Metabolic phenotyping combined with transcriptomics metadata fortifies the diagnosis of early-stage Hepatocellular carcinoma

  • Sun Jo Kim
  • , Cheol Woon Jung
  • , Nguyen Hoang Anh
  • , Young Cheol Yoon
  • , Nguyen Phuoc Long
  • , Soon Sun Hong
  • , Eun Ju Cho*
  • , Sung Won Kwon*
  • *Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

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.

Original languageEnglish
Pages (from-to)153-163
Number of pages11
JournalJournal of Advanced Research
Volume74
DOIs
StatePublished - 08 2025
Externally publishedYes

Bibliographical note

Copyright © 2024. Published by Elsevier B.V.

Keywords

  • Carcinoma, Hepatocellular
  • Early diagnosis
  • Gene expression profiling
  • Machine learning
  • Metabolomics
  • Gene Expression Profiling/methods
  • Humans
  • Biomarkers, Tumor/blood
  • Middle Aged
  • Transcriptome
  • Metadata
  • Male
  • Early Detection of Cancer/methods
  • Phenotype
  • alpha-Fetoproteins/metabolism
  • Carcinoma, Hepatocellular/diagnosis
  • Liver Neoplasms/diagnosis
  • Female
  • Metabolomics/methods

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