer cell death 236

following chemotherapeutic treatment. The glycosylated UDP compounds serve as substrates for glycosylation 237

of proteins and lipids, and thus they play considerable role in proliferation and malignant transformation.238

Tripathi et al.[38] employed 1H-NMR for metabolite profiling of head and neck squamous cell carcinoma. PCA 239

applied on the data showed a clear distinction between the cancerous and control (normal human oral 240

keratinocytes) tissue. Cancer cells exhibited significantly altered levels of various metabolites that clearly 241

revealed perturbation in metabolic events viz. Warburg effect, oxidative phosphorylation, energy metabolism, 242

TCA cycle, glutaminolysis, and hexosamine pathway, osmo-regulatory and antioxidant mechanism. Also, 243

significant alterations in the ratios of phosphatidylcholine/lysophosphatidylcholine and 244

phosphocholine/glycerophosphocholine, and elevated arachidonic acid level observed in cancer cells indicated 245

an altered membrane choline phospholipid metabolism. Significant increase in the activity of phospholipase A2246

observed in all the cancer cells confirmed an altered membrane choline phospholipid metabolism, which 247

projected cytosolic phospholipase A2 as a potential therapeutic target for anti-cancer therapy of head and neck 248

cancer. Nakamizo et al.[39] used GC-MS for metabolomic profiling of pre-operative cerebrospinal fluid samples 249

from patients with glioma. A total of 61 metabolites were identified, among which citric and isocitric acid levels 250

were significantly higher in the glioblastoma (the aggressive form of brain tumour whereas glioma is often a 251

benign primary brain tumour) samples than in the grades I, II, III glioma samples. Also, the lactic acid and 2-252

aminopimelic acid levels were relatively higher in the glioblastoma samples than in the grades I and II glioma 253

samples. The levels of the citric, isocitric and lactic acids in the cerebrospinal fluid were significantly higher in 254

grade I, II and III gliomas with mutant isocitrate dehydrogenase as compared to wild-type isocitrate 255

dehydrogenase. Yonezawa et al.[40] performed GC-MS of serum and tissue samples from squamous cell 256

carcinoma of the head and neck (HNSCC) patients. Metabolites related to the glycolytic pathway, such as 257

glucose were higher while some amino acids were lower in the sera of patients. However, the levels of many 258

metabolites related to the glycolytic pathway were lower in the tumour tissues than in non-tumourous tissues, 259

and the levels of several amino acids, such as valine, thyrosine, serine and methionine, were significantly higher.260

Lungs261

Lung cancer is among the leading causes of mortality worldwide. Gao et al.[41] investigated the 262

metabolic profile of patients with benign and malignant pulmonary nodules using a combination of GC-MS and 263

LC-MS. Chemometric analysis of the profiles by OPLS-DA resulted in 63 differential metabolites. Among 264

them, 48 metabolites were detected in both malignant and benign nodules, which imply common inflammatory265

pathways shared between the two groups. Fourteen metabolites viz. N-succinyl-2,6-diaminopimelate, 266

deoxycholic acid glycine conjugate, octanoylcarnitine, lysophosphatidylcholines, lysophosphatidylcholines, 267

phosphatidylcholines, phosphatidylserine and cholesteryl acetate distinguished malignant from benign nodules.268

Wu et al.[42] recruited a dansylated LC-MS to analyze variations of metabolites in pulmonary tumour and 269

normal tissues and reported abundance of dipeptides in the former. Wen et al.[43] used a combination of GC-270

MS and LC-MS to investigate the metabolic signatures in the plasma of stage-I human lung adenocarcinoma 271

patients and healthy controls. The OPLS-DA models reported 37 discriminatory metabolites between the two 272

groups. The fluctuated metabolites in human lung adenocarcinoma consisted of amino acids, lipids, fatty acids 273

and glutaminolysis (reactions by which glutamine is converted into glutamate, aspartate, CO2, pyruvate, lactate, 274

alanine and citrate). Also, significant decrease in the sulfate conjugate of sex hormones viz. testosterone, 275

androsterone, pregnenolone were observed in the plasma of patients. The development of resistance towards276

epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors remains a major hurdle in lung cancer277

therapy. Serizawa et al.[44] employed CE-TOF-MS to screen biomarkers for the early detection of resistance to 278

these inhibitors, using PC-9ER cells (drug erlotinib-resistant non-small-cell lung cancer cell). Enhanced 279

glutamine metabolism was observed in the cancer cells that can be explained by the multiple pathways for 280

bioenergetics and biosynthesis in proliferating cells. Surged glutamine metabolism might be considered as 281

marker to predict patient response to EGFR- tyrosine kinase inhibitors. Li et al.[45] performed UPLC-Q-TOF 282

MS on serum of lung cancer patient. Data clustering by PLS-DA resulted in differential metabolites involved 283

with the perturbation of lipid metabolism, including choline, free fatty acids, lysophosphatidylcholines.284

