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1 Varied methodologies have been employed for the study of CRC metabolome. Studies indicate the promising status of the analysis of small molecules, using non-invasive approaches, in the determination of CRC with high accuracy. + 4070 word(s) 4070 2020-10-07 07:45:26 |
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Monedeiro, F.; Monedeiro-Milanowski, M.; Ligor, T.; Buszewski, B. Metabolic Biomarkers of Colorectal Cancer. Encyclopedia. Available online: (accessed on 17 June 2024).
Monedeiro F, Monedeiro-Milanowski M, Ligor T, Buszewski B. Metabolic Biomarkers of Colorectal Cancer. Encyclopedia. Available at: Accessed June 17, 2024.
Monedeiro, Fernanda, Maciej Monedeiro-Milanowski, Tomasz Ligor, Bogusław Buszewski. "Metabolic Biomarkers of Colorectal Cancer" Encyclopedia, (accessed June 17, 2024).
Monedeiro, F., Monedeiro-Milanowski, M., Ligor, T., & Buszewski, B. (2020, October 13). Metabolic Biomarkers of Colorectal Cancer. In Encyclopedia.
Monedeiro, Fernanda, et al. "Metabolic Biomarkers of Colorectal Cancer." Encyclopedia. Web. 13 October, 2020.
Metabolic Biomarkers of Colorectal Cancer

Metabolic biomarkers of colorectal cancer (CRC) can be found in several matrices obtained from human body, such as breath, urine, feces, blood, intestinal gas, and tissue. Metabolic CRC biomarkers consist of small molecules, including volatile organic compounds (VOCs), which patterns (profiles) can be acquired by analytical techniques and be used to study the presence and progression of disease in the organism. Gas chromatography-mass spectrometry is a technique that allows to analyze volatiles and other classes of compounds of different chemical groups. Molecular profiles may indicate very specific biochemical ongoing processes in a biological system. Comparisons of metabolic profiles and the processing of this data using statistical tools can potentially enacoloble to distinguish diseased subjects from healthy ones.

colorectal cancer gas chromatography metabolomics biological sample VOCs diagnostics

1. Introduction

1.1. Colorectal Cancer Background

According to data regarding cancer burden in 2018 (GLOBOCAN 2018), colorectal cancer (CRC) is currently the third most incident cancer type in the word, with nearly 1.85 million cases and 881 thousand deaths worldwide. In Europe, it occupies the second place in the ranking of cancer occurrence and related deaths, with approximately half a million new cases registered and almost a quarter of a million associated deaths. Moreover, research on cancer progression predicts an increase of 75% in CRC cases over the next 20 years [1]. The global population over time has experienced significant changes in their habits, notably the prevalence of sedentarism, increased intake of dietary fat and processed food, and exposure to carcinogens, all risk factors in CRC [2]. Such context presents a complex perspective on CRC, also a from socioeconomic point of view, emphasizing the need for prevention strategies and promotion of early diagnosis.

It is observed that around 95% of colorectal neoplasms are adenocarcinomas and start as colonic adenomatous polyps [3]. Then, a series of genomic and molecular alterations induce the development of the malignancy in the colon [4]. CRC can be prevented if an intervention occurs leading to excision of the polyps and conduction of proper treatment; therefore, approaches directed towards an early detection of polyps and lesions, before these achieve the malignancy threshold, have substantial importance to reduce both CRC incidence and mortality [3].

1.2. Available Diagnostic Methods

The fecal occult blood test (FOBT), also known as the guaiac test, is generally applied for CRC screening. Nevertheless, this procedure presents relatively low sensitivity, which for this once-only test can be 50% or lower [5][6]. Additionally, FOBT is affected by the presence of interferers, is not specific for distal gut blood and may be insensitive to smaller bleedings. The antibody-based fecal immunochemical test (FIT) for hemoglobin is an improved alternative to FOBT, obtaining a sensitivity greater than 80% [6]. Notwithstanding, the verification of fecal blood can have a low impact on CRC primary assessment and is occasionally indicative of late stage cancer [7]. Currently, colonoscopy is described as the gold-standard screening procedure for CRC as it presents high sensitivity and specificity. However, colonoscopy is a costly and invasive procedure, limiting a patient’s access to the examination and resulting in poor compliance rates, aspects that hinder successful implementation of this test in CRC prevention [8][9]. Imaging exams have great reported efficiency, although also carry limitations regarding the cost of procedures and required exposure to radiation [10].

