Serum Metabolites as Diagnostic Biomarkers for Cholangiocarcinoma, Hepatocellular Carcinoma, and Primary Sclerosing Cholangitis

Abstract

Early and differential diagnosis of intrahepatic cholangiocarcinoma (iCCA) and hepatocellular carcinoma (HCC) by noninvasive methods represents a current clinical challenge. The analysis of low-molecular-weight metabolites by new high-throughput techniques is a strategy for identifying biomarkers. Here, we have investigated whether serum metabolome can provide useful biomarkers in the diagnosis of iCCA and HCC and could discriminate iCCA from HCC. Because primary sclerosing cholangitis (PSC) is a risk factor for CCA, serum metabolic profiles of PSC and CCA have also been compared. The analysis of the levels of lipids and amino acids in the serum of patients with iCCA, HCC, and PSC and healthy individuals (n = 20/group) showed differential profiles. Several metabolites presented high diagnostic value for iCCA versus control, HCC versus control, and PSC versus control, with areas under the receiver operating characteristic curve (AUC) greater than those found in serum for the nonspecific tumor markers carbohydrate antigen 19-9 (CA 19-9) and alpha-fetoprotein (AFP), commonly used to help in the diagnosis of iCCA and HCC, respectively. The development of an algorithm combining glycine, aspartic acid, SM(42:3), and SM(43:2) permitted to accurately differentiate in the diagnosis of both types of tumors (biopsy-proven). The proposed model yielded 0.890 AUC, 75% sensitivity, and 90% specificity. Another algorithm by combination of PC(34:3) and histidine accurately permitted to differentiate PSC from iCCA, with an AUC of 0.990, 100% sensitivity, and 70% specificity. These results were validated in independent cohorts of 14-15 patients per group and compared with profiles found in patients with nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. Conclusion: Specific changes in serum concentrations of certain metabolites are useful to differentiate iCCA from HCC or PSC, and could help in the early diagnosis of these diseases.

Figure 1

Comparative serum metabolomic profiles of patients with iCCA and controls in the discovery cohort. (A) Volcano plot (−log₁₀[P value] and log₂[fold‐change]) of the serum metabolic ion features of patients with iCCA compared with controls. (B) Percentage of metabolite classes significantly different in the serum of patients with iCCA compared with healthy individuals. (C) Diagnostic capacity of the nine selected metabolites also similarly found altered in the validation cohort. Abbreviations: AA, amino acids; AC, acylcarnitines; AUC, area under the receiver operating characteristic curve; BA, bile acids; Cer, ceramides; ChoE, cholesteryl esters; CMH, monohexosylceramides; DG, diglycerides; DHEAS, dehydroepiandrosterone sulfate; FC, fold‐change; FSB, free sphingoid bases; GCA, glychocolic acid; GCDCA, glychochenodeoxycholic acid; LPC, lysophosphatidylcholines; LPE, lysophosphatidylethanolamines; LPI, lysophosphatidylinositols; MUFA, monounsaturated fatty acids; NAE, N‐acyl ethanolamines; oxFA, oxidized fatty acids; PC, phosphatidylcholines; PE, phosphatidylethanolamines; PI, phosphatidylinositols; PUFA, polyunsaturated fatty acids; SEN, sensitivity; SFA, saturated fatty acids; SM, sphingomyelins; SPE, specificity; ST, steroids; TG, triglycerides.

Figure 2

Comparative serum metabolomic profiles of patients with HCC and controls in the discovery cohort. (A) Volcano plot (−log₁₀[P value] and log₂[fold‐change]) of the serum metabolic ion features of patients with HCC compared with controls. (B) Percentage of metabolite classes significantly different in the serum of patients with HCC compared with healthy individuals. (C) Diagnostic capacity of the 13 selected metabolites also similarly found altered in the validation cohort. Abbreviations: AA, amino acids; AC, acylcarnitines; AUC, area under the receiver operating characteristic curve; BA, bile acids; Cer, ceramides; ChoE, cholesteryl esters; CMH, monohexosylceramides; DG, diglycerides; FC, fold‐change; FSB, free sphingoid bases; LPC, lysophosphatidylcholines; LPE, lysophosphatidylethanolamines; LPI, lysophosphatidylinositols; MUFA, monounsaturated fatty acids; NAE, N‐acyl ethanolamines; oxFA, oxidized fatty acids; PC, phosphatidylcholines; PE, phosphatidylethanolamines; PI, phosphatidylinositols; PUFA, polyunsaturated fatty acids; SEN, sensitivity; SFA, saturated fatty acids; SM, sphingomyelins; SPE, specificity; ST, steroids; TG, triglycerides.

