Biomarker Development for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) remains a leading cause of cancer mortality worldwide, owing in part to the often late stage at which it is diagnosed. Biomarker development for HCC aims to improve early detection, stratify risk, guide therapeutic choices and monitor disease progression or recurrence. Traditional serum markers such as alpha-fetoprotein (AFP) have limited sensitivity and specificity in screening high-risk populations, especially in the context of evolving aetiologies such as non-alcoholic steatohepatitis. Advances in proteomics, genomics and metabolomics have unveiled panels of candidate proteins, nucleic acids and metabolites that reflect the underlying tumour biology, including aberrant signalling in Wnt/β-catenin, PI3K/Akt and TGF-β pathways. Integrative multiomics approaches, reinforced by machine learning, now allow the interrogation of complex molecular signatures and the construction of predictive algorithms. Beyond circulating proteins, novel modalities include tumour-derived extracellular vesicles, circulating tumour DNA and immune cell profiles. Validation in diverse patient cohorts has revealed biomarker combinations that outperform single analytes, offering the prospect of personalised surveillance protocols and earlier intervention strategies. Translational efforts are under way to deploy point-of-care assays and risk calculators that could reshape clinical practice by shifting the diagnostic window towards curative stages.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Biomarker Development for Hepatocellular Carcinoma publication trend

The graph below shows the total number of articles in biomarker development for hepatocellular carcinoma across all publications each year (not limited to Nature Index journals).

Technical terms

Alpha-fetoprotein (AFP): An oncofoetal glycoprotein originally used as a serum marker for HCC screening, with limited sensitivity in early disease.

Biomarker: A measurable indicator of a biological state or condition, employed for diagnosis, prognosis or therapeutic monitoring.

Proteomics: The large-scale study of proteins, their structures, functions and interactions within a given biological sample.

Multiomics: An integrative approach combining data from different molecular levels (genome, transcriptome, proteome, metabolome) to characterise disease processes.

Machine learning: Computational methods that identify patterns in complex datasets and build predictive models without explicit programming.

Sensitivity and specificity: Metrics of diagnostic performance; sensitivity denotes the proportion of true positives correctly identified, specificity the proportion of true negatives.

Midkine (MDK): A heparin-binding growth factor overexpressed in several cancers, evaluated as a serum biomarker for early HCC detection and prognosis.

References

1. Inhibition of Dickkopf-1 enhances the anti-tumor efficacy of sorafenib via inhibition of the PI3K/Akt and Wnt/β-catenin pathways in hepatocellular carcinoma. Cell Communication and Signaling (2023).
2. Midkine (MDK) in Hepatocellular Carcinoma: More than a Biomarker. Cells (2024).
3. AGO1 may influence the prognosis of hepatocellular carcinoma through TGF-β pathway. Cell Death & Disease (2018).
4. Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine. Biomedicines (2021).
5. Midkine Increases Diagnostic Yield in AFP Negative and NASH-Related Hepatocellular Carcinoma. PLOS ONE (2016).
6. The Value of Serum Midkine Level in Diagnosis of Hepatocellular Carcinoma. International Journal of Hepatology (2015).