Breast285

Breast cancer is a heterogeneous disease with different subtypes and it develops through multi-step 286

carcinogenesis. Lefort et al.[46] used 1H-NMR to profile the effects of drugs and concluded that treatment of 287

cancer cells (MDA-MB-231 and MCF-7) with dichloroacetate (a pyruvate dehydrogenase kinase inhibitor) and 288

allopurinol (xanthine oxidase/dehydrogenase inhibitor) results in more pronounced changes in metabolites found 289

in extracellular medium than intracellular pools. An elevation in phosphocholine, total choline and 290

phosphocholine/glycerophosphocholine level was observed in cancer cells compared to control. Wei et al.[47]291

used LC-MS and 1H-NMR to generate serum metabolite profile and categorize breast cancer patients as 292

responders and non-responders to therapy. The concentrations of four metabolites, threonine, isoleucine, 293

glutamine, linolenic acid were significantly different in patients and controls when comparing response to 294

chemotherapy. The metabolites correctly identified 80% of the patients whose tumours did not show complete 295

response to chemotherapy. Shen et al.[48] analyzed plasma samples of breast cancer patients and controls. 296

Results showed that metabolomic profiles are influenced by race and tumour receptor status. Also, the levels of297

many amino acids were lower in the patients compared to controls, suggesting the rapid utilization of amino 298

acids in breast tumour metabolism. Metabolites related to fatty acid β-oxidation were significantly higher in 299

breast cancer patients than controls, signifying fast lipid metabolism. Mimmi et al.[49] used LC-ESI-MS and 300

1H-NMR for quantification of choline, phosphocholine and glycerophosphocholine in breast carcinoma tissues 301

and controls. These metabolites were found in abundance in the cancer tissues, as characteristics of other 302

cancers. Budczies et al.[50] investigated estrogen receptor positive (ER+) and estrogen receptor negative (ER-) 303

breast cancer tissues using GC-TOF-MS and detected 19 altered metabolites, which included β-alanine, 2-304

hydroyglutarate, glutamate, xanthine and glutamine. Beta-alanine demonstrated the strongest variation between 305

ER- and ER+ breast cancer. The findings corroborated the current view of ER+ breast cancer being different 306

from ER-, thus necessitating unique treatment strategies. Further, the metabolomics analysis revealed that ER-307

subtype is the favoured target for glutaminase inhibitors. Jobard et al.[51] conducted 1H-NMR to monitor308

metabolite fluctuation in the serum of advanced metastatic breast cancer compared to localized early stage 309

tumour. The 9 significant discriminatory metabolites were histidine, acetoacetate, glycerol, pyruvate, 310

glycoproteins (N-acetyl), mannose, glutamate and phenylalanine. Corona et al.[52] performed MS-based serum 311

metabolomic profiling of elderly breast cancer. Further, ANOVA was used to identify the significant 312

metabolites from amino acids, acylcarnitines, sphingo- and glycerol-phospolipid class. In frail patients, the 313

perturbations of serine, tryptophan, hydroxyproline, histidine, its derivate 3-methyl-hystidine, cystine, and β-314

aminoisobutyric acid was observed. Also, in this phenotype, decrease lowered level of glycerol- and sphingo-315

phospholipid metabolites were observed. Qiu et al.[53] used GC-TOF-MS to analyze colorectal cancer biopsies.316

Differential metabolites were identified and evaluated as potential prognostic markers. A panel of 15 317

significantly altered metabolites was identified, which demonstrates the ability to predict the rate of recurrence 318

and survival for patients after surgery and chemotherapy. 319

Oesophagus and stomach320

Stomach cancers are a leading cause of mortality worldwide. Xu et al.[54] studied plasma of oesophageal 321

squamous cell carcinoma patients using LC-MS. Clustering with PCA and PLS-DA revealed the significant 322

plasma metabolites in the patients to be lysophosphatidylcholines, fatty acids, L-carnitine, acylcarnitines, 323

organic acids, and a sterol metabolite. Also, variation in metabolites between pre- and post-treatment patients 324

was observed. Zhang et al.[55] used 1H-NMR coupled to UHPLC for characterizing the metabolic turbulence325

caused by oesophageal cancer. OPLS-DA identified 19 metabolites as potential biomarkers. The pathways 326

undergoing perturbation in cancer patients were lipid metabolism, amino acid metabolism, glycolysis, 327

ketogenesis, TCA cycle and energy metabolism. Wang et al.[56] investigated the metabolites in human 328

oesophageal cancer tissues and normal esophageal mucosa using 1H-NMR followed by PCA, PLS-DA and 329

