Cancer Prevention Research, pages OF1-OF10

Pre-Diagnostic Plasma Metabolites are Associated with Incident Hepatocellular Carcinoma: A Prospective Analysis

Robert Wilechansky 1, 2
Prasanna K Challa 3, 4, 5
Xijing Han 6, 7
Xinwei Hua 8, 9
Alisa Manning 3, 5, 10, 11, 12
Kathleen E Corey 3, 4, 10, 13
R. Chung 4, 10, 13, 14
Wei Zheng 7, 15
Andrew Chan 4, 5, