Emerging biomolecules for practical theranostics of liver hepatocellular carcinoma

Liquid biopsy in hepatobiliary and pancreatic cancers: A paradigm shift in early detection, prognostic stratification, and perioperative monitoring

BACKGROUND

Hepatobiliary and pancreatic cancers are among the most lethal malignancies due to late-stage diagnosis and limited treatment options. Liquid biopsy has emerged as a minimally invasive tool for early cancer detection, prognosis, and therapeutic monitoring.

AIM

To concise the available data on liquid biopsy and establish its role in hepatobiliary surgeries.

METHODS

This systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009 guidelines. A comprehensive literature search was performed using PubMed, Scopus, Web of Science, and EMBASE for studies published up to March 2025. Studies assessing the role of circulating tumor DNA, circulating tumor cells, exosomes, and other liquid biopsy markers in hepatobiliary and pancreatic cancers were included. The risk of bias was evaluated using the Newcastle-Ottawa Scale for observational studies and the Cochrane Risk of Bias Tool for clinical trials.

RESULTS

Liquid biopsy demonstrated significant potential for early cancer detection, perioperative risk stratification, intraoperative surgical decision-making, and postoperative monitoring of minimal residual disease. However, challenges remain regarding standardization, sensitivity, and clinical validation.

CONCLUSION

Liquid biopsy represents a paradigm shift in hepatobiliary and pancreatic cancer management. Advancements in next-generation sequencing and artificial intelligence may enhance its clinical utility. Further large-scale studies are needed to establish standardized protocols for routine implementation.

Early monitoring values of oncogenic signalling molecules for hepatocellular carcinoma

The prevention and early diagnosis of liver cancer remains a global medical challenge. During the malignant transformation of hepatocytes, a variety of oncogenic cellular signalling molecules, such as novel high mobility group-Box 3, angiopoietin-2, Golgi protein 73, glypican-3, Wnt3a (a signalling molecule in the Wnt/β-catenin pathway), and secretory clusterin, can be expressed and secreted into the blood. These signalling molecules are derived from different signalling pathways and may not only participate in the malignant transformation of hepatocytes but also become early diagnostic indicators of hepatocarcinogenesis or specific targeted molecules for hepatocellular carcinoma therapy. This article reviews recent progress in the study of several signalling molecules as sensitive biomarkers for monitoring hepatocarcinogenesis.

Early monitoring values of oncogenic signalling molecules for hepatocellular carcinoma

The prevention and early diagnosis of liver cancer remains a global medical challenge. During the malignant transformation of hepatocytes, a variety of oncogenic cellular signalling molecules, such as novel high mobility group-Box 3, angiopoietin-2, Golgi protein 73, glypican-3, Wnt3a (a signalling molecule in the Wnt/β-catenin pathway), and secretory clusterin, can be expressed and secreted into the blood. These signalling molecules are derived from different signalling pathways and may not only participate in the malignant transformation of hepatocytes but also become early diagnostic indicators of hepatocarcinogenesis or specific targeted molecules for hepatocellular carcinoma therapy. This article reviews recent progress in the study of several signalling molecules as sensitive biomarkers for monitoring hepatocarcinogenesis.

Application of Machine Learning Techniques to Assess Alpha-Fetoprotein at Diagnosis of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver tumor and is associated with high mortality rates. Approximately 80% of cases occur in cirrhotic livers, posing a significant challenge for appropriate therapeutic management. Adequate screening programs in high-risk groups are essential for early-stage detection. The extent of extrahepatic tumor spread and hepatic functional reserve are recognized as two of the most influential prognostic factors. In this retrospective multicenter study, we utilized machine learning (ML) methods to analyze predictors of mortality at the time of diagnosis in a total of 208 patients. The eXtreme gradient boosting (XGB) method achieved the highest values in identifying key prognostic factors for HCC at diagnosis. The etiology of HCC was found to be the variable most strongly associated with a poorer prognosis. The widely used Barcelona Clinic Liver Cancer (BCLC) classification in our setting demonstrated superiority over the TNM classification. Although alpha-fetoprotein (AFP) remains the most commonly used biological marker, elevated levels did not correlate with reduced survival. Our findings suggest the need to explore new prognostic biomarkers for individualized management of these patients.