OPLS-DA. Down-regulation of glucose, adenosine monophosphate (AMP), NAD and up-regulation of formate 330

yet again confirmed the great energy necessity by oesophageal cancer. The amplification in acetate, short-chain 331

fatty acid and gamma aminobutyric acid (GABA) in oesophageal cancer tissue pointed towards fatty acids 332

metabolism required for cellular membrane formation. Other altered metabolites in oesophageal cancer were 333

associated with choline metabolic pathway.334

Bile duct, pancreas and liver337

Cancers of bile duct, pancreas and liver are very challenging to treat. Wen et al.[57] subjected the bile 338

collected from biliary cancer patients to NMR followed by OPLS-DA. Results showed good distinction between 339

cancer and benign groups. The diagnostic ability of this method proved better than the conventional markers 340

(CEA, CA19-9 and bile cytology).341

He et al.[58] used HR-MAS-NMR to profile xenograft pancreatic cancer (SW1990 cell implanted in 342

nude mice) before and after radiotherapy. PCA selected metabolites of normal pancreas, pancreatic tumour343

tissues, and radiation- treated pancreatic tumour tissues were compared. The levels of choline, taurine, alanine, 344

isoleucine, leucine, valine, lactate and glutamic acid in the pancreatic cancer group increased, whereas,345

phosphocholine, glycerophosphocholine, and betaine levels were found to be decreased. The ratio of 346

phosphocholine to creatine, and glycerophosphocholine to creatine showed remarkable decrease in the 347

pancreatic cancer group. PCA results showed that the levels of choline and betaine were decreased with the 348

increased radiation dose, whereas, the level of acetic acid was dramatically increased. Yabushita et al.[59] used 349

transgenic rats bearing pancreatic ductal adenocarcinoma and control transgenic rats with normal pancreatic 350

tissues for metabolomic analysis of serum and pancreatic tissue by non-targeted as well as targeted GC-MS and 351

by microarray-based transcriptomic analysis. Results showed that the decreased serum levels of palmitoleic acid 352

in rats with pancreatic ductal adenocarcinoma was due to the decrement in pancreatic tissue and suggested 353

deeper investigation into biomarker potential of palmitoleic acid. Kobayashi et al.[60] analyzed the sera from 354

pancreatic cancer patients and healthy volunteers by GC-MS followed by statistical analyses. The diagnostic 355

model constructed seemed promising for improving the prognosis of pancreatic cancer via its early detection 356

and accurate discrimination from chronic pancreatitis. Leichtle et al.[61] analyzed serum samples of pancreatic 357

carcinoma patients, pancreatitis patients and controls using MS and statistics. The result indicated that multi-358

marker models are better than conventional marker CA19-9 (carbohydrate antigen 19-9) in differentiating 359

between the different groups. Ritchie et al.[62] employed a non-targeted metabolomics approach based on flow-360

injection Fourier transform ion cyclotron resonance mass spectrometry (FI-FTICR-MS) to generate 361

comprehensive metabolomic profiles of sera from pancreas cancer patients and control. The metabolome of 362

pancreas cancer patients was significantly altered with decrease in metabolites like 36-carbon ultra long-chain 363

fatty acids, multiple-choline-related systems including phosphatidylcholines, lysophosphatidylcholines and 364

sphingomyelins, as well as vinyl ether-containing plasmalogen ethanolamines. Metabolite biomarkers PC-594365

(an ultra-long-chain fatty acid) appeared promising in diagnosis of candidates at higher risk of developing366

pancreas cancer. 367

Xiao et al.[63] compared metabolite levels in sera of hepatocellular carcinoma and cirrhosis patients by 368

UPLC-Q-TOF-MS. After statistical analysis, the discriminating candidate biomarkers were found to be369

glycholic acid, glycodeoxycholic acid, 3β, 6β-dihydroxy-5β-cholan-24-oic acid, oleoyl carnitine and the peptide 370

Phe-Phe. Chen et al.[64] used UHPLC-QQQ-MRM-MS-based pseudo-targeted method to discover serum 371

biomarkers for patients with hepatocellular carcinoma. The metabolite lysophosphatidylcholine, medium-chain 372

acylcarnitines and branched-chain amino acid levels were found decreased; whereas the metabolite long-chain 373

acylcarnitines and aromatic amino acid levels were found elevated in the patients. Beyoglu et al.[65] combined 374

transcriptomics and GC-MS datasets to examine energy metabolism in tumours and non-tumorous tissues from 375

the liver of same hepatocellular carcinoma patients. The tumour was characterized by about 2-fold depletion of 376

glucose, glycerol 3- and 2-phosphate, malate, alanine, myo-inositol, and linoleic acid. Data were consistent with 377

a metabolic remodelling involving a 4-fold increase in glycolysis over mitochondrial oxidative phosphorylation.378

Liu et al.[8] used UPLC-Q-MS characterize metabolites in hepatitis B virus-related hepatocellular carcinoma. 379

