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Comprehensive characterization and evaluation of hepatocellular carcinoma by LC–MS based serum metabolomics

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Abstract

The liver is an active organ in energy metabolism, and hepatocellular carcinomas (HCC) therefore unavoidably induce a series of metabolic alterations in the serum metabolome. In this study, 587 sera from HCC patients, hepatitis B virus (HBV) infected subjects and healthy control subjects were employed to characterize metabolic alterations using a liquid chromatography tandem mass spectrometry-based metabolomics approach. d-Galactose significantly increased whereas undecanoyl-l-carnitine and PE (P-18:0/0:0) decreased in HCC patients when compared with non-HCC controls (HBVs and NCs). These metabolites were finally identified as independent diagnostic factors for HCC discrimination. The combination of discriminative metabolites and alpha-fetoprotein could also significantly improve the diagnostic performance for HCC in terms of the sensitivity, specificity and area under the curve of the receiver operating characteristic curve; these three parameters displayed values of 0.96, 0.92 and 0.98, respectively. Furthermore, network and pathway analyses were conducted to explore the latent relationship among differential metabolites. In the network analysis, metabolites of diverse categories (including amino acids, dipeptides, phospholipids, fatty acids, steroids and acylcarnitines) reflected the correlation between metabolite categories on material and energy conversion in HCC. Meanwhile, metabolites mapped in the pathways for primary bile acid biosynthesis and alanine, aspartate and glutamate metabolism revealed a centrality and significant variances. These results indicated that substantial biochemical perturbations occurred in these pathways in HCC patients. In summary, our study revealed the systematic landscape for metabolic alterations in the serum of HCC patients and provides useful information to clinicians and biologists.

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Abbreviations

UPLC:

Ultra-performance liquid chromatography

MS:

Mass spectrometry

TOF:

Time-of-flight

NMR:

Nuclear magnetic resonance spectroscopy

HCC:

Hepatocellular carcinoma

HBV:

Hepatitis B virus

AFP:

α-Fetoprotein

ROC:

Receiver operating characteristic

AUC:

Area under the curve

PCA:

Principle component analysis

HCA:

Hierarchical cluster analysis

PLS-DA:

Partial least square discriminant analysis

OPLS-DA:

Orthogonal projection to latent structures discriminant analysis

RF:

Random forest

VIM:

Variable importance

QC:

Quality control

RT:

Retention time

m/z :

Mass-to-charge ratio

ALT:

Alanine aminotransferase

AST:

Asparate aminotransferase

BPC:

Basic peak chromatogram

TNM:

Tumor node metastasis

GLS:

Glutaminase

ROS:

Reactive oxygen species

PC:

Phosphatidylcholine

PE:

Phosphatidylethanolamine

LPC:

Lysophosphatidylcholine

LPE:

Lysophosphatidylethanolamine

FA:

Fatty acid

DP:

Dipeptide

ST:

Sterol lipid

AA:

Amino acid

Se:

Sensitivity

Sp:

Specificity

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Acknowledgments

We express our appreciation to all the participants for donating their blood and clinical characteristic information to this study. We thank the stuff from the Department of Epidemiology and Biostatistics, Harbin Medical University, who helped in the data statistics and multivariate modeling. This work was supported by grant from Open Project of State Key Laboratory of Urban Water Resource and Environment of Harbin Institute of Technology (No. ES201115).

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional committees and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Correspondence to Yubao Zhang or Yu Li.

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Lu, X., Nie, H., Li, Y. et al. Comprehensive characterization and evaluation of hepatocellular carcinoma by LC–MS based serum metabolomics. Metabolomics 11, 1381–1393 (2015). https://doi.org/10.1007/s11306-015-0797-4

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  • DOI: https://doi.org/10.1007/s11306-015-0797-4

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