The group of currently available CRC biomarkers can be classified according to the affected biological matrices related to colorectal neoplasm. The most common are tumor, blood and stool biomarkers [11]. Moreover, molecular indicators can be grouped into three classes: prognostic, predictive and diagnostic markers [12]. Prognostic markers indicate the possible progression of the disease, such as: adenomatous polyposis coli (almost 100% of individuals develop CRC with this germ line mutation) [13][14], p53 (tumor suppressor p53 expression) [12], and epidermal growth factor receptor (EGFR; up to 80% over expression in CRC) [15]. Predictive indicators are used to foresee treatment measures to be taken on a patient. They include, e.g., Kirsten rat sarcoma viral oncogene (KRAS; more than 50% of CRC patients carry a mutant allele) [13][16], BRAF (a mutant KRAS gene, which encodes protein B-Raf, found in only 30–40% of the 90% of patients not affected by anti-EGFR therapy) [14][16], and COX-2 (Cyclooxygenase-2; the expression exhibited in 70% of CRC tumors) [12]. Risk stratification and early detection of polyps are provided by diagnostic markers, such as: insulin like growth factor binding protein 2 (IGFBP2; elevated levels in plasma and serum of CRC patients) [12][14], telomerase (an enzyme responsible for synthesizing DNA from chromosome ends for which an increase in activity was noticed for 90% of colorectal tumors) [17], and pyruvate kinase M2 (PKM2; a glycolytic pyruvate kinase isoenzyme increased in the stool of CRC subjects) [16]. Epi proColon® (Epigenomics Inc., San Diego, CA, USA) is a commercially available test relying on the verification of methylated Septin-9 in DNA extracted from blood, by means of polymerase chain reaction (PCR) [18]. This genetic alteration is associated with the presence of CRC tissue. Studies showed that Epi proColon® exam presented sensitivity and specificity ranging from 75 to 81% and from 96 to 99%, respectively [19]. Nevertheless, subsequent clinical trials demonstrated that test sensitivity was insufficient in case of asymptomatic cases and stage I CRC. Cologuard® (Exact Sciences Corporation, Madison, WI, USA) is a stool-based presumptive test for CRC, based on the qualitative detection of fecal DNA markers. This exam presented to be superior to the FIT test, although its rate of detection was around 42% in cases of advanced adenomas [8]. Apart from the displayed limitations, these screening strategies tend to achieve wider acceptance among the population and can indicate the need for further colonoscopic investigation, aiding a more approachable monitoring of CRC.

1.3. Metabolomics Studies on CRC

Metabolomics science emerged as a new approach to study biological systems [20]. In a metabolomics workflow, biological samples are processed and comprehensively analyzed in terms of total metabolites, which can belong to a specific chemical class depending on the envisioned approach and the methodologies selected for sample preparation and pre-concentration. Measurements can involve different analytical platforms, with emphasis given to chromatographic techniques—able to resolve complex mixtures—coupled to mass spectrometry [21], such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) [22][23][24].

Among the small metabolites, volatile organic compounds (VOCs) are metabolic products that can elicit diversified patterns that may represent very specific biochemical ongoing processes in the organism. Volatiles’ profiles have been studied in the context of several diseases, especially in exhaled breath, using GC-based analyses [25][26][27][28]. In this context, GC analysis is extremely relevant, because it encompasses the group of VOC metabolites, which cannot be properly assessed by LC.

Research on global molecular metabolites as potential markers of diseases is a very interesting approach for the design of methods directed towards the early diagnosis and evaluation of patient’s response to therapeutic intervention [20][29]. Molecular profiling presents promising perspectives towards clinical applications. The assessment of a set of metabolites has the possibility to provide information regarding simultaneous metabolic alterations, potentially offering a more accurate and detailed diagnosis, thus, it represents a great advance in personalized medicine [30].