Clustering identified 14 discriminating metabolites viz. L-phenylalanine, glycerophosphocholine, 380

lysophosphatidylcholines, lysophosphatidylethanolamines and chenodeoxycholic acid glycine conjugate, β-381

sitosterol, quinaldic acid, arachidyl carnitine, tetradecanal, and oleamide. Zhang et al. [66] used UPLC-Q-TOF-382

HDMS for metabolomic profiling of glycocholic acid from urine samples of hepaocellular carcinoma patients. A383

metabolite network followed by biochemical analyses of urine sample revealed that glycocholic acid expression 384

gets upregulated hepatocellular carcinoma. Pathway analysis suggested the modulation of multiple vital 385

physiological pathways, including primary bile acid biosynthesis, secondary bile acid biosynthesis, metabolic 386

pathways, and bile secretion. The in silico network generation enhanced the interpretation and understanding of 387

mechanisms for glycocholic acid. Clarke et al.[67] monitored the changes in the expression of hepatocellular 388

carcinoma-related genes and metabolite profiles in non-alcoholic fatty liver disease progression. Results 389

indicated an overlap in the pathogenesis of non-alcoholic fatty liver disease and hepatocellular carcinoma where 390

several classes of oncogenic genes and metabolites were altered in the former. Wnt signalling and several 391

metabolites were different, which indicated that the perturbed genes and metabolites could be mediators in the 392

transition from non-alcoholic steatohepatitis to hepatocellular carcinoma. Bowers et al.[68] conducted 393

metabolomic profling of serum samples from patients with hepatitis C virus and the virus with hepatocellular 394

carcinoma using HPLC-MS. Application of PLS-DA on the data revealed discriminatory metabolites. Further 395

the combined application of NMR and LC-MS in followed by random forest statistical analysis could find the 396

differential metabolites. Perturbations in the synthesis of ketone bodies, citrate cycle, phospholipid metabolism, 397

sphingolipid metabolism, fatty acid oxidation, amino acid catabolism and bile acid metabolism was observed in 398

hepatocellular carcinoma patients as compared to controls[69].399

Kidney and bladder400

Bladder cancer is the most common malignancy of the urinary system. Pasikanti et al.[70] conducted 401

GC-GC-TOF-MS for metabotyping of urine from bladder cancer and control. OPLS-DA resulted in 46 402

metabolites associated with the cancer. Alteration of the tryptophan-quinolinic acid level in the patient urine 403

suggested the potential roles of kynurenine (metabolite of tryptophan) in the malignancy and possible 404

therapeutic strategy. Tripathi et al.[71] subjected tissue samples from bladder cancer patient and control to HR-405

MAS-NMR and subsequent PLS-DA. Fifteen metabolites showed significant difference between the types of 406

tissues. Alberice et al.[72] conducted LC-MS and CE-TOF-MS on urine samples from urothelial bladder cancer 407

patients. Twenty-seven discriminatory metabolites were obtained which included histidine, phenylalanine, 408

tyrosine and tryptophan. Perturbed level of urinary nucleosides are considered hallmark of urinogenital cancer. 409

In this regard, the nucleosides were quantified using CE-MS and their significance assessed by PCA and PLS-410

DA [73]. LC-QQQ-MS of urogenital cancer patient urine sample revealed 5 nucleosides makers viz. 6-411

methyladenosine, inosine, N-2-methylguanosine, 3-methyluridine and N,N-dimethylguanosine [74].412

Pterins are nitrogen heterocylic compounds with 2-amino-4-hydroxypiridine core, characterized to play 413

role as cofactors in cell metabolism. Possible biomarker roles of pterin compounds in bladder cancer urine 414

samples were investigated using reverse-phase HPLC. Four pterins viz. neopterin, pterin-6-carboxylic acid, 415

biopterin and isoxanthopterin were detected in higher level in cancer patients. [75]. Though the elevation in 416

concentration of these metabolites was not statistically-significant, it might pave way for further verification of 417

the efficacy.418

Ovary, uterus and cervix419

Gynecologic cancers develop in female reproductive system and can be cervical, ovarian, uterine, vaginal 420

and vulvar. Zhang et al.[76] performed UPLC-Q-TOF-MS on urine samples from epithelial ovarian cancer421

patients, including nucleotide metabolism (pseudouridine, N4-acetylcytidine), histidine metabolism (L-histidine, 422

imidazol-5-yl-pyruvate), tryptophan metabolism (3-indolelactic acid) and mucin metabolism (3'-sialyllactose 423

and 3-sialyl-N-acetyllactosamine). The metabolites N4-acetylcytidine, pseudouridine, urate-3-ribonucleoside, 424

and succinic acid differed significantly between pre-operation and post-operation group and hold promise as 425

biomarkers. Vettukattil et al.[77] measured metabolic differences between ovarian serous carcinoma effusions 426

