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International Journal of Clinical and Experimental Medicine logoLink to International Journal of Clinical and Experimental Medicine
. 2015 Jul 15;8(7):11052–11063.

Metabolomics in bladder cancer: a systematic review

Yidong Cheng 1,*, Xiao Yang 1,*, Xiaheng Deng 1,*, Xiaolei Zhang 1, Pengchao Li 1, Jun Tao 1, Chao Qin 1, Jifu Wei 2, Qiang Lu 1
PMCID: PMC4565288  PMID: 26379905

Abstract

Bladder cancer (BC) is the most common urological malignancy. Early diagnosis of BC is crucial to improve patient outcomes. Currently, metabolomics is a potential technique that can be used to detect BC. We reviewed current publications and synthesised the findings on BC and metabolomics, i.e. metabolite upregulation and downregulation. Fourteen metabolites (lactic acid, leucine, valine, phenylalanine, glutamate, histidine, aspartic acid, tyrosine, serine, uracil, hypoxanthine, carnitine, pyruvic acid and citric acid) were identified as potential biomarkers for BC. In conclusion, this systematic review presents new opportunities for the diagnosis of BC.

Keywords: Bladder cancer, metabolomics, mass spectrometry, systematic review

Introduction

Bladder cancer (BC) is among the most common cancers worldwide: with more than 330,000 new cases of BC and more than 130,000 deaths because of BC occur each year [1]. Thus, early detection of BC can reduce mortality and improve the patients’ quality of life. The early symptom of BC is gross or microscopic blood in the urine, but hematuria only has a 5% specificity in BC diagnosis [2]. The standard for the initial diagnosis of BC is cystoscopy and histopathological analysis of biopsy specimens. However, these procedures are invasive, uncomfortable and costly [3,4]. Furthermore, cystoscopy may omit certain lesions, particularly small areas of carcinoma in situ [5]. Urine cytology has a sensitivity of only 35% and specificity of 94% [6], but it can be used to detect high-grade BC. Therefore, the development of new biomarkers to detect of BC would benefit patients. Recently, evidence suggested that metabolomics is useful for this purpose.

Metabolomics is the quantitative description of all endogenous low-molecular-weight components (<1 kDa) of biological samples, such as plasma, tissue or urine [7]. Metabolomics is a potential technique for effectively diagnosing BC by identifying one or more biomarkers in biological fluids and tissues. These low molecules are involved in the whole of metabolic processes including anabolism and catabolism as well as all the related cellular processes such as absorption, distribution, and energy utilization [8,9]. These endogenous compounds are highly susceptible to various factors, such as hormones, age, lifestyle, physical activities, medication and underlying disease. Metabolomics aims to elucidate potential changes in biochemical processes in various disease states, recovery and so on [10]. In oncology, tumor cells are known to have abundant biomarkers that function in specific metabolic processes, and the profiles of these biomarkers are possibly useful to assess the pathology, progression and prognosis of cancer.

Predominantly, two analytic platforms including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectrometry were used to analyse metabolomic profiling, which are based on spectroscopic techniques. MS and NMR have become crucial steps in quantitative and qualitative metabolic analyses. Both techniques allow extensive and rapid analysis of small molecule metabolites [11]. The choice of either one of these technologies depends on the aims and sensitivity and selectivity requirements of the study. Advantages and limitations of each of these technologies have been presented [8,10,12-15].

In recent years, metabolomic studies primarily aimed to identify tumour-specific biomarkers for cancers, including colorectal cancer, breast cancer, pancreatic cancer, lung cancer, gastric cancer, oesophageal cancer, cervical cancer and cancer cells [16]. As expected, an increasing amount of evidence revealed the metabolomic profiles of biological samples from BC patients [17-27]. In this study, we systematically evaluated and synthesized the validating data from various metabolomic studies that have attempted to identify various metabolites as potential biomarkers for BC.

Methods

Literature search

An initial literature search was performed in Pubmed, Medline, Embase and Web of science (the last search was updated on 31 December 2014). We used the following search strings: metabolo*, metabolic profiling, mass spectrometry, nuclear magnetic resonance spectrometry, and bladder cancer. The search was limited to English-language articles. The reviewer also hand-searched all included studies to ensure that a comprehensive search strategy was implemented.

