Current state and future direction of screening tool for colorectal cancer

Table 4 High-throughput metabolomic studies of potential biomarkers in CRC screening

Sample type		Ref.	Analytical technique(s)	Major metabolites	Out-comes	Sn / Sp	Significant finding(s)
Dried blood		Jing et al[93], 2017	Direct infusion MS	AA (4) FA (4)	CRC	81.2/84	Establishing a reasonable diagnostic regression model with eight blood parameters
SERUM	BP	Zhang et al[122], 2018	UPLC-MS/MS	FA(2): Eicosanoids	CRC	N/A	Identification of eicosanoids as potential biomarkers for identifying among health, enteritis and CRC
		Guo et al[123], 2017	FTICR MS	FA(5): Male FA(2): Female	CRC	77.3/92.4 80.8/85.9	Presenting the relationship between the change trends of six phospholipids and cancer stages
		Farshidfar et al[124], 2016	GC-MS	AA (9) FA(7) CH (12) Others (13)	CRC	85.0/86.0	Discovery of a suite of CRC biomarkers that provide early detection, prognostication and preliminary staging information
		Zhang et al[125], 2016	FTICR MS	FA (6)	CRC	93.8/92.2	Identification of Free Fatty Acids as diagnostic indicators of early-stage CRC patients
		Gu et al[126], 2015	LC-MS/MS	AA (8)	CRC	65.0/95.0	Performing a combined analysis of amino acids in three different domains: FAAs, FSPAAs, and IPAAs
		Zhu et al[127], 2014	LC-MS	AA (7) FA (3) CH (3)	CRC	96.0/80.0	Establishing Partial least-squares-discriminant analysis (PLS-DA) models for distinguishing CRC patients
		Li et al[128], 2013	DI-ESI (±) -FTICR MS	FA (9)	CRC	86.5/96.2	Emphasize that the facile loss of methyl chloride from the [M  +  Cl] (-) form of LPC (16:0) in its tandem mass spectrum
		Tan et al[129], 2013	UPLC-QTOFMS	AA (6) FA (1) CH (3)	CRC	83.7/91.7	Identification of serum metabolite markers as diagnostic indicators for the detection of CRC
		Ma et al[130], 2012	GC-MS	AA (3) CH (3)	CRC	93.31/96.71	Emphasize integrated network connectivity analysis for the diagnosis
		Nishiumi et al[131], 2012	GC-MS	AA (3) CH (1)	CRC	83.1/81.0	Establishing potential predictive model for early detection of colorectal cancer
		Ritchie et al[132], 2010	FTICR MS	FA (3)	CRC	75.0/90.0	IdentifIcation of a systemic metabolic dysregulation comprising previously unknown hydroxylated polyunsaturated ultra-long chain fatty acid metabolites in CRC patients
		Ludwig et al[133], 2009	Hadamard-encoded TOCSY spectra	FA (1) CH (4)	CRC	70.0/95.0	Showing the potential of fast Hadamard-encoded TOCSY spectra for improved classification of serum samples from colorectal cancer patients using a metabolomics approach
	S	Hata et al[96], 2017	FIA–MS/MS	FA (1: GTA-446)	CRC	83.3/84.8	Identification of GTA-446 as promising tool for primary colorectal cancer screening
		Uchiyama et al[98], 2017	CE-TOFMS	FA (1): Benzoic FA (1): Octanoic FA (1): Decanoic AA (1): Histidine	CRC	89.0/82.0 76.0/71.0 71.0/75.0 63.0/82.0	The first report to determine the correlation between serum metabolites and CRC stage using CE-TOFMS Identification of benzoic acid as diagnostic indicators
		Ritchie et al[97], 2013	TQ-MS	FA (1)	CRC	85.7/~52.12	Identification of low-serum GTA-446 as significant risk factor for CRC and sensitive predictor of early-stage disease
		Ikeda et al[134], 2012	GC-MS	AA (1): Alanine CH (1): GluL AA(1): Glutamine	CRC	54.5/91.6 75.0/75.0 81.8/66.7	Showing the potential of metabolomics as an early diagnostic tool for cancer
		Leichtle et al[135], 2012	TIS-MS	AA (1)	CRC	N/A	Showing serum glycine and tyrosine in combination with CEA are superior to CEA for the discrimination
PLASMA	BP	Nishiumi et al[94], 2017	GC/QqQMS	AA (3) FA (3) CH (2)	Stage 0/I/II	99.3/93.8	Establishing potential predictive model of colorectal cancer that do not involve lymph node or distant metastasis
		Li et al[136], 2013	Lipid extraction MS	FA (3)	CRC	88.3/80.0	Identification of the plasma choline-containing phospholipid levels as potential biomarkers to distinguish between healthy controls, AP and CRC cases, implying their clinical usage in CRC and/or AP-CRC progression detection
		Miyagi et al[137], 2011	HLPC-ESI-MS	AA (10)	CRC	N/A	Showing the potential of plasma free amino acids profiling for improving cancer screening and diagnosis and understanding disease pathogenesis
