Abstract
Background & AimsThe effectiveness of surveillance for hepatocellular carcinoma (HCC) in patients with cirrhosis is limited, due to inadequate risk stratification and suboptimal performance of the screening modalities. MethodsWe developed a multicenter prospective cohort of cirrhotic patients undergoing surveillance with MRI, and applied global untargeted metabolomics to 612 longitudinal serum samples from 203 patients. Among them, 37 developed HCC during follow-up. ResultsWe identified 150 metabolites with significant abundance changes in samples collected prior to HCC (Cases) compared to samples from patients who didn’t develop HCC (Controls). Tauro-conjugated bile acids and gamma-glutamyl amino acids were increased, while acyl-cholines and deoxycholate derivatives were decreased. Seven amino acids including serine and alanine, had strong associations with HCC risk, while strong protective effects were observed for N-acetylglycine and glycerophosphorylcholine. Machine learning using the 150 metabolites, age, gender, patatin-like phospholipase domain-containing protein 3 (PNPLA3) and transmembrane 6 superfamily member 2 (TMS6SF2) SNPs, identified 15 variables giving optimal performance. Among them, N-acetylglycine had the highest AUC in discriminating Cases and Controls. When restricting Cases to samples collected within 1 year prior to HCC (Cases-12M), additional metabolites including microbiota-derived metabolites were identified. The combination of the top 6 variables identified by machine learning (alpha-fetoprotein, 6-bromotryptophan, N-acetylglycine, salicyluric glucuronide, testosterone sulfate and age) had good performance in discriminating Cases-12M from Controls (AUC=0.88 [0.83-0.93]). Finally, 23 metabolites distinguished Cases with Liver Imaging Reporting and Data System (LI-RADS)-3 lesions from Controls with LI-RADS-3 lesions, with reduced abundance of acyl-cholines and glycerophosphorylcholine-related lysophospholipids in Cases-LR3. ConclusionsThis study identified N-acetylglycine, amino acids, bile acids and choline-derived metabolites as main biomarkers of HCC risk, and microbiota-derived metabolites as main contributors to HCC development. Impact and ImplicationsThe effectiveness of surveillance for hepatocellular carcinoma (HCC) in patients with cirrhosis is limited. There is an urgent need for improvement in risk stratification and new screening modalities, blood biomarkers in particular. Longitudinal collection of paired blood samples and MRI images from cirrhotic patients is particularly valuable in assessing how early blood and imaging markers become positive during the period when lesions are observed to obtain a diagnosis of HCC. We generated a multicenter prospective cohort of patients with cirrhosis under surveillance with contrast MRI, applied untargeted metabolomics on 612 serum samples from 203 patients and identified metabolites associated with risk of HCC development. Such biomarkers may significantly improve early stage HCC detection for cirrhotic patients undergoing HCC surveillance, a critical step to increasing curative treatment opportunities and reducing mortality.
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