(common manifestation of advanced cancer) obtained pre- and post-chemotherapy. HR-NMR followed by PCA 427

and PLS-DA showed elevated levels of ketones (aceto-acetate and β-hydroxybutyrate) and lactate in the 428

carcinoma.429

Chen et al.[78] used LC-MS followed by multivariate statistics to identify potential biomarkers in uterine 430

cervix cancer. Nine potential biomarkers associated with the cancer were identified viz. proline betaine, 431

pyridylacetic acid, urocanic acid, 1-methylguanine, uric acid, methylxanthine, tryptophan, theophylline, and 432

carnitine.433

Prostate434

Prostate cancer is the second leading cause of cancer death in men. Zhou et al[79] employed ESI-MS and 435

chemometric tools PCA and HCA to identify lipid biomarkers in plasma of prostate cancer patients. It was 436

reported that three out of 13 lipid classes, phosphatidylethanolamine, ether-linked phosphatidylethanolamine 437

and ether-linked phosphatidylcholine possessed biomarker potential. Raina et al.[80] performed 1H-NMR on 438

prostate tissue extracts from transgenic adenocarcinoma of the mouse prostate (TRAMP) model. It showed that 439

inositol hexaphosphate (IP6) significantly decreased glucose metabolism and membrane phospholipid synthesis, 440

in addition to raising myoinositol levels in the prostate. Oral IP6 supplement blocked growth and angiogenesis 441

of prostate cancer in the TRAMP model, depriving the tumour of energy. The inhibitory potential of IP6 in 442

towards prostate cancer could be explored further. Giskeodegrad et al.[81] performed metabolic profiling of 443

prostate cancer tissue by aid of HR-MAS-NMR. Data analysis by PLS, PLS-DA and absolute quantification 444

(LCModel) were used to predict cancer aggressiveness. High grade cancer tissue differed from low grade cancer445

tissue in lower level of spermine and citrate. McDunn et al.[82] analyzed malignant and normal prostate tissue446

using GC-MS and UHPLC-MS/MS. HCA identified the metabolites associated with cancer aggressiveness. 447

Prostate tumours had significantly altered metabolite profiles compared to cancer-free prostate tissues. The 448

discriminatory metabolites were associated with cell growth, energetics, stress, and loss of prostate-specific 449

biochemistry. Aggressive prostate tumours could be further categorized by metabolites viz. NAD+ and 450

kynurenine. 451

Colon452

Colorectal cancer is one of the most prevalent cancers and a major cause of morbidity and mortality.453

Effective screening can facilitate timely detection and result in a better clinical outcome. Jimenez et al.[83]454

applied HR-MAS-NMR to analyze metabolites in tumour samples and adjacent mucosal tissues excised from 455

colorectal cancer patients. OPLS-DA of metabolic profiles showed biochemical discrimination between the two 456

types of tissues. Taurine, isoglutamine, choline, lactate, phenylalanine, tyrosine (increased concentrations in 457

tumour tissue) together with lipids and triglycerides (decreased concentrations in tumour tissue) were the most 458

discriminant metabolites between the two groups. Also, the metabolic profiling could distinguish between 459

tumours of different T- and N-stages (T stage has greater weight than the N stage and the former affects survival 460

more significantly) according to tumour node metastasis (TNM) stage classification. It was observed that 461

adjacent mucosa (10 cm from the tumour margin) harbours unique metabolites for telling apart from T- to N-462

stage and predicting 5-year survival, more reliably than tumours. Holst et al.[84] compared glycosylation 463

profiles of colorectal tumour tissues and corresponding control tissues of patients. Using MALDI-TOF-MS and 464

2D-LC-MS/MS, enzymatically-released and 2-aminobenzoic acid-labelled glycans from glycosphingolipids 465

were characterized. PCA and PLS-DA revealed significant differences between tumour and corresponding 466

control tissues. Anomalous glycosylations in colorectal tumours were observed. The obtained discriminating 467

glycans (various degrees of fucosylation, acetylation, sulfation sialylation etc.) can play role as biomarkers to 468

improve diagnostics and therapy. Tan et al.[85] profiled serum metabolites from newly diagnosed colorectal 469

carcinoma patients as well as healthy subjects using GC-TOF-MS and UPLC-Q-TOF-MS. A distinct metabolic 470

signature involving TCA cycle, urea cycle, glutamine, fatty acids, and gut flora metabolism was observed in the 471

patients. Differential metabolites were identified with OPLS-DA. 2-hydroxybutyrate, 2-oxobutyrate and 2-472

aminobutyrate level was higher in the patients that indicated higher ROS generation in the patients. 5-473

hydroxytryptamine, N-acetyl-5-hydroxytryptamine, indoxyl, and indoxyl sulfate were found significantly 474

lowered in the patients. Phua et al.[86] conducted metabolomic analysis of faeces to detect colorectal cancer. 475

