한빛사논문
Sun Jo Kim a,b, Cheol Woon Jung c, Nguyen Hoang Anh a,c, Young Cheol Yoon c, Nguyen Phuoc Long d, Soon-Sun Hong e, Eun Ju Cho f, Sung Won Kwon a,c,g
aResearch Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Republic of Korea
bCollege of Pharmacy, Chonnam National University, Gwangju 61186, Republic of Korea
cCollege of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea
dDepartment of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan 47392, Republic of Korea
eDepartment of Biomedical Science, College of Medicine, and Program in Biomedical Sciences and Engineering, Inha University, Incheon 22332, Republic of Korea
fDepartment of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
gPlant Genomics and Breeding Institute, Seoul National University, Seoul 08826, Republic of Korea
Corresponding author : Sung Won Kwon, Eun Ju Cho
Abstract
Introduction
The low sensitivity of alpha-fetoprotein (AFP) renders it unsuitable as a stand-alone marker for early hepatocellular carcinoma (eHCC) surveillance. Therefore, additional blood-based biomarkers with enhanced sensitivities are required.
Objectives
In light of the metabolic changes that are distinctive to eHCC development, the current study presents a panel of serum metabolites that may serve as noninvasive diagnostic indicators for patients with eHCC.
Methods
Serum samples obtained from normal control (NC), cirrhosis, and eHCC patients were analyzed by four different metabolomic platforms. A meta-analysis of very early-stage HCC transcriptomic datasets retrieved from public sources supports the integrated interpretation with metabolic changes.
Results
A total of 94 metabolites were significantly correlated with a progressive disease status. Integrated analysis of the significant metabolites and differentially expressed genes from meta-analysis emphasized metabolic pathways including bile acid biosynthesis, phenylalanine and tyrosine metabolism, and butanoate metabolism. The 11 metabolites associated with these pathways were compiled into a metabolite panel for use as diagnostic signatures. With an accuracy of 81.8%, compared with 45.4% for a model trained solely on AFP, the model enhanced its ability to differentiate between the three groups by incorporating a metabolite panel and AFP. Upon examining the trained models using receiver operating characteristic curves, the AFP and metabolite panel combined model exhibited greater area under the curve values in comparisons between NC and eHCC (1.000 versus 0.810) and cirrhosis and eHCC (0.926 versus 0.556). The result was consistent in an independent validation cohort.
Conclusion
This study emphasizes the role of circulating metabolite markers in the diagnosis of eHCC.
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