Internal medicine

Fetus

Liver cancer

Hepatocellular carcinoma

Bioinformatics

Biology

Cancer

Cell-free fetal DNA

Nucleic acid

Prenatal diagnosis

Pregnancy

Computational biology

Cancer research

Genetics

Medicine

Nature Reviews Gastroenterology & Hepatology

Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal causes of cancer-related death worldwide. The treatment of HCC remains challenging and is largely predicated on early diagnosis. Surveillance of high-risk groups using abdominal ultrasonography, with or without serum analysis of α-fetoprotein (AFP), can permit detection of early, potentially curable tumours, but is limited by its insensitivity. Reviewed here are two current approaches that aim to address this limitation. The first is to use old re-emerged empirically derived biomarkers such as AFP, now applied within statistical models. The second is to use circulating nucleic acid biomarkers, which include cell-free DNA (for example, circulating tumour DNA, cell-free mitochondrial DNA and cell-free viral DNA) and cell-free RNA, applying modern molecular biology-based technologies and machine learning techniques closely allied to the underlying biology of cancer. Taken together, these approaches are likely to be complementary. Both hold considerable promise for achieving earlier diagnosis as well as offering additional functionalities including improved monitoring of therapy and prediction of response thereto. Surveillance of hepatocellular carcinoma (HCC), one of the most lethal solid cancers globally, is insensitive for the detection of early-stage tumours. In this Review, the authors discuss HCC biomarkers that can improve early diagnosis, therapy monitoring and prediction of therapy response.

Circulating biomarkers in the diagnosis and management of hepatocellular carcinoma