GC-TOF-MS based metabolic profiling of the samples from patients and healthy subjects showed significant 476

distinction. OPLS-DA revealed the discriminatory marker metabolites to be fructose, linoleic acid and nicotinic 477

acid. Manna et al.[87] performed metabolic profiling of colon tumour and adjacent tissues as well as urine 478

samples from cancerous mice and patients. The discriminatory metabolites were found to be proline, threonine, 479

glutamic acid, arginine, N1-acetylspermidine, xanthine, uracil, betaine, symmetric dimethylarginine and480

asymmetric dimethylarginine. These altered metabolites were detected to be the products of polyamine 481

metabolism, nucleic acid metabolism and methylation.482

Leukaemia483

Wang et al.[88] used UPLC/Q-TOF-MS to study the effects of an acridone derivative-based anti-tumour484

compound on human immature T-cell line (CCRF-CEM). PCA and OPLS-DA identified distinct metabolites 485

involved in glutathione (GSH) and glycerophospholipid metabolism pathways. Glutathione level and the 486

reduced/oxidized glutathione ratio were significantly decreased in the treated cells, while L-cysteinyl-glycine 487

and glutamate were greatly increased. In glycerophospholipid metabolism, cell membrane components 488

phosphatidylcholines were decreased in the treated cells, while the oxidative products lysophosphatidylcholines 489

were significantly increased. The ROC and lipid peroxidation product malondialdehyde (MDA) were notably 490

increased, accompanied with decrease of mitochondrial trans-membrane potential, release of cytochrome C and 491

activation of caspase-3, indicating oxidative stress-mediated apoptosis in the leukaemia cells. Acute myeloid 492

leukaemia is a fatal form of cancer. Wang et al.[89] analyzed the serum metabolites in acute myeloid leukaemia 493

patients and healthy controls using 1H-NMR spectroscopy in conjunction with multivariate data analysis. 494

Significant metabolite differences were observed due to perturbation in glycolysis/gluconeogenesis, TCA cycle, 495

biosynthesis of proteins and lipoproteins, and metabolism of fatty acids and cell membrane components, 496

especially choline and its phosphorylated derivatives.497

Myeloma498

Lodi et al.[90] performed NMR analysis of blood serum and urine samples from multiple myeloma 499

patients and detected metabolites associated with diagnosis, post-treatment remission and disease progression. 500

Carnitine and acetylcarnitine emerged as novel biomarkers of diagnosis and relapse. Puchades-Carrasco et 501

al.[91] used 1H-NMR for metabolic profiling of multiple myeloma patient serum. Patient at diagnosis exhibited 502

higher levels of isoleucine, arginine, acetate, phenylalanine, tyrosine and lower levels of 3-hydroxybutyrate, 503

lysine, glutamine, and some lipids compared with the control set. Serum of patients at remission displayed a 504

metabolic profile different from that in diagnosis stage, indicating patient had responded to drug.505

Melanoma506

Abaffy et al.[92] used an untargeted approach to compare volatile metabolomic signature of melanoma507

and matched control non-neoplastic skin from the same patient. VOC from tissues were extracted and analyzed 508

by GC-MS. XCMS and MetaboAnalyst were used to find differentially expressed metabolic features. The 509

analysis revealed increased levels of lauric acid and palmitic acid in melanoma, assumed to be due to enhanced510

de novo lipid synthesis (a characteristic of cancer, caused due to increased oxidative stress). Increased oxidative 511

stress is likely to cause the spike in lauric acid. Feng et al.[93] employed HR-MAS-NMR as well 1H-NMR, 512

followed by PCA to investiagte secondary metastatic B16-F10 melanoma in C57BL/6J mouse liver. Absolute 513

concentrations of glutamate, creatine, fumarate and cholesterol were elevated in the melanoma group as 514

compared to control, while the absolute concentrations of succinate, glycine, glucose, and the family of linear 515

lipids including long chain fatty acids, total choline and acyl glycerol were decreased. The ratio of 516

glycerophosphocholine to phosphocholine is increased by about 1.5-fold in the melanoma group, while the 517

estimate of absolute concentration of total choline is lower in melanoma mice. Linear lipid levels were518

decreased by β-oxidation in the melanoma group, which contributes to an increase in the synthesis of cholesterol519

and also serves as energy source for TCA cycle. These findings suggest a correlation between lipid oxidation, 520

TCA cycle and the hypoxia-inducible factors signal pathway in tumour metastases. Fedele et al[94] subjected 521

B16F10 melanoma tumour cells to 1H-NMR for metabolite profiling. The result showed the relationship 522

between the metabolites involved in energy metabolism, apoptosis and proliferation. Among the metabolites, 523

lactate, aspartate, glycerol, lipids, alanine, myo-inositol, phosphocholine, choline, acetate, creatine and taurine 524

showed differential expression. Choline and creatine were shown to be closely related with tumour progression. 525