Inclusion and exclusion criteria

Inclusion criteria

(1) Profiling studies on BC tissues, serum and urine. (2) Profiling studies on BC cell lines. (3) Profiling studies using the metabolomic techniques, such as MS and NMR.

Exclusion criteria

(1) Studies on genomics, transcriptomics and proteomics. (2) Studies on benign bladder disease. (3) Studies without detailed metabolites. (4) Animal studies.

Quality assessment

To ensure that all relevant studies included in our review were of good quality and should be included, three independent assessors conducted a critical assessment using a Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool [28]. The QUADAS-2 tool comprises four key domains namely, patient selection, index test, reference standard, and flow and timing. Seven questions were used to evaluate the quality of diagnostic accuracy studies. Each question can be answered with “yes”, “no”, or “unclear”. The answer “yes” means that the risk of bias is low, whereas the answer “no” or “unclear” means the risk of bias is high. The individual quality assessments of QUADAS-2 were provided in Supplementary Table 1.

Results

A total of 488 potentially relevant publications were identified during the literature search. A total of 477 non-relevant publications for this systematic review were excluded after reviewing the titles, abstracts and the full text. Finally, 11 original articles met the inclusion criteria and were eligible for systematic review (Table 1). Figure 1 shows the selection process. The biological samples used in metabolomic analyses included urine (n = 6), serum (n = 3), cell (n = 1) and a combination of urine, tissue and cell (n = 1). Results of the QUADAS-2 study quality assessment are shown in a bar graph in Figure 2. The majority of all included studies in this review met most items in QUADAS-2, thereby indicating that the overall quality of the included studies was generally good.

Table 1.

Summary of included studies. All comparisons are determined at a statically significant level

Author (year) Analytical platform Sample type NCa Control NC Metabolites of cancer compared to control