Although contemporary, metabolomics-based methods still face several challenges, such as: the existence of a large body of variables that may impact the metabolic profile; the lack of standardization in workflow protocol and irreproducibility between studies that lead to varied panels of potential biomarkers. Therefore, a deeper inspection is required in order to compare the results reported so far by different research groups concerning the metabolomic investigation in CRC, listing the main developments made to date, and thus offering insights into new aspects to be studied regarding CRC characterization.

2. Studies on Colorectal Cancer Metabolic Biomarkers

2.1. Applied Methodologies

A critical matter involving metabolomics studies is the employment of varied protocols covering sample collection, processing and analysis. In this sense, the selection of specific analytical parameters can deeply influence the set of acquired metabolites, turning valid the discussion on the main aspects prevalent in sample pre-treatment, extraction procedure and analysis in GC-based metabolomics directed towards CRC markers investigation. Several techniques have been employed for the extraction and pre-concentration of the metabolites of interest in different biological samples. The particular characteristics of each matrix determine which sample preparation techniques are required, which in turn, have associated advantages and limitations to be observed by the analyst. Fundamental aspects regarding the selection of biological matrix are the concentration range of the target analytes in the sample, window of detection provided, matrix complexity and involved distribution mechanisms. Sample preparation techniques to be used should be chosen based on their ability to pre-concentrate the analyte, the availability of specific materials, required processing time and involved costs. Data concerning sample preparations details, study design and statistical approaches employed by the reviewed studies are summarized in Table 1.

Table 1. Table summarizing all 21 studies regarding investigation of biomarkers of CRC in urine, feces, and breath samples.



Sample Preparation and Analytical Technique

Main Analytes

Type of GC Column

Statistical Approach


Qiu et al., 2010 [31]

·                  60 CRC:

Ø     stage I: 7

Ø     stage II: 23

Ø     stage III: 21

Ø     stage IV: 9

·                  63 HC

solvent extraction with chloroform and derivatization with ECF


SNM: amino acids; organic acids

DB-5MS capillary column (30 m × 250 µm i.d., 0.25-μm film thickness)


Silva et al., 2011 [32]

·                  12 CRC

·                  21 HC


(75 µm)


SVM: hydrocarbons; aldehydes; sulfur compounds

30 m × 0.25 mm ID × 0.25 µm film thickness BP-20

one-way ANOVA, LSD, PCA

Cheng et al., 2012 [33]

·                  103 CRC:

Ø     stage I: 24

Ø     stage II: 45

Ø     stage III: 27

Ø     stage IV: 5

·                  101 HC

solvent extraction with methanol and derivatization with methoxyamine (in pyridine) and BSTFA (1% TMCS)


SNM: amino acids; organic acids; saccharides

DB-5MS capillary column (30 m × 250 µm I.D., 0.25-μm film thickness; (5%-phenyl) methyl-polysiloxane bonded and cross-linked

PCA, OPLS-DA, ROC curve, Student’s t-test, Wilcoxon−Mann−Whitney test

Arasaradnam et al., 2014 [34]

·                  83 CRC

·                  50 HC



SVM: ketones; aldehydes; nitrogen compounds

Rxi-624Sil column (20 m length, 0.18 mm ID, 1.0 µm df)

FDA, KNN method

Liesenfeld et al., 2015 [35]

Total for GC-MS and 1H-NMR is 199 CRC:

·                  CRC pre-surgery:

Ø     s0: 5; sI: 12; sII: 40; sIII: 22; sIV: 18

·                  CRC post-surgery:

Ø     sI: 4; sII: 4; sIII: 2; sIV: 2

·                  CRC 6 months follow-up:

Ø     sI: 12; sII: 17; sIII: 15; sIV: 8

·                  CRC 12 months follow-up:

Ø     sI: 7; sII: 13; sIII: 14; sIV: 4

solvent extraction with methanol and derivatization with methoxyamine (in pyridine) and BSTFA (1% TMCS)


SNM: alcohols; amino acids; organic acids; saccharides

HP-5 MS fused silica column (30 m × 0.25 mm; 0.25 µm film thickness of the 5% phenyl 95% dimethylpolysiloxane stationary phase