From the above studies, it can be inferred that metabolomics is effective non-invasive technique for melanoma 526

biomarker discovery.527

Hurdles and future directions528

Metabolic phenotyping has furnished crucial biomarker findings, but replicating them with different 529

sample sets is cumbersome, often futile due to un-uniformity in analytical and clinical protocols used in the 530

studies [4,95]. The same metabolites can also be high in some non-cancerous conditions such as thyroid disease, 531

rheumatoid arthritis, inflammatory bowel disease, pancreatitis and liver diseases. So, markers might tend to 532

mislead diagnosis, prognosis and therapy. The Cancer Genome Atlas (TCGA) is a database of cancer genomics 533

and proteomics, a resource to understand tumour initiation and progression. However, metabolomic information 534

has not been incorporated into it. Tang et al.[96] suggest the addition of metabolomic profiles to the public 535

TCGA. This dataset might provide insight on tumour biology. Strengthened with metabolic flux analyses 536

(isotope tracers and mathematical modelling) metabolomics can serve as an excellent tool for biomarker 537

discovery. Cross metabotyping through different analytical devices could validate result. This strategy has been 538

adopted in order to obtain high-fidelity markers. Integration of two highly sensitive and complementary539

metabolomics platforms could enable a comprehensive metabolic profiling and assist in discrimination 540

malignant from benign tumours[41].541

Conclusions 542

Robust, low-cost and non-invasive biomarkers to facilitate screening, surveillance and therapy 543

monitoring are scanty, necessitating the development of more effective modalities. In this regard, metabolomics 544

seems highly promising. Biomarkers detected by analytical platforms give an insight into cancer biology and545

tapped properly, metabolomics might open new avenues for diagnostics and drug discovery. It relies on the 546

analysis of the cancer metabolome to identify marker metabolites. By identifying the key metabolic pathways 547

which elaborated the significant metabolites, potential targets for cancer therapy could be developed. However, 548

the issues with the specificity and sensitivity for their detection must be dealt with, in order to unleash full 549

potential of metabolomics. The appreciation of metabolomics in the realm of cancer research domain has just 550

started, tremendous amount of work remains to be done by thwarting bottlenecks.551

Conflict of interest statement552

The authors declare there is no conflict of interest in submission of this manuscript to this journal.553

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Fig. 1. Potential drug targets in cancer and role of metabolomics in diagnosis, prognosis and therapy863

Table. Conventional cancer biomarkers and nvel biomarkers discovered by metabolomics864865

Cancer Coventional markers Novel markers from metabolomics study Metabolomic tool used

Reference

Breast BRCA1CA 15-3, CEAHER2 (HER2/neu, erbB-2, EGFR2)ER/PR

Phosphocholine, total choline, glycerophosphocholineThreonine, isoleucine, glutamine, linolenic acidβ-alanine, 2-hydroyglutarate, glutamate, xanthine and glutamineHistidine, acetoacetate, glycerol, pyruvate, glycoproteins (N-acetyl), mannose, glutamate and phenylalanine

1H-NMRLC-MSLC-ESI-MSGC-TOF-MS

Lefort et al.[45]Wei et al.[46]Mimmi et al.[48]Jobard et al.[50]

Colorectal CEA, CA 19-9EGFRBRAFUGT1A1KRAS

Taurine, isoglutamine, choline, lactate, phenylalanine, tyrosine, lipids and triglyceridesGlycosylationsTCA cycle, urea cycle, glutamine, fatty acids, and gut flora metabolism2-hydroxybutyrate, 2-oxobutyrate and 2-aminobutyrate5-hydroxytryptamine, N-acetyl-5-hydroxytryptamine, indoxyl, and indoxyl sulphateLinoleic acid and nicotinic acidProline, threonine, glutamic acid, arginine, N1-acetylspermidine, xanthine, uracil, betaine, symmetric dimethylarginine andasymmetric dimethylarginine

HR-MAS-NMRMALDI-TOF-MS 2D-LC-MS/MSGC-TOF-MS UPLC-Q-TOF-MS

Jimenez et al.[79]Holst et al.[80]Tan et al.[81]Phua et al.[82]Manna et al.[83]

Gastric (oesophagus, GIST)

HER2 (HER2/neu, erbB-2 or EGFR2)c-kit

Lysophosphatidylcholines, fatty acids, L-carnitine, acylcarnitinesLipid metabolism, amino acid metabolism, glycolysis, ketogenesis, TCA cycle and energy metabolismGlucose, adenosine monophosphate (AMP), NAD and up-regulation of formate

LC-MS1H-NMR UHPLC

Xu et al.[53]Zhang et al.[54]Wang et al.[55]