Up-Regulated Down-regulated
Pasikanti et al. (2010) GC/TOF-MS Urine 24 non-BC patients 51 Uridine Senecioic acid, Citric acid
Melibiose 2-Butenedioic acid, Ribitol
Valine Ribonic acid, Sumiki’s acid
2,5-Furandicarboxylic acid
2-Propenoic acid
Srivastava et al. (2010) 1H NMR Urine 33 HC, n = 37 70 Taurine Citrate, Phenylalanine
UTI, n = 31 Hippurate
BS, n = 2
Putluri et al. (2011) LC-MS Urine Tissues BC cell lines 83 Benign adjacent tissues 51 Spermindine, NI-acetylspermine Aniline
Serine, Uracil, Thymine, Urocanic acid Creatine
N8- acetylspermine, Tyrosine, Histidine 2-Hydroxykynurenine acid
S-Adenosylmethionine, Oleic acid Histamine
Isoleucine, Homoserine, Kynurenine Giyceraldhyde-3-PO4
Guanine, Phenylalanine, Palmitic acid Hippuric acid
Lysine, Hypoxanthine, Valine, Leucine 4-pyridoxic acid
D-Ribonolactone, Pipecolic acid Coumarin
Cytidine monophosphate, Homocysteine Taurine
Niacinamine , Asparagines, Carnitine Lauric acid
3-Hydroxybutyic acid, Retinol 2-hydroxykynurenine
Niacinamide, 3-Hydroxykynurenie Giyceraldhyde-3-po4
Lin et al. (2012) RPLC-MS Serum 13 HC, n = 20 38 Eicosatrienol, Phosphatidylcholine
HILIC-MS Nephrolithiasis, n = 8 Azaprostanoic acid, Docosatrienol
BPH, n = 10 14’-apo-beta-carotenal, Acetylphenylalanine
Methyl hippuric acid
Cao et al. (2012) 1H NMR Serum 37 HC, n = 25 73 Glucose, Acetoacetate Tyrosine, Phenylalanine
CP, n = 28 Isoleucine, Leucine
Post-TURBT, n = 20 Lactate, Glycine
Alberice et al. (2013) LC-MS Urine 48 Histidine, Phenylalanine, Hypoxanthine
CE-MS Tyrosine, Tryptophan, Leucine
N-acetyltryptophan, Carnitine
Bansal et al. (2013) 1H NMR Serum 67 HC 32 DMA, Malonate, Histidine,
Lactate, Glutamine, Valine
Dettmer et al. (2013) LC-MS BC cell lines Alanine, Arginine, Histidine, Fructose Cystine, Glycine
CE-MS Asparagines, Aspartate, Ornithine Glucose, Pyruvate
Citruline, Glutamine, Leucine Glycerate, Fumarate
Methionine, Myoinositol, Threonine Citrate, Indole 3 lactate
Phenylalanine, Serine, Tryptophan Hydroxy indole 3 acetate
Valine, Lactate, Kynurenine
Pasikanti et al. (2013) GC×GC-TOFMS Urine 38 non-BC controls 61 Adipic acid, Erythritol, Pseudouridine Citric acid
Anthranilic acid, Talonic acid Dihydroxyacetone
Coumaric acid, Lactic acid Ethyl tartrate
Cyclopentane-1, 2-diamine Gluconic acid, Glycerol
Erythropentonic acid, 2-pentadecanol Levulinic acid
Ethylmalonic acid, Melibiose Pinene, Ribitol
Gluconic acid, Itaconic acid Ribonic acid, Sebacic acid
Heptadecanoic acid, Uridine Sumiki’s acid
Hydroxybutyric acid 2,5-furandicarboxylic acid
N-acetyl-anthranilic acid 2-butanedioic acid
Vanillylmandelic acid 2-hydroxyglutaric acid
2-aminoisobutyric acid
2-hydroxymalonic acid
3,4-dihydroxyphenylpyruvate
3-hydroxysebacic acid
4-methoxycinnamic acid
Tripathi et al. (2013) HR-MAS-NMR Tissue 33 Benign disease 26 Triglycerides , Leucine, Aspartate
GC-MS Isoleucine, Valine, Lactate, Creatine
Alanine, Glutamine, Acetate, Uridine
Lysine, Glutamate, Choline, Taurine
Glutathione, Phosphocholine, Glycine
Glycerophosphocholine, Myoinositol
Tyrosine, Phenylalanine
Diphosphate sugars
Jin et al. (2014) HPLC-QTOFMS Urine 138 HC 121 Pyruvate, Phosphoenolpyruvate Melatonin, Glutarylcarnitine, Decanoylcarnitine
Carnitine, Trimethyllysine,
Isovalerylcarnitine, Oxoglutarate,
palmitoyltransferase

GC/TOFMS, Gas chromatography-time-of-flight mass spectrometry; GC-MS, Gas chromatography-mass spectrometry; LC-MS, Liquid chromatography-mass spectrometry; CE-MS, Capillary electrophoresis-mass spectrometry; HR-MAS-NMR, High resolution-magic angle spinning-nuclear magnetic resonance spectroscopy; 1H NMR, Proton nuclear magnetic resonance; BC, Bladder cancer; RPLC-MS, Reversed phase liquid chromatography-mass spectrometry; HILIC-MS, Hydrophilic interaction chromatography-mass spectrometry; GC×GC-TOFMS, Two-dimensional gas chromatography time-of-flight mass spectrometry; BPH, Benign prostatic hyperplasia; UTI, Urinary tract infection; BS, Bladder stone; HC, Healthy controls; Nc, Number of control patients; NCa, Number of cancer patients; CP, Calculi patients; HPLC-QTOFMS, High-performance liquid chromatography-quadrupole time-of-flight mass spectrometry.

Figure 1.

Figure 1

Study flow diagram.

Figure 2.

Figure 2

Overall quality assessment of included articles using the QUADAS-2 tool. A. Risk of bias. B. Applicability concerns.

Glucose metabolites

Glucose metabolism includes glycolysis and aerobic oxidation. The metabolite intermediates that were found to be significantly different in cancer samples are shown in Table 2. Data revealed that lactic and citric acids were the most represented metabolites, and these were upregulated and downregulated, respectively. Results for glucose and fructose have been inconsistent in different articles. Pyruvic acid and fumarate, which are the metabolic intermediates of glycolysis and tricarboxylic acid (TCA) cycle, respectively, were downregulated in BC cell lines [24].

Table 2.

