Serum lipidomics as diagnostic potential for metabolic-associated hepatocellular carcinoma

Abstract Metabolic-associated fatty liver disease (MAFLD) is affecting more people globally. Indeed, MAFLD is associated with a spectrum of metabolic dysfunctions that can progress to hepatocellular carcinoma (MAFLD-HCC). This development can occur in a non-cirrhotic liver and thus, often lack clinical surveillance. Using comprehensive ultra-high-performance liquid chromatography mass-spectrometry, we investigated 1,295 metabolites in serum from 249 Caucasian liver patients. Here we show that MAFLD-HCC is characterized by a complete rearrangement of the serum lipidome which distinguishes MAFLD-HCC from other HCC patients. We used machine learning to build a diagnostic model for MAFLD to MAFLD-HCC. We quantified predictive metabolites and adjusted the model into the MAFLD-HCC Diagnostic Score, presenting superior diagnostic potential compared to current alpha-fetoprotein (AFP). The metabolic landscape shows a progressive depletion in unsaturated fatty acids and acylcarnitines during transformation. Therefore, serum metabolomics may provide valuable insight to monitor patients at risk, including morbidly obese, diabetics, and MAFLD patients.