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Metabolomic profiles of hepatocellular carcinoma in a European prospective cohort.

Fages A, Duarte-Salles T, Stepien M, Ferrari P, Fedirko V, Pontoizeau C, Trichopoulou A, Aleksandrova K, Tjønneland A, Olsen A, Clavel-Chapelon F, Boutron-Ruault MC, Severi G, Kaaks R, Kuhn T, Floegel A, Boeing H, Lagiou P, Bamia C, Trichopoulos D, Palli D, Pala V, Panico S, Tumino R, Vineis P, Bueno-de-Mesquita HB, Peeters PH, Weiderpass E, Agudo A, Molina-Montes E, Huerta JM, Ardanaz E, Dorronsoro M, Sjöberg K, Ohlsson B, Khaw KT, Wareham N, Travis RC, Schmidt JA, Cross A, Gunter M, Riboli E, Scalbert A, Romieu I, Elena-Herrmann B, Jenab M - BMC Med (2015)

Bottom Line: A metabolic pattern associated with HCC risk comprised of perturbations in fatty acid oxidation and amino acid, lipid, and carbohydrate metabolism was observed.Sixteen metabolites of either endogenous or exogenous origin were found to be significantly associated with HCC risk.Our results show clear metabolic alterations from early stages of HCC development with application for better etiologic understanding, prevention, and early detection of this increasingly common cancer.

View Article: PubMed Central - PubMed

Affiliation: Institut des Sciences Analytiques, Centre de RMN à très hauts champs, CNRS/ENS Lyon/UCB Lyon-1, Université de Lyon, 5 rue de la Doua, 69100, Villeurbanne, France.

ABSTRACT

Background: Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is difficult to diagnose and has limited treatment options with a low survival rate. Aside from a few key risk factors, such as hepatitis, high alcohol consumption, smoking, obesity, and diabetes, there is incomplete etiologic understanding of the disease and little progress in identification of early risk biomarkers.

Methods: To address these aspects, an untargeted nuclear magnetic resonance metabolomic approach was applied to pre-diagnostic serum samples obtained from first incident, primary HCC cases (n = 114) and matched controls (n = 222) identified from amongst the participants of a large European prospective cohort.

Results: A metabolic pattern associated with HCC risk comprised of perturbations in fatty acid oxidation and amino acid, lipid, and carbohydrate metabolism was observed. Sixteen metabolites of either endogenous or exogenous origin were found to be significantly associated with HCC risk. The influence of hepatitis infection and potential liver damage was assessed, and further analyses were made to distinguish patterns of early or later diagnosis.

Conclusion: Our results show clear metabolic alterations from early stages of HCC development with application for better etiologic understanding, prevention, and early detection of this increasingly common cancer.

No MeSH data available.