Altered glucose metabolites expression in bladder cancer biological samples

Author Sample type Analytical platform Anaerobic oxidation (Glycolysis) Aerobic oxidation (The TCA cycle)


Glucose Fructose Pyruvic acid Lactic acid Citric acid Fumaric acid
Pasikanti et al. (2010) Urine GC-MS
Shatakshi et al. (2010) Urine 1H NMR
Cao et al. (2012) Serum 1H NMR
Bansal et al. (2013) Serum 1H NMR
Dettmer et al. (2013) Cell LC-MS
CE-MS
Pasikanti et al. (2013) Urine GC×GC-TOFMS
Tripathi et al. (2013) Tissue HR-MAS-NMR
GC-MS

Amino acid metabolites

Table 3 shows that the metabolic intermediates of amino acids (both essential and non-essential) in cancer samples were significantly different from those of the control. Thirteen amino acid metabolites are the most consistently upregulated biomarker in the urine, serum and tumor tissues of patients with BC. A similar trend was observed in BC cell lines as well [19,24]. Meanwhile, upregulation of amino acids, especially leucine, valine, phenylalanine, glutamate, histidine, aspartic acid, tyrosine and serine, was generally observed in the majority of studies.

Table 3.

Altered amino acid metabolites expression in bladder cancer biological samples

Author Sample type Analytical platform Essential amino acid Non-essential amino acid


Leu Met Lys Iie Val Phe Trp Thr Glu Gly His Ala Arg Cys Asp Pro Tyr Asn Gln Ser
Pasikanti et al. (2010) Urine GC/TOF-MS
Shatakshi et al. (2013) Urine 1H NMR
Putluri et al. (2011) Urine Tissue Cell LC-MS
Cao et al. (2012) Serum 1H NMR
Alberice et al. (2013) Urine LC-MS
CE-MS
Bansal et al. (2013) Serum 1H NMR
Dettmer et al. (2013) Cell LC-MS
CE-MS
Tripathi et al. (2013) Tissue HR-MAS-NMR
GC-MS

Leu, Leucine; Met, Methionine; Lys, Lysine; Ile, Isoleucine; Val, Valine; Phe, Phenylalanine; Trp, Tryptophan; Thr, Threonine; Glu, Glutamate; Gly, Glycine; His, Histidine; Ala, Alanine; Arg, Arginine; Cys, Cysteine; Asp, Aspartic acid; Pro, Proline; Tyr, Tyrosine; Asn, Asparagine; Gln, Glutamine; Ser, Serine.

Lipid metabolites

A study on saturated and unsaturated free fatty acids (FFA) has shown upregulation of oleic and palmitic acids and downregulation of lauric acid in BC samples using LC-MS [19]. In addition to FFA, several other metabolites related to lipid metabolism were upregulated in BC (Table 4). A study also demonstrated that demonstrates up-regulation of choline-containing compounds (choline, phosphocholine and glycerophosphocholine) were upregulated in BC patients compared with benign controls [26]. Meanwhile, Cao et al. reported the increase in acetoacetate amount in cancer patients compared with the control [20]. Carnitine was also upregulated in the different BC specimens. In addition, Pasikanti et al. and Lin et al. demonstrated upregulation of phosphatidylcholine and downregulation of glycerol in patients with BC, respectively [21,25].

Table 4.

Altered lipid metabolites expression in bladder cancer biological samples

Author Sample type Analytical platform Un-saturated/saturated fatty acid PC Glycerol TG Aceto-acetate Inositol Carnitine Choline-containing compounds


Oleic acid Lauric acid Palmitic acid Phospho-choline Choline Glyceropho-sphocholine
Putluri et al. (2011) Urine Tissue Cell LC-MS
Lin et al. (2012) Serum RPLC-MS
HILIC-MS
Cao et al. (2012) Serum 1H NMR
Bansal et al. (2013) Serum 1H NMR
Pasikanti et al. (2013) Urine GC-MS
Tripathi et al. (2013) Tissue HR-MAS-NMR
GC-MS
Dettmer et al. (2013) Cell LC-MS
CE-MS
Jin et al. (2014) Urine HPLC-QTOFMS

PC, Phosphatidylcholine; TG, Triglycerides.