		Okamoto et al[138], 2009	HLPC-ESI-MS	AA (6)	CRC	N/A	Presenting the possibility of plasma free amino acids profiling
		Zhao et al[139], 2007	LC- MS	FA (4)	CRC	82.0/93.0	Identification of percentage of 18:1-LPC or 18:2-LPC plasma levels compared with total saturated LPC levels, either individually or in combination as potential biomarkers for CRC
	S	Liu et al[140], 2018	N/A	AA(1) :Homocysteine	CRC/A	43.5/98.8	Presenting the possibility of using homocysteine with CEA in screening of early rectal cancer
		Shen et al[95], 2017	2D LC-QToF/MS	FA (1): PG FA (1): SM	CRC	1.00/1.00 1.00/1.00	Presenting the possibility of 2D LC-QToF/MS-based lipidomics profiling
		Crotti et al[99], 2016	GC-MS	FA (1)	CRC	87.8/80.0	Identification of the C10 fatty acid as valuable early diagnostic biomarker of CRC
		Cavia-Saiz et al[141], 2014	high pressure-LC	AA (1)	CRC	85.2/100	Identification of the plasma levels of l-kynurenine as a potential biomarkers of CRC
URINE	BP	Nakajima et al[105], 2018	LC- MS	AA (2)	CRC	N/A	Presenting the potential of polyamines and a machine-learning method as a screening tool of CRC
		Deng,Fang et al[142], 2017	1-dimensional NMR	AA (7) FA (2) CH (8)	A	82.6/42.4	Presenting novel urine-based metabolomic diagnostic test for the detection of adenomatous polyps
		Deng et al[101], 2017	LC- MS	FA (1) CH (2)	A	82.43/36.03	Presenting a clinically scalable MS-based urine metabolomic test for the detection of adenomatous polyps
		Wang et al[143],2017	H-NMR	AA (3) CH (1)	Stage I/II	87.5/91.3	Supporting the utility of NMR-based urinary metabolomics fingerprinting in early diagnosis of CRC
		Rozalski et al[144], 2015	GC-MS	CH (3)	CRC	78.6/75.0	Identification of Urinary 5-hydroxymethyluracil and 8-oxo-7,8-dihydroguanine as potential biomarkers
		Wang et al[102], 2014	1-dimensional NMR	AA (7) FA (2) CH (8)	A	82.7/51.2	Presenting a proof-of-concept spot urine-based metabolomic diagnostic test
		Hsu et al[145], 2013	HPLC-MS/MS	CH (6)	CRC	69.0/98.0	Identification of a set of six targeted nucleosides as marker
		Eisner et al[100], 2013	H-NMR	AA (2) CH (2)	Polyps	64.0/65.0	Presenting a machine-learned predictor of colonic polyps based on urinary metabolomics
		Yue et al[103], 2013	RRLC-QTOF/MS	FA (9) Others (1)	CRC	100/80.0	Identification of CRC urinary metabolites as marker
		Cheng et al[104], 2012	GC/TOF-MS UPLC-QTOFMS	AA (4) FA (1) CH (2)	CRC	97.5/100	Reporting a second urinary metabonomic study on a larger cohort of CRC (n = 101) and healthy subjects (n = 103)
		Chen[146], 2012	CE-MS	AA (8) CH (4)	CRC	N/A	Presenting the usefulness of the technique of CE-MS based on moving reaction boundary
		Wang et al[147], 2010	UPLC-MS SPE-HPLC	AA(4) FA(5) / CH (7)	CRC	N/A	Identification of urinary metabolic biomarker based on UPLC-MS and SPE-HPLC
		Feng[148], 2005	RP-HPLC	CH (2)	CRC	71.2/93.3	Identification of Pseu and m1G as novel biomarkers for colorectal cancer diagnosis and surgery monitoring
		Zheng et al[149], 2005	Column switching HPLC	CH (14)	CRC	71.0/96.0	Identification of urinary nucleosides determined by column switching high performance liquid chromatography method
	S	Johnson et al[150], 2006	LC- MS	FA (1)	ACN	90.0/45.0	Identification of urinary PGE-M as a potential biomarker of ACN
		Hiramatsu et al[106], 2005	ELISA	AA (1)	CRC	75.8/96.0	Indicating that urinary N(1), N(12)-Diacetylspermine is a more sensitive marker than CEA, CA19-9, and CA15-3
FECES	BP	Amiot et al[108], 2015	H-NMR	AA (2) FA (4) CH (1)	ACN	N/A	Identification of (1)H NMR Spectroscopy of Fecal Extracts as biomarker
		Phua et al[107], 2014	GC/TOF-MS	FA (1) CH (2)	CRC	N/A	Establishing proof-of-principle for GC/TOFMS-based fecal metabonomic detection of CRC
		Bezabeh et al[151], 2009	(1)H-MRS	AA (6) FA (1) CH (3)	CRC	85.2/86.9	Detecting colorectal cancer by 1H magnetic resonance spectroscopy of fecal extracts
	S	Lin et al[152], 2016	H-NMR	FA (1): Acetate FA (1): Succinate	Early stage	94.7/92.3 91.2/93.5	Identification of the potential utility of NMR-based fecal metabolomics fingerprinting as predictors