Wilcoxon–Mann–Whitney tests, PLS-DA, one-way ANOVA, ROC curve

Delphan et al., 2018 [36]

·                  163 CRC pre-surgery:

Ø     stage I/II: 76; stage III/IV: 87

·                  83 with 6 months follow-up:

Ø     stage I/II: 36; stage III/IV: 47

·                  54 with 12 months follow-up:

Ø     stage I/II: 32; stage III/IV: 25

solvent extraction with methanol and derivatization with methoxyamine (in pyridine) and BSTFA (1% TMCS)


SNM: amino acids

HP-5 MS fused silica column (30 m × 0.25 mm; 0.25 µm film thickness of the 5% phenyl 95% dimethylpolysiloxane stationary phase

one-way ANOVA, Pearson Chi-squared test, Pearson’s partial correlation coefficients, Cox proportional hazard models

Mozdiak et al., 2019 [37]

·                  12 CRC

·                  80 adenoma

·                  14 diverticular disease

·                  5 haemorrhoids

·                  14 inflammatory bowel disease

·                  1 excluded

·                  37 HC

not specified



not specified

ROC curve, Sparse logistic regression, Random Forest, Gaussian process classifier, Support vector machine, Neural network


Weir et al., 2013 [38]

·                  10 CRC

·                  11 HC

solvent extraction with isopropanol:acetonitrile:water and derivatization with methoxyamine (in pyridine) and MSTFA (1% TMCS)



SNM: amino acids; organic acids; lipids; steroids

TG-5MS column (30 m, 0.25 mm i.d., 0.25 µm film thickness),

SCFA determination: TG-WAX-A column (30 m, 0.25 mm ID, 0.25 µm film thickness)

AMOVA, Student’ t test, ANOVA, Pearson correlation, PLS-DA

Phua et al., 2014 [39]

·                  11 CRC:

Ø     sB: 6; sC: 5

·                  10 HC

solvent extraction with methanol:water and derivatization with methoxyamine (in pyridine) and MSTFA (1% TMCS)


SNM: lipids; saccharides

DB-1 (30 min × 250 µm i.d.) fused silica capillary column with 0.25 µm film thickness

PCA, OPLS-DA, ROC curve, Welch t test

Bond et al., 2016 [40]

·                  21 CRC

·                  56 with adenomatous polyp/s

·                  60 HC




not specified

Student’s t test, Fisher’s exact test, ANOVA, false discovery rate correction, PLS-DA, factor analysis, ROC curve

Wang et al., 2017 [41]

·                  15 CRC:

Ø     sII: 4; sIII: 6; sIV: 5

·                  12 HC

solvent extraction with isopropanol:acetonitrile:water and derivatization with pyridine-methoxy amino acid salt solution,

SCFA determination:

solvent extraction and derivatization with sulfuric acid solution (50%) and diethyl ether


SNM: amino acids; organic acids; lipids; steroids

30-m TG-5MS column

Student’s t-test, Pearson correlation

Song et al., 2018 [42]

·                  26 CRC:

Ø     sI: 3; sIIa: 5; sIIc: 1; sIIIb: 11; sIIIc: 3; sIVa: 3

·                  28 HC

Analysis of Long-Chain Fatty Acids: solvent extraction with chloroform:methanol (Folch method) and derivatization with BCl3–MeOH

Analysis of Short-Chain Fatty Acids:

solvent extraction with HCl and diethyl ether and derivatization with PFBB in acetonitrile and EDIPA



HP-5 MS 30 m × 250 µm × 0.25 µm column

Chi-square test, Fisher’s exact test, Mann–Whitney U test

Bond et al., 2019 [43]

·                  21 CRC

·                  56 with adenomatous polyp/s

·                  60 HC



SVM: esters; alcohols

60 m long Zebron ZB-624 capillary column with an inner diameter of 0.25 mm. The column was lined with a 1.4 µm film of 94% dimethyl polysiloxane and 6% cyanopropylphenyl

Student’s t test, Mann-Whitey tests, Fisher’s exact test, ANOVA,

false discovery rate correction, PLS-DA, factor analysis, ROC curve


Haines et al. 1977 [44]