Related in: MedlinePlus

NMR Metabolomic discrimination between HCC cases (n = 114) and matched controls (n = 222) based on 1H Carr-Purcell-Meiboom-Gill NMR data. (a) Orthogonal partial least-square (O-PLS) score plot of NMR spectra, R2 = 35 %, Q2 = 21 %. (b) O-PLS metabolic signature colored according to the correlation between NMR variables and case–control status after significance to ANOVA tests followed by Benjamini-Hochberg multiple correction (non-significant NMR variables are colored in grey). The validation of the model is presented in Additional file 1: Figure S1a. 1, CH3 bond of lipids mainly very-low-density lipoproteins; 2, Leucine; 3, Isoleucine; 4, Valine; 5, Propylene glycol; 6, Ethanol; 7, CH2 bond of lipids; 8, CH2-CH2-COOC bond of lipids; 9, Acetate; 10, CH2-CH = bond of lipids; 11, N-acetyl glycoproteins; 12, Acetone and CH2-CH2-COOC bond of lipids; 13, Glutamate; 14, Glutamine; 15, Citrate; 16, =CH-CH2-CH = bond of lipids; 17, Choline; 18, Glucose; 19, Lipid O-CH2; 20, Mannose and lipids; 21, CH = CH bond of lipids; 22, Tyrosine; 23 Phenylalanine. An equivalent metabolic signature obtained from 1H NOESY NMR data is presented in Additional file 1: Figure S1b. (c) ROC analyses including AFP, liver function score, O-PLS score, O-PLS cross-validated (CV) status, and a combination between O-PLS CV status and AFP or liver function score. The ROC of O-PLS CV status and the combination of O-PLS CV status and AFP overlap. The characteristics of each model are presented in Table 3
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Fig2: NMR Metabolomic discrimination between HCC cases (n = 114) and matched controls (n = 222) based on 1H Carr-Purcell-Meiboom-Gill NMR data. (a) Orthogonal partial least-square (O-PLS) score plot of NMR spectra, R2 = 35 %, Q2 = 21 %. (b) O-PLS metabolic signature colored according to the correlation between NMR variables and case–control status after significance to ANOVA tests followed by Benjamini-Hochberg multiple correction (non-significant NMR variables are colored in grey). The validation of the model is presented in Additional file 1: Figure S1a. 1, CH3 bond of lipids mainly very-low-density lipoproteins; 2, Leucine; 3, Isoleucine; 4, Valine; 5, Propylene glycol; 6, Ethanol; 7, CH2 bond of lipids; 8, CH2-CH2-COOC bond of lipids; 9, Acetate; 10, CH2-CH = bond of lipids; 11, N-acetyl glycoproteins; 12, Acetone and CH2-CH2-COOC bond of lipids; 13, Glutamate; 14, Glutamine; 15, Citrate; 16, =CH-CH2-CH = bond of lipids; 17, Choline; 18, Glucose; 19, Lipid O-CH2; 20, Mannose and lipids; 21, CH = CH bond of lipids; 22, Tyrosine; 23 Phenylalanine. An equivalent metabolic signature obtained from 1H NOESY NMR data is presented in Additional file 1: Figure S1b. (c) ROC analyses including AFP, liver function score, O-PLS score, O-PLS cross-validated (CV) status, and a combination between O-PLS CV status and AFP or liver function score. The ROC of O-PLS CV status and the combination of O-PLS CV status and AFP overlap. The characteristics of each model are presented in Table 3

Mentions: The O-PLS analysis presented in Fig. 2a shows a metabolic profile discriminating between HCC cases and the matched controls (R2 = 35 %, Q2 = 21 %). The metabolic signature (Fig. 2b) associated with HCC occurrence presented (1) higher levels in the aromatic amino acids (AAA) tyrosine and phenylalanine, glutamate, acetate, citrate, glucose, propylene glycol, and ethanol; (2) lower levels in unsaturated lipids and VLDL, N-acetyl glycoproteins, choline, glutamine, acetone, mannose and the branched-chain amino-acids (BCAA) valine, leucine, and isoleucine levels, compared to the control group. The corresponding P values, q values, and fold changes of the metabolites are presented in Table 2. The ROC analyses (Fig. 2c) of the metabolic signature (O-PLS score) and of the cross-validated data (O-PLS CV status) presented an AUC of 85 % and 74 %, respectively (Table 3). The ROC parameters obtained from AFP compared to the combination of O-PLS CV status with AFP were increased after combining the variables (AUC 73 % vs. 75 %, specificity 65.3 % vs. 80.6 %, sensitivity 71.9 % vs. 75.4 %, accuracy 67.5 % vs. 78.9 %). Multivariable adjusted CLR models showed that AAA (per 1-SD), tyrosine (OR = 2.46; 95 % CI, 1.65–3.6), phenylalanine (OR = 2.07; 95 % CI, 1.40–3.06), glutamate (OR = 2.44; 95 % CI, 1.54–3.87), citrate (OR = 1.76; 95 % CI, 1.22–2.54), glucose (OR = 1.67; 95 % CI, 1.19–2.35), and propylene glycol (OR = 2.20; 95 % CI, 1.06–4.60) were associated with a statistically significant higher HCC risk. In contrast, BCAA, leucine (OR = 0.60; 95 % CI, 0.43–0.85), isoleucine (OR = 0.72; 95 % CI, 0.53–0.98), choline (OR = 0.45; 95 % CI, 0.31–0.65), N-acetyl glycoproteins (OR = 0.46; 95 % CI, 0.32–0.67), unsaturated lipids (OR = 0.36; 95 % CI, 0.21–0.63), and VLDL (OR = 0.52; 95 % CI, 0.36–0.74) were inversely associated with HCC risk (Table 4).Fig. 2


Metabolomic profiles of hepatocellular carcinoma in a European prospective cohort.

