Abstract
Hepatocellular carcinoma (HCC) is a common malignancy that has region specific etiologies. Unfortunately, 85% of cases of HCC are diagnosed at an advanced stage. Reliable biomarkers for the early diagnosis of HCC are urgently required to reduced mortality and therapeutic expenditure. We established a non-targeted gas chromatography–time of flight–mass spectrometry (GC-TOFMS) metabolomics method in conjunction with Random Forests (RF) analysis based on 201 serum samples from healthy controls (NC), hepatitis B virus (HBV), liver cirrhosis (LC) and HCC patients to explore the metabolic characteristics in the progression of hepatocellular carcinogenesis. Ultimately, 15 metabolites were identified intimately associated with the process. Phenylalanine, malic acid and 5-methoxytryptamine for HBV vs. NC, palmitic acid for LC vs. HBV, and asparagine and β-glutamate for HCC vs. LC were screened as the liver disease-specific potential biomarkers with an excellent discriminant performance. All the metabolic perturbations in these liver diseases are associated with pathways for energy metabolism, macromolecular synthesis, and maintaining the redox balance to protect tumor cells from oxidative stress.
Highlights
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide with rising incidence[1], especially in developing countries, which account for 84% of the total incidence and 83% of the total deaths[2]
The metabolic profiling of serum samples was performed by using the GC-TOFMS in a random order and the representative total ion current (TIC) chromatograms of NC, hepatitis B virus (HBV), liver cirrhosis (LC) and HCC are shown in Supplementary Figure S1
Most HCC cases are developed from liver cirrhosis (LC), which is primarily caused by chronic HBV24,25
Summary
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide with rising incidence[1], especially in developing countries, which account for 84% of the total incidence and 83% of the total deaths[2]. We discriminated the serum profiles of healthy controls (NC), HBV, LC and HCC to comprehensively investigate the metabolites associated with hepatocarcinogenesis and identify potential biomarkers of each liver disease status. To obtain reliable markers and accurate diagnosis of HBV, LC and HCC, unsupervised principal components analysis (PCA) and supervised projection to latent structure with discriminant analysis (OPLS-DA), Random Forests (RF), the binary logistic regression and the Bayes’ multi-group stepwise discriminant analysis were applied on the training set, and the potential biomarkers were further validated by the validation set. We identified 15 metabolites related to the stepwise hepatocarcinogenesis, uncovered robust and technically validated potential biomarkers of HBV, LC and HCC and established a reliable Bayes’ multi-group stepwise discriminant model that can aid clinical diagnosis and guide therapeutic decisions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
