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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

Mean 1H Carr-Purcell-Meiboom-Gill NMR spectrum of serum samples with metabolite assignment. 1, CH3 bond of lipids, mainly VLDL; 1’, CH3 bond of lipids, mainly LDL; 1”, CH3 bond of lipids, mainly HDL; 2, CH2 bond of lipids; 3, CH2-CH2-COOC bond of lipids; 4, CH2-CH = bond of lipids; 5, CH2-CH2-COOC bond of lipids; 6, =CH-CH2-CH = bond of lipids; 7, Lipid O-CH2; 8, CH = CH bond of lipids
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Fig1: Mean 1H Carr-Purcell-Meiboom-Gill NMR spectrum of serum samples with metabolite assignment. 1, CH3 bond of lipids, mainly VLDL; 1’, CH3 bond of lipids, mainly LDL; 1”, CH3 bond of lipids, mainly HDL; 2, CH2 bond of lipids; 3, CH2-CH2-COOC bond of lipids; 4, CH2-CH = bond of lipids; 5, CH2-CH2-COOC bond of lipids; 6, =CH-CH2-CH = bond of lipids; 7, Lipid O-CH2; 8, CH = CH bond of lipids

Mentions: Serum samples (200 μL) were processed according to standard procedures for NMR metabolomic measurement [38]. One-dimensional 1H Carr-Purcell-Meiboom-Gill (CPMG) and Nuclear Overhauser effect spectroscopy (NOESY) NMR spectra were recorded for each serum sample on a Bruker Avance III spectrometer operating at 800.15 MHz 1H NMR frequency. Additional two-dimensional NMR spectra were recorded on a set of representative samples (one control and one case) to achieve assignment of the NMR signals observed in the 1H one-dimensional fingerprints to metabolites. The measured chemical shifts were compared to reference shifts of pure compounds using the HMDB [39], MMCB [40], and ChenomX NMR Suite (Chenomx Inc., Edmonton, Canada) databases. Figure 1 shows the mean CPMG spectrum with metabolite assignments. The detailed list of the 44 annotated metabolites is provided in Additional file 1: Table S1. NMR signals arising from lipids enabled the quantification of unsaturated lipids in the serum (signal at 5.28 ppm, resonance of -CH = CH- from unsaturated lipids) as well as terminal lipids methyls corresponding to several classes of lipoproteins: very-low-density lipoproteins (VLDL; δ 0.86 ppm), low-density lipoproteins (LDL; δ 0.84 ppm), and high-density lipoproteins (HDL; δ 0.82 ppm). After processing and calibration, each 1D NMR spectrum was reduced into bins of 0.001 ppm width over a chemical shift range of 0.5–9 ppm using the AMIX software (Bruker GmbH, Rheinstetten, Germany), giving a total number of 8,500 NMR variables.Fig. 1


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)

Mean 1H Carr-Purcell-Meiboom-Gill NMR spectrum of serum samples with metabolite assignment. 1, CH3 bond of lipids, mainly VLDL; 1’, CH3 bond of lipids, mainly LDL; 1”, CH3 bond of lipids, mainly HDL; 2, CH2 bond of lipids; 3, CH2-CH2-COOC bond of lipids; 4, CH2-CH = bond of lipids; 5, CH2-CH2-COOC bond of lipids; 6, =CH-CH2-CH = bond of lipids; 7, Lipid O-CH2; 8, CH = CH bond of lipids
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4581424&req=5

Fig1: Mean 1H Carr-Purcell-Meiboom-Gill NMR spectrum of serum samples with metabolite assignment. 1, CH3 bond of lipids, mainly VLDL; 1’, CH3 bond of lipids, mainly LDL; 1”, CH3 bond of lipids, mainly HDL; 2, CH2 bond of lipids; 3, CH2-CH2-COOC bond of lipids; 4, CH2-CH = bond of lipids; 5, CH2-CH2-COOC bond of lipids; 6, =CH-CH2-CH = bond of lipids; 7, Lipid O-CH2; 8, CH = CH bond of lipids
Mentions: Serum samples (200 μL) were processed according to standard procedures for NMR metabolomic measurement [38]. One-dimensional 1H Carr-Purcell-Meiboom-Gill (CPMG) and Nuclear Overhauser effect spectroscopy (NOESY) NMR spectra were recorded for each serum sample on a Bruker Avance III spectrometer operating at 800.15 MHz 1H NMR frequency. Additional two-dimensional NMR spectra were recorded on a set of representative samples (one control and one case) to achieve assignment of the NMR signals observed in the 1H one-dimensional fingerprints to metabolites. The measured chemical shifts were compared to reference shifts of pure compounds using the HMDB [39], MMCB [40], and ChenomX NMR Suite (Chenomx Inc., Edmonton, Canada) databases. Figure 1 shows the mean CPMG spectrum with metabolite assignments. The detailed list of the 44 annotated metabolites is provided in Additional file 1: Table S1. NMR signals arising from lipids enabled the quantification of unsaturated lipids in the serum (signal at 5.28 ppm, resonance of -CH = CH- from unsaturated lipids) as well as terminal lipids methyls corresponding to several classes of lipoproteins: very-low-density lipoproteins (VLDL; δ 0.86 ppm), low-density lipoproteins (LDL; δ 0.84 ppm), and high-density lipoproteins (HDL; δ 0.82 ppm). After processing and calibration, each 1D NMR spectrum was reduced into bins of 0.001 ppm width over a chemical shift range of 0.5–9 ppm using the AMIX software (Bruker GmbH, Rheinstetten, Germany), giving a total number of 8,500 NMR variables.Fig. 1

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