Fages A, Duarte-Salles T, Stepien M, Ferrari P, Fedirko V, Pontoizeau C, Trichopoulou A, Aleksandrova K, Tjønneland A, Olsen A, Clavel-Chapelon F, Boutron-Ruault MC, Severi G, Kaaks R, Kuhn T, Floegel A, Boeing H, Lagiou P, Bamia C, Trichopoulos D, Palli D, Pala V, Panico S, Tumino R, Vineis P, Bueno-de-Mesquita HB, Peeters PH, Weiderpass E, Agudo A, Molina-Montes E, Huerta JM, Ardanaz E, Dorronsoro M, Sjöberg K, Ohlsson B, Khaw KT, Wareham N, Travis RC, Schmidt JA, Cross A, Gunter M, Riboli E, Scalbert A, Romieu I, Elena-Herrmann B, Jenab M - BMC Med (2015)

NMR Metabolomic discrimination between HCC cases (n = 114) and matched controls (n = 222) based on 1H Carr-Purcell-Meiboom-Gill NMR data. (a) Orthogonal partial least-square (O-PLS) score plot of NMR spectra, R2 = 35 %, Q2 = 21 %. (b) O-PLS metabolic signature colored according to the correlation between NMR variables and case–control status after significance to ANOVA tests followed by Benjamini-Hochberg multiple correction (non-significant NMR variables are colored in grey). The validation of the model is presented in Additional file 1: Figure S1a. 1, CH3 bond of lipids mainly very-low-density lipoproteins; 2, Leucine; 3, Isoleucine; 4, Valine; 5, Propylene glycol; 6, Ethanol; 7, CH2 bond of lipids; 8, CH2-CH2-COOC bond of lipids; 9, Acetate; 10, CH2-CH = bond of lipids; 11, N-acetyl glycoproteins; 12, Acetone and CH2-CH2-COOC bond of lipids; 13, Glutamate; 14, Glutamine; 15, Citrate; 16, =CH-CH2-CH = bond of lipids; 17, Choline; 18, Glucose; 19, Lipid O-CH2; 20, Mannose and lipids; 21, CH = CH bond of lipids; 22, Tyrosine; 23 Phenylalanine. An equivalent metabolic signature obtained from 1H NOESY NMR data is presented in Additional file 1: Figure S1b. (c) ROC analyses including AFP, liver function score, O-PLS score, O-PLS cross-validated (CV) status, and a combination between O-PLS CV status and AFP or liver function score. The ROC of O-PLS CV status and the combination of O-PLS CV status and AFP overlap. The characteristics of each model are presented in Table 3
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Related In: Results  -  Collection