Figure 3

Comparative serum metabolomic profiles of patients with HCC and iCCA in the discovery cohort. (A) Volcano plot (−log₁₀[P value] and log₂[fold‐change]) of the serum metabolic ion features of patients with iCCA compared with HCC. (B) Percentage of metabolite classes significantly different in the serum of patients with HCC versus iCCA. (C) Diagnostic capacity of the six selected metabolites also similarly found altered in the validation cohort. Abbreviations: AA, amino acids; AC, acylcarnitines; AUC, area under the receiver operating characteristic curve; BA, bile acids; Cer, ceramides; ChoE, cholesteryl esters; CMH, monohexosylceramides; DG, diglycerides; FC, fold‐change; FSB, free sphingoid bases; LPC, lysophosphatidylcholines; LPE, lysophosphatidylethanolamines; LPI, lysophosphatidylinositols; MUFA, monounsaturated fatty acids; NAE, N‐acyl ethanolamines; oxFA, oxidized fatty acids; PC, phosphatidylcholines; PE, phosphatidylethanolamines; PI, phosphatidylinositols; PUFA, polyunsaturated fatty acids; SEN, sensitivity; SFA, saturated fatty acids; SM, sphingomyelins; SPE, specificity; ST, steroids; TG, triglycerides.

Figure 4

Comparative serum metabolomic profiles of patients with PSC and controls in the discovery cohort. (A) Volcano plot (−log₁₀[P value] and log₂[fold‐change]) of the serum metabolic ion features of patients with PSC compared with controls. (B) Percentage of metabolite classes significantly different in the serum of patients with PSC compared with healthy individuals. (C) Diagnostic capacity of the 24 selected metabolites also similarly found altered in the validation cohort. Abbreviations: AA, amino acids; AC, acylcarnitines; AUC, area under the receiver operating characteristic curve; BA, bile acids; Cer, ceramides; ChoE, cholesteryl esters; CMH, monohexosylceramides; DG, diglycerides; FC, fold‐change; FSB, free sphingoid bases; LPC, lysophosphatidylcholines; LPE, lysophosphatidylethanolamines; LPI, lysophosphatidylinositols; MUFA, monounsaturated fatty acids; NAE, N‐acyl ethanolamines; oxFA, oxidized fatty acids; PC, phosphatidylcholines; PE, phosphatidylethanolamines; PI, phosphatidylinositols; PUFA, polyunsaturated fatty acids; SEN, sensitivity; SFA, saturated fatty acids; SM, sphingomyelins; SPE, specificity; ST, steroids; TG, triglycerides.

Figure 5

Comparative serum metabolomic profiles of patients with iCCA and PSC in the discovery cohort. (A) Volcano plot (−log₁₀[P value] and log₂[fold‐change]) of the serum metabolic ion features of patients with iCCA compared with PSC. (B) Percentage of metabolite classes significantly different in the serum of patients with iCCA as compared with PSC. (C) Diagnostic capacity of the 12 selected metabolites also similarly found altered in the validation cohort. Abbreviations: AA, amino acids; AC, acylcarnitines; AUC, area under the receiver operating characteristic curve; BA, bile acids; Cer, ceramides; ChoE, cholesteryl esters; CMH, monohexosylceramides; DG, diglycerides; FC, fold‐change; FSB, free sphingoid bases; LPC, lysophosphatidylcholines; LPE, lysophosphatidylethanolamines; LPI, lysophosphatidylinositols; MUFA, monounsaturated fatty acids; NAE, N‐acyl ethanolamines; oxFA, oxidized fatty acids; PC, phosphatidylcholines; PE, phosphatidylethanolamines; PI, phosphatidylinositols; PUFA, polyunsaturated fatty acids; SEN, sensitivity; SFA, saturated fatty acids; SM, sphingomyelins; SPE, specificity; ST, steroids; TG, triglycerides.

Figure 6

Diagnostic prediction capacity of the selected metabolites in iCCA versus HCC, and iCCA versus PSC in discovery and validation cohorts. (A,B) Combination of aspartic acid, glycine, SM(42:3), and SM(43:2) in serum iCCA versus HCC. (C) Values of AUC, sensitivity, and specificity of other algorithms to differentiate iCCA versus HCC. (D,E) Combination of histidine and PC(34:3) in serum iCCA versus PSC (0.990 AUC, 100% sensitivity, 70% specificity, 76.9% positive predictive value, and 100% negative predictive value). Linear discriminant (A,C,D) analysis was carried out through LOOCV. (B,E) Box plot diagrams for each combination, respectively. Abbreviations: AUC, area under the receiver operating characteristic curve; N, number of metabolites in the algorithm; SEN, sensitivity; SPE, specificity. The asterisks indicate combination of metabolites.

Figure 7

Metabolic profiling workflow. (A) Metabolite extraction was accomplished by fractionating samples into pools of species with similar physicochemical properties using appropriate combinations of organic solvents, after the addition of an internal standard. (B) Three separate UHPLC‐MS–based platforms were used to perform optimal profiling of UPLC‐MS of the serum metabolome. (C) Data preprocessing generated a list of chromatographic peak areas for the metabolites detected in each sample injection. An approximated linear detection range was defined for each identified metabolite. Intra‐ and inter‐batch normalization was carried out with internal standard correction and quality control calibration. (D) Once normalized, multivariate and univariate data analyses were performed. (E) Selection of the best candidate biomarkers for the differential diagnosis of HCC, iCCA, and PSC. The asterisks indicate combination of metabolites. Abbreviation: QC, quality control.