¹Sensitivity and specificity calculated from available data.

²Specificity was calculated for the intention to screening population (40–74 year-olds in the colonoscopy population).

³Additional results for different cut-off values can be read from the original article. BP: Biomarker panels; S: Single markers; MS: mass spectrometer; FTICR: Fourier transform ion cyclotron resonance; SM: sphingomyelins; PC: phosphatidylcholine; FIA-MS/MS: flow injection analysis–mass spectrometry; Arg: arginine; Val: valine; Phe: phenylalanine; Tyr: tyrosin; Ala: alanine; TQ-MS: triple-quadrupole tandem mass spectrometry; GluL: glucuronic lactone; TIS-MS: Turbo Ion Spray Source mass spectrometer; AP: adenomatous polyps; HLPC-ESI-MS: high-performance liquid chromatography-electrospray ionization-mass spectrometry; A: adenomas; GC/QqQMS: gas chromatography/triple-quadrupole mass spectrometry; 2D LC-QToF/MS: two dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry; PG: phosphatidylglycerol(34:0); SM: sphingomyelin (38:8); CE-TOFMS: capillary electrophoresis-time-of-flight mass spectrometry; GC-MS: gas chromatography-mass spectrometry; LC-MS/MS: liquid chromatography tandem MS; FAAs: free amino acids; FSPAAs: free and soluble-proteome amino acids; IPAAs: insoluble-proteome amino acids; DI-ESI(±): Direct-infusion positive and negative ion electrospray ionization; ACN: advanced colorectal neoplasms; NMR: nuclear magnetic resonance spectra; H-NMR: proton nuclear magnetic resonance spectroscopy; RRLC: rapid resolution liquid chromatography; UPLC-QTOFMS: Ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry; SPE, solid phase extraction; RP, reverse-phase; ELISA: Enzyme-linked immunosorbent assay; (1)H-MRS: (1)H magnetic resonance spectroscopy.