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Fig2: NMR Metabolomic discrimination between HCC cases (n = 114) and matched controls (n = 222) based on 1H Carr-Purcell-Meiboom-Gill NMR data. (a) Orthogonal partial least-square (O-PLS) score plot of NMR spectra, R2 = 35 %, Q2 = 21 %. (b) O-PLS metabolic signature colored according to the correlation between NMR variables and case–control status after significance to ANOVA tests followed by Benjamini-Hochberg multiple correction (non-significant NMR variables are colored in grey). The validation of the model is presented in Additional file 1: Figure S1a. 1, CH3 bond of lipids mainly very-low-density lipoproteins; 2, Leucine; 3, Isoleucine; 4, Valine; 5, Propylene glycol; 6, Ethanol; 7, CH2 bond of lipids; 8, CH2-CH2-COOC bond of lipids; 9, Acetate; 10, CH2-CH = bond of lipids; 11, N-acetyl glycoproteins; 12, Acetone and CH2-CH2-COOC bond of lipids; 13, Glutamate; 14, Glutamine; 15, Citrate; 16, =CH-CH2-CH = bond of lipids; 17, Choline; 18, Glucose; 19, Lipid O-CH2; 20, Mannose and lipids; 21, CH = CH bond of lipids; 22, Tyrosine; 23 Phenylalanine. An equivalent metabolic signature obtained from 1H NOESY NMR data is presented in Additional file 1: Figure S1b. (c) ROC analyses including AFP, liver function score, O-PLS score, O-PLS cross-validated (CV) status, and a combination between O-PLS CV status and AFP or liver function score. The ROC of O-PLS CV status and the combination of O-PLS CV status and AFP overlap. The characteristics of each model are presented in Table 3
Mentions: The O-PLS analysis presented in Fig. 2a shows a metabolic profile discriminating between HCC cases and the matched controls (R2 = 35 %, Q2 = 21 %). The metabolic signature (Fig. 2b) associated with HCC occurrence presented (1) higher levels in the aromatic amino acids (AAA) tyrosine and phenylalanine, glutamate, acetate, citrate, glucose, propylene glycol, and ethanol; (2) lower levels in unsaturated lipids and VLDL, N-acetyl glycoproteins, choline, glutamine, acetone, mannose and the branched-chain amino-acids (BCAA) valine, leucine, and isoleucine levels, compared to the control group. The corresponding P values, q values, and fold changes of the metabolites are presented in Table 2. The ROC analyses (Fig. 2c) of the metabolic signature (O-PLS score) and of the cross-validated data (O-PLS CV status) presented an AUC of 85 % and 74 %, respectively (Table 3). The ROC parameters obtained from AFP compared to the combination of O-PLS CV status with AFP were increased after combining the variables (AUC 73 % vs. 75 %, specificity 65.3 % vs. 80.6 %, sensitivity 71.9 % vs. 75.4 %, accuracy 67.5 % vs. 78.9 %). Multivariable adjusted CLR models showed that AAA (per 1-SD), tyrosine (OR = 2.46; 95 % CI, 1.65–3.6), phenylalanine (OR = 2.07; 95 % CI, 1.40–3.06), glutamate (OR = 2.44; 95 % CI, 1.54–3.87), citrate (OR = 1.76; 95 % CI, 1.22–2.54), glucose (OR = 1.67; 95 % CI, 1.19–2.35), and propylene glycol (OR = 2.20; 95 % CI, 1.06–4.60) were associated with a statistically significant higher HCC risk. In contrast, BCAA, leucine (OR = 0.60; 95 % CI, 0.43–0.85), isoleucine (OR = 0.72; 95 % CI, 0.53–0.98), choline (OR = 0.45; 95 % CI, 0.31–0.65), N-acetyl glycoproteins (OR = 0.46; 95 % CI, 0.32–0.67), unsaturated lipids (OR = 0.36; 95 % CI, 0.21–0.63), and VLDL (OR = 0.52; 95 % CI, 0.36–0.74) were inversely associated with HCC risk (Table 4).Fig. 2

Bottom Line: A metabolic pattern associated with HCC risk comprised of perturbations in fatty acid oxidation and amino acid, lipid, and carbohydrate metabolism was observed.Sixteen metabolites of either endogenous or exogenous origin were found to be significantly associated with HCC risk.Our results show clear metabolic alterations from early stages of HCC development with application for better etiologic understanding, prevention, and early detection of this increasingly common cancer.

View Article: PubMed Central - PubMed

Affiliation: Institut des Sciences Analytiques, Centre de RMN à très hauts champs, CNRS/ENS Lyon/UCB Lyon-1, Université de Lyon, 5 rue de la Doua, 69100, Villeurbanne, France.

ABSTRACT

Background: Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is difficult to diagnose and has limited treatment options with a low survival rate. Aside from a few key risk factors, such as hepatitis, high alcohol consumption, smoking, obesity, and diabetes, there is incomplete etiologic understanding of the disease and little progress in identification of early risk biomarkers.

Methods: To address these aspects, an untargeted nuclear magnetic resonance metabolomic approach was applied to pre-diagnostic serum samples obtained from first incident, primary HCC cases (n = 114) and matched controls (n = 222) identified from amongst the participants of a large European prospective cohort.

Results: A metabolic pattern associated with HCC risk comprised of perturbations in fatty acid oxidation and amino acid, lipid, and carbohydrate metabolism was observed. Sixteen metabolites of either endogenous or exogenous origin were found to be significantly associated with HCC risk. The influence of hepatitis infection and potential liver damage was assessed, and further analyses were made to distinguish patterns of early or later diagnosis.

Conclusion: Our results show clear metabolic alterations from early stages of HCC development with application for better etiologic understanding, prevention, and early detection of this increasingly common cancer.

No MeSH data available.


Related in: MedlinePlus