Nucleotide metabolites

Table 5 shows that purine, pyrimidine and ribose in cancer samples were significantly different from those in the control. Two studies found that hypoxanthine levels in urine significantly increased in BC patients versus normal individuals [19,22]. In addition, mass spectrometry revealed that uracil was significantly higher in urine samples of BC patients. Pentose is another essential component of nucleotide, which comprises ribose and deoxyribose. It has shown that pentose was upregulated in the urine of patients with BC using GC×GC-TOFMS.

Table 5.

Altered nucleotide metabolites expression in bladder cancer biological samples

Author Sample type Analytical platform Purine Pyrimidine Hypoxanthine Ribose


Adenine Guanine Cytosine Thymine Uracil
Pasikanti et al. (2010) Urine GC-MS
Putluri et al. (2011) Urine Tissue Cell LC-MS
Pasikanti et al. (2013) Urine GC×GC-TOFMS
Alberice et al. (2013) Urine LC-MS
CE-MS

Discussion

This study is the first systematic review on the current status of metabolomic profiling as a diagnostic tool for BC. The main aim of this review was to summarise the use of non-invasive urine and serum based tests for detecting BC during diagnosis. A total of 11 studies were included. These studies reported that discriminatory metabolomics is a potential biomarker for BC detection (Table 1). High sensitivity and specificity biomarkers should be investigated by non-invasion method. In the present systematic review, we founded that out of 148 differentially expressed metabolites, 1 glucose metabolite, 12 amino acid metabolites, 10 lipid metabolites and 5 nucleotide metabolites were highly expressed, whereas, 3 glucose metabolites and 2 lipid metabolites were obtained at low concentrations. Metabolites whose expression levels have obviously changed may be identified as potential biomarkers in BC patients.

The essential hallmarks of cancer are associated with an altered cancer cell-intrinsic metabolism, either as a consequence or as a cause [9]. Six essential characteristics are acquired by a cell during its progression into malignancy: limitless replicative potential, sustained angiogenesis, avoidance of apoptosis, self-sufficiency in growth signals, insensitivity to anti-growth signals and tissue invasion and metastasis. These hallmarks have been studied extensively [29,30]. Energy metabolism, an obvious hallmark of cancer, has been added to the list [9,31,32]. The incremental rate of cell proliferation in cancer requires the reprogramming of metabolic pathways to satisfy large demands for ATP, NADPH, NADH and carbon skeletons [33]. In other words, the metabolic pathways associated with BC cells are complex.

The studies revealed that lactic acid is upregulated in BC sample [23-26]. This phenomenon may be attributed to the Warburg effect [34], which states that cancer cells exhibit increased dependence on glycolytic pathway for ATP generation and gives rise to enhanced lactic acid production [35]. Such acidic condition in tumor microenvironment promotes tumor invasion and suppresses anticancer immune effectors [36-38]. Pyruvic acid is mainly transformed into lactic acid by LDH enzymes. Dettmer et al. found that pyruvate is downregulated in BC cell lines [24]. Several other lactic acid precursor metabolites related to glycolysis metabolism are downregulated. In addition, low level of pyruvic acid is believed to be related to a high synthetic rate of lactic acid. These observations suggest that increased lactic acid level in biological samples of BC is useful in BC diagnosis.

Decreased levels of citric acid and fumarate, which are the metabolic intermediates of aerobic oxidation (especially in the TCA cycle), were observed in BC samples [17,24,25]. The downregulated metabolite intermediates of Krebs cycle may explain cancer cell preference for glycolysis over oxidative phosphorylation in mitochondria. This observation, as discussed above, implies that reduced citric acid concentration resulted from its active uptake from the extracellular matrix into the tumor cell [39]. Some studies confirmed that citric acid has been used as an antineoplastic drug in cancer treatment [40,41]. Thus, reduced citric acid concentration can be regarded as a specific marker of BC.

