A Large-scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma

Abstract

Hepatocellular carcinoma (HCC) is the third most lethal cancer worldwide. The lack of effective biomarkers for the early detection of HCC results in unsatisfactory curative treatments. Here, metabolite biomarkers were identified and validated for HCC diagnosis. A total of 1,448 subjects, including healthy controls and patients with chronic hepatitis B virus infection, liver cirrhosis, and HCC, were recruited from multiple centers in China. Liquid chromatography-mass spectrometry-based metabolomics methods were used to characterize the subjects' serum metabolic profiles and to screen and validate the HCC biomarkers. A serum metabolite biomarker panel including phenylalanyl-tryptophan and glycocholate was defined. This panel had a higher diagnostic performance than did α-fetoprotein (AFP) in differentiating HCC from a high-risk population of cirrhosis, such as an area under the receiver-operating characteristic curve of 0.930, 0.892, and 0.807 for the panel versus 0.657, 0.725, and 0.650 for AFP in the discovery set, test set, and cohort 1 of the validation set, respectively. In the nested case-control study, this panel had high sensitivity (range 80.0%-70.3%) to detect preclinical HCC, and its combination with AFP provided better risk prediction of preclinical HCC before clinical diagnosis. Besides, this panel showed a larger area under the receiver-operating characteristic curve than did AFP (0.866 versus 0.682) to distinguish small HCC, and 80.6% of the AFP false-negative patients with HCC were correctly diagnosed using this panel in the test set, which was corroborated by the validation set. The specificity and biological relevance of the identified biomarkers were further evaluated using sera from another two cancers and HCC tissue specimens, respectively. Conclusion: The discovered and validated serum metabolite biomarker panel exhibits good diagnostic performance for the early detection of HCC from at-risk populations. (Hepatology 2018;67:662-675).

Figure 1

Design of the study.

Figure 2

Score plots of principal component analysis based on the combinational data of ESI⁺ and ESI⁻ modes from the discovery set (A) and the test set (B). Colors and shapes display the subjects from different groups (, normal controls; , patients with cirrhosis; , patients with HCC; and , QC samples).

Figure 3

Identification of potential metabolic biomarkers for the diagnosis of HCC. (A) Partial least squares discriminant analysis score plot based on NC, Cir, and HCC groups in the discovery set. (B) Venn diagram displays variables with VIP values >1 on two principal components (VIP1 and VIP2). (C) Venn diagram displays the differential metabolites when the HCC group was compared with the NC and Cir groups in the discovery set, respectively. Serum relative concentrations of defined potential biomarkers of Phe‐Trp (D) and GCA (E) in the discovery, test, and validation sets, respectively. ^* P < 0.05, ^** P < 0.01, and ^*** P < 0.001 when compared with NC/controls, respectively; ^# P < 0.05, ^## P < 0.01, and ^### P < 0.001 when compared with HCC, respectively. All data are presented as mean ± SE. Abbreviation: FDR, false discovery rate.

Figure 4

Diagnostic performances of serum metabolite panel (2‐Meta), AFP, and both in the diagnosis of HCC and S‐HCC. (A‐C) Diagnostic accuracy of 2‐Meta in the individuals from groups CHB, Cir, HCC, and S‐HCC or falsely diagnosed patients by AFP in the discovery, test, and validation (cohort 1) sets, respectively. AFP⁺, false‐positive AFP (CHB and Cir patients with AFP >20 ng/mL), AFP^–, false‐negative AFP (HCC and S‐HCC patients with AFP <20 ng/mL).

Figure 5

Understanding the biological relevance of the biomarkers for the HCC diagnosis. (A) Heat map of the Pearson correlation coefficients between differential metabolite contents and clinical parameters. Only metabolites with absolute values of correlation coefficient >0.5 and P values <0.05 were left, and the shades of the color represent the strength of the relationship (red, black, and green represent the positive, no, and negative correlations, respectively). (B,C) Histograms of potential biomarkers in distal noncancerous tissue, adjacent noncancerous tissue, and HCT specimens. All data are presented as mean ± SE. Abbreviations: ALB, albumin; ALT, alanine aminotransferase; ANDS, androsterone sulfate; ANT, adjacent noncancerous tissue; AST, aspartate aminotransferase; BMI, body mass index; DBIL, direct bilirubin; DHTS, dihydrotestosterone sulfate; DMHC, dimethylheptanoylcarnitine; DNT, distal noncancerous tissue; FFA, free fatty acid; GCDCA, glycochenodeoxycholate; GCDCS, glycoursodeoxycholate sulfate; IBIL, indirect bilirubin; LPC, lysophosphatidylcholine; LPE, lysophosphatidylethanolamine; PC, phosphatidylcholine; TBIL, total bilirubin; TCDCA, taurochenodeoxycholate; TCA, taurocholate; TP, total protein.