Head, neck Bmi-1FAP

Warburg effect, oxidative phosphorylation, energy metabolism, TCA cycle, glutaminolysis, hexosamine pathway, osmo-regulatory and antioxidant mechanismPhosphatidylcholine/lysophosphatidylcholine Phosphocholine/glycerophosphocholineArachidonic acidLactic acid and 2-aminopimelic acidCitric acid, isocitric acidValine, thyrosine, serine and methionine

1H-NMRGC-MS

Tiziani et al.[35]Pan et al.[36]Tripathi et al.[37]Nakamizo et al.[38]Yonezawa et al.[39]

Kidney and bladder

BTANMP22

Tryptophan-quinolinic acidHistidine, phenylalanine, tyrosine and tryptophan

GC-GC-TOF-MSHR-MAS-NMRLC-MS CE-TOF-MS

Pasikanti et al.[69]Alberice et al.[71]

Leukaemia BCR-ABLB2MCD20CD30PDGFR

Glutathione L-cysteinyl-glycine, glutamate Phosphatidylcholines, lysophosphatidylcholines

UPLC/Q-TOF-MS

Wang et al.[84]Wang et al.[85]

Lungs (NSCLC,mesothelioma)

PAPSMRPALKKRASEGFR

N-succinyl-2,6-diaminopimelate, deoxycholic acid glycine conjugate, octanoylcarnitine, lysophosphatidylcholines, lysophosphatidylcholines, phosphatidylcholines, phosphatidylserine and cholesteryl acetate, dipeptidesGlutamine metabolismCholine, free fatty acids, lysophosphatidylcholines.

GC-MS LC-MSCE-TOF-MSUPLC-Q-TOF MS

Gao et al.[40]Wu et al.[41]Serizawa et al.[43]Li et al.[44]

Melanoma S-100BRAF

Lauric acid and palmitic acidGlutamate, creatine, fumarate, cholesterolSuccinate, glycine, glucose, long chain fatty acids, total choline,acyl glycerolGlycerophosphocholine, phosphocholineLactate, aspartate, glycerol, alanine, myo-inositol, phosphocholine, acetate, taurine

HR-MAS-NMR

Abaffy et al.[88]Feng et al.[89]Fedele et al[90]

Multiple myeloma

ImmunoglobulinsFree light chainsPAP

Carnitine, acetylcarnitine Isoleucine, arginine, acetate, phenylalanine, tyrosine, 3-hydroxybutyrate, lysine, glutamine

1H-NMR Lodi et al.[86]Puchades-Carrasco et al.[87]

Ovary, uterus, cervix

CA 125HE-4Inhibin

Nucleotide, histidine, tryptophan and mucin metabolism N4-acetylcytidine, pseudouridine, urate-3-ribonucleoside, and succinic acidKetones and lactateProline betaine, pyridylacetic acid, urocanic acid, 1-methylguanine, uric acid, methylxanthine, tryptophan, theophylline, carnitine

UPLC-Q-TOF-MSHR-NMRLC-MS

Zhang et al.[72]Vettukattil et al.[73]Chen et al.[74]

Pancreas and liver

CA 19-9CEAAFP

Choline, taurine, alanine, isoleucine, leucine, valine, lactate and glutamic acidPhosphocholine, glycerophosphocholine, betainepalmitoleic acidPhosphatidylcholines, lysophosphatidylcholines and

NMRHR-MAS-NMRGC-MSUPLC-Q-

Wen et al.[56]He et al.[57]Yabushita et al.[58]Ritchie et al.[61]Xiao et al.[62]

sphingomyelinsGlycholic acid, glycodeoxycholic acid, 3β, 6β-dihydroxy-5β-cholan-24-oic acid, oleoyl carnitine, peptide Phe-PheMedium-chain acylcarnitines and branched-chain amino acidlong-chain acylcarnitines and aromatic amino acidGlucose, glycerol 3- and 2-phosphate, malate, alanine, myo-inositol, and linoleic acidL-phenylalanine, glycerophosphocholine, chenodeoxycholic acid Glycine conjugate, β-sitosterol, quinaldic acid, arachidyl carnitine, tetradecanal oleamideSynthesis of ketone bodies, citrate cycle, phospholipid metabolism, sphingolipid metabolism, fatty acid oxidation, amino acid catabolism and bile acid metabolism

TOF-MSUHPLC-QQQ-MRM-MS

Chen et al.[63]Beyoglu et al.[64]Liu et al.[8]Bowers et al.[67]Lit et al. [68]

Prostate PSA Phosphatidylethanolamine,Ether-linked phosphatidylethanolamine Ether-linked phosphatidylcholineNAD+ and kynurenineSpermine and citrate

ESI-MS1H-NMRHR-MAS-NMRGC-MS and UHPLC-MS/MS

Zhou et al[75]McDunn et al.[78]Raina et al.[76]Giskeodegrad et al.[77]

866867868869870871872873874

Fig 1.874

emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery

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