PMID: 39994676 https://pubmed.ncbi.nlm.nih.gov/39994676/

Abstract

BACKGROUND: Hepatocellular carcinoma (HCC) is the third deadliest malignant tumor worldwide. Most patients are initially diagnosed as HCC at advanced stages and are too late for radical treatment by surgery, resulting in poor prognosis. Over 50% of the HCC patients are caused by hepatitis B virus (HBV) infection. Therefore, effective early identification of HCC in the high-risk population with HBV infection is crucial for early intervention of HCC.

METHODS: We employed plasma lipidomics to identify critical lipid classes associated with tumorigenesis in the high-risk population with HBV infection. Potential regulatory mechanisms are validated at multi-omic levels. A machine learning algorithm is used for feature selection and diagnostic modelling, and performance of the models is evaluated by ROC curves.

RESULTS: We unveiled varied profiles of plasma lipid metabolites in a cohort of 57 HBV-related HCC subjects, 57 HBV-related liver cirrhosis (LC) subjects and 61 chronic hepatitis B (CHB) subjects with matched age, sex and HBV status. We identified a correlation of the ether phosphatidylcholine (PC) synthesis with hepatocarcinogenesis in patients with HBV-related liver diseases. The diagnostic models achieved an area under ROC curve (AUC) of 0.849 for discriminating HCC from CHB and an AUC of 0.829 for discriminating HCC from LC.

CONCLUSIONS: We illustrate the role of ether PC in hepatocarcinogenesis upon HBV infection and provide novel effective markers for early detection of HCC in a cohort with HBV infection.