·                  30 CRC

·                  64 with non-malignant large-bowel disorders

·                  208 without known large-bowel disorders

direct gas sampling by means of:

either a modified Haldane–Priestley tube’ or a 3-bag collecting system in which one bag contains sample which can then be transferred to a syringe or evacuated aerosol can for later analysis

+ GC


not specified

p value

Piqué et al. 1984 [45]

·                  47 CRC

·                  156 HC

direct gas sampling by means of a 3-bag collecting system in which one bag contains sample which can then be transferred to a syringe or evacuated aerosol can for later analysis



not specified

p value

Peng et al., 2010 [46]

·                  26 CRC:

Ø     sI: 3; sII: 7; sIII: 7; sIV: 7

·                  22 HC



SVM: hydrocarbons

H5-5MS 5% phenyl methyl siloxane (30 m length, 0.25 mm i.d., 0.25 µm thickness)


Altomare et al., 2013 [47]

·                  37 CRC

·                  41 HC

adsorption of VOCs on to sorbent cartridges and thermal desorption


SVM: hydrocarbons

SUPELCOWAX, polyethylene glycol 30 m × 0.25 mm ID. × 0.25 µm stationary phase thickness

PNN, ROC curve

Depalma et al., 2014 [48]

·                  15 CRC

·                  20 with colonoscopic diagnosis of colonic polyps

·                  15 HC

adsorption of VOCs on to sorbent cartridges and thermal desorption



not specified


Wang et al., 2014 [49]

·                  20 CRC

·                  20 HC


(75 µm)


SVM: alcohols; hydrocarbons

DB-5MS (length 30 m × inner diameter (ID) 0.250 mm × film thickness 0.25 µm)

PCA, PLS-DA, Kruskal–Wallis rank sum test

Altomare et al., 2015 [50]

·                  48 CRC

·                  55 HC

adsorption of VOCs on to sorbent cartridges and thermal desorption


SVM: hydrocarbons

HP-5MS, 95% polydimethylsiloxane, 5% polydiphenylsiloxane, 30 m × 0.25 mm ID, 0.25 µm stationary phase thickness

Mann–Whitney U test, chi-square test, Student’s t test, PNN, ROC curve

Amal et al., 2016 [51]

·                  65 CRC

·                  22 with advanced or nonadvanced adenomas

·                  122 HC

adsorption of VOCs on to sorbent cartridges and thermal desorption


SVM: hydrocarbons; ketones; esters; alcohols

SLB-5ms capillary column (with 5% phenyl methyl siloxane; 30 m length; 0.25 mm internal diameter; 0.5 µm thicknesses)

Student’s t test, DFA, ROC curve

VOC—volatile organic compound; CRC—colorectal cancer; HC—healthy controls; s—stage of cancer; ITEX—in-tube extraction; 1H-NMR—proton nuclear magnetic resonance; GC-MS—gas chromatography-mass spectrometry; HS-SPME—headspace-solid-phase microextraction; CAR/PDMS—Carboxen/Polydimethylsiloxane; PDMS/DVB—Polydimethylsiloxane/Divinylbenzene; GC-FID—gas chromatography with flame ionization detection; GC-IMS—gas chromatography coupled with ion mobility spectrometry; GC-TOFMS—gas chromatography/time-of-flight mass spectrometry; PCA—principal component analysis; OPLS-DA—orthogonal partial least squares discriminant analysis; ANOVA—analysis of variance; AMOVA—analysis of molecular variance; LSD—least significant difference; ROC—receiver operating characteristic; FDA—Fisher discriminant analysis; KNN—k-nearest neighbors algorithm; PLS-DA—partial least squares discriminant analysis; PNN—probabilistic neural network; LDA—linear discriminant analysis; DFA—discriminant function analysis; MSTFA—N-methyl-N-(trimethylsilyl)trifluoroacetamide; SCFA—short-chain fatty acid; PFBB—pentafluorobenzyl bromide; BSTFA—N,O-bis(trimethylsilyl)trifluoroacetamide; TMCS—trimethylsilyl chloride; ECF—ethyl chloroformate; EDIPA—3’-O-ethyl-N,N-diisopropylphosphoramidite; SNM—screening of nonvolatile metabolites; SVM—screening of volatile metabolites.


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