Disease monitoring of hepatocellular carcinoma through metabolomics

Table 3 Utility of significantly altered (P < 0.05) metabolites in accurately predicting hepatocellular carcinoma (hepatocellular carcinoma cases vs patients with cirrhosis)

Ref.	Platform	Comparison	Class prediction methodology	Classification accuracy or sensitivity/specificity	AFP sensitivity/specificity
Patterson et al[29]	UPLC/ESI-QTOF-MS	HCC (n = 20) vs cirrhosis (n = 7)	Random forest	96.3	-
Chen et al[30]	Integrated GC/QTOF-MS + UPLC/QTOF-MS	HCC (n = 82) vs healthy (n = 71)	OPLS-DA	100.0	-
Wu et al[31]	SELDI-TOF MS	HCC (n = 48) vs cirrhosis (n = 54) or healthy (n = 42)	GRO-α + thrombin light chain PS20 Protein immunoassay	89.6/89.6	69/83
Cao et al[32]	UPLC/QTOF-MS	HCC (n = 23) vs cirrhosis (n = 22)	PLS-DA	67.0	-
Gao et al[33]	NMR	HCC (n = 39) vs cirrhosis (n = 36)	PLS-DA	45.7	-
Wu et al[34]	GC/MS	HCC (n = 20) vs healthy (n = 20)	PCA with ROC curve analysis	AUC=88.3; AUCAFP = 92.5 when combined with AFP	-
Soga et al[35]	LC/MS-MS	HCC (n = 32) vs HCV-only (n = 35) or cirrhosis (n = 18)	Multiple logistic regression; ROC curve analysis	88.1	0.760
Wang et al[38]	UPLC-MS	HCC (59) vs cirrhosis (20) or NHC (20)	PLS-DA, ROC curve analysis	CSA 79.3/100 CSA + AFP20 96.4/100 UPLC-MS 100/100	AFP20 74/38 AFP200 52/90
Zhou et al[39]	UPLC-QTOF-MS	HCC (n = 69) vs cirrhosis (n = 28)	PLS-DA, ROC curve analysis	AEA 88.0 PEA 82.0 AEA + PEA 88.0	-
Nahon et al[40]	NMR	Small HCC (n = 28) vs cirrhosis (n = 93); Large HCC (n = 33) vs cirrhosis (n = 93)	OPLS	Small HCC: 61.0/100.0 Large HCC: 100.0/100.0	-
Yin et al[41]	RPLC/QTOF-MS; HILIC/QTOF-MS	HCC (n = 25) vs cirrhosis (n = 24) or healthy (n = 25)	OPLS	RPLC: 61.8 HILIC: 57.0 RPLC + HILIC = 63.6	-
Li et al[42]	UPLC/QTOF-MS	HCC (n = 8) vs cirrhosis (n = 6) or healthy (n = 6) (murine samples)	OPLS-DA	88.2	-
Budhu et al[43]	Training set1: GC/MS + UPLC/MS-MS; Testing set2: Affymetrix GeneChip	Training set: Stem-like aggressive HpSC-HCC (n = 15) vs Mature hepatocyte less aggressive MH-HCC (n = 15); Testing set: HpSC-HCC and MH-HCC (n = 217)	Multivariate analysis	172.0/83.0, AUC = 0.830 272.0/91.0, AUC = 0.860	-
Fitian et al[45]	UPLC/MS-MS + GC/MS	HCC (n = 30) vs HCV-cirrhosis (n = 27)	Random forest	72% 12-HETE 73.3/69.2	AFP20 63.3/83.6
			ROC analysis	15-HETE 83.3/59.3
				Aspartate 100/51.9
				Glycine 83.3/63.0
				Serine 73.3/85.2
				Phenylalanine 73.3/81.5
				Homoserine 70.0/85.2
				Sphingosine 58.3/86.7
				Xanthine 63.3/88.9
				2-Hydroxybutyrate 76.7/77.8
Gao et al[46]	GC-TOF/MS	HCC (n = 39) vs HBV-cirrhosis (n = 52)	Random forest (validation set)	96.8% in HCC vs HBV-cirrhosis 100% in HBV-cirrhosis vs HBV	-
				100% in HBV vs NHC
			ROC analysis (validation set)	100/95.2 HBV vs NC
				83.3/100 HBV-cirrhosis vs HBV
				76.9/83.3 HCC vs HBV-cirrhosis
			Bayes discriminant function model (validation set)	76.9% HCC 100% HBV-cirrhosis
				94.1% HBV
				100% NHC

Classification accuracy describes the capacity of the metabolomic classification technique to accurately predict the group of each study subject. UPLC: Ultrahigh-performance liquid chromatography; AEA: Anandamide; OPLS: Orthogonal projection to latent structure; PCA: Principal component analysis; PEA: Palmitylethanolamide; PLS-DA: Partial least squares-discriminant analysis; MS: Mass spectrometry; TOF: Time-of-flight; GC: Gas chromatography; HCC: Hepatocellular carcinoma; HBV: Hepatitis B virus; GRO-α: Growth related oncogene-alpha; ROC: Receiver operator characteristic; NHC: Normal healthy controls; AFP20: AFP performance at the cutoff of 20 ng/mL; AFP200: AFP performance at the cutoff of 200 ng/mL.