In this review, increased amino acids levels (Table 3) have been demonstrated in the urine, serum and tissue samples of BC patients. The availability of amino acids in the tumor microenvironment is necessary for cell proliferation and as a metabolic substrate. The source of these amino acids has not been determined, but amino source is likely a combination of systemic protein catabolism [42], tumor microenvironment degradation of extracellular matrix and de novo biosynthesis [43]. Increased levels of leucine, valine, phenylalanine, histidine and serine resulted from the increased rates of amino acid metabolism in BC patients. These findings corroborate that amino acids are important biomarkers of BC and provide a promising diagnostic standard between patients and healthy controls. Thus, amino acid metabolism, particularly the increased levels of leucine, valine, phenylalanine, glutamate, histidine, aspartic acid, tyrosine and serine, could be used for monitoring tumor occurrence in BC patients.

Increased levels of the majority of lipid metabolic intermediates are shown in our review. Fatty acids are the major building blocks for the synthesis of triglycerides (TG), which are mainly used for energy storage [44]. Several studies have demonstrated that tumor cells reactivate de novo lipid synthesis [45-47], which may suggest that lipid synthesis plays an important role in tumor pathogenesis [48]. Meanwhile, some studies confirmed that fatty acid ß-oxidation is a dominant pathway for energy production in prostate [49] and pancreatic cancers [50]. Rapid tumor cells proliferation relies on large amounts of lipids as building blocks of cell membranes. The level of choline, a vital precursor of cell membrane components, was observed to be significantly higher in human and animal studies [26,51]. Higher concentrations of choline in the extracellular fluid are positively correlated with cancer cell proliferation [52]. Meanwhile, acetoacetate is among the components of ketone bodies to be upregulated in the serum of BC patients, as revealed by Cao et al. using 1H NMR [20]. Acetoacetate is being evaluated as an intermediate to measure ketone body utilization of the tumors [53,54]. Jobard et al., reported that acetoacetate plays an important role in the diagnosis, prognosis and management of cancer [55]. Interestingly, upregulation of carnitine is also presented in this review, which might be an important factor in determining the cancer status [27]. Results showed that using lipid metabolism profile for BC diagnosis is relatively promising.

Nucleotide metabolism imbalance plays an important role in a variety of human diseases, including cancer [56]. Increased levels of guanine, cytosine, thymine, hypoxanthine, uracil and ribose are found in the urine of BC patients [17,19,22,25]. Concentrations of these metabolites were significantly higher in BC patients than in the controls. Hypoxanthine is an upstream metabolite in the nucleotide biosynthetic pathway and is possibly a biomarker in BC diagnosis. Purine and pyrimidine nucleotides play an essential role in a large number of cellular processes involving DNA and RNA synthesis, biosynthesis, energy supply and regulatory mechanisms. These nucleotides are synthesised in mammalian cells by de novo pathways, which are energy consuming, or by conserve energy salvage pathways [57]. These studies confirmed that a high level of nucleotide metabolism may be considered as a novel biomarker for BC.

Our systemic review has several limitations. The possibility of publication bias exists, and eligible studies may have been omitted despite a comprehensive and systematic literature search. In fact, the majority of papers included in this review were published within the last three years. In some of these studies, tumor stages and grades were not elaborated. Thus, evaluating the diagnostic effects of these potential biomarkers was difficult. The specificity and sensitivity for BC diagnosis using metabolites are still unclear. Furthermore, analogous results possibly do not represent identical metabolites that result from the diverse samples and analysis techniques used in different studies. Because of the lack of abundant homogenous data, we were not able to perform a diagnostic meta-analysis.

A systematic review on metabolomic profiling studies on BC was presented. Although a few conflicting results may have interfered with our judgment, the majority of consistent results are useful in BC diagnosis. A few metabolites, namely, lactic acid, leucine, valine, phenylalanine, glutamate, histidine, aspartic acid, tyrosine, serine, uracil, hypoxanthine and carnitine are considered novel potential biomarkers for BC, and these potential biomarkers may have diagnostic value and may even indicate the risk of cancer recurrence. However, the clinical application of metabolomic profiling for BC detection still requires further validation in future studies.

Acknowledgements

The work was supported by the Provincial Initiative Program for Excellency Disciplines of Jiangsu Province, the National Natural Science Foundation of China (grants No. 81272832 and 81201997), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and Jiangsu Provincial Special Program of Medical Science (BL2012027).

Disclosure of conflict of interest

None.

Supporting Information

ijcem0008-11052-f3.pdf (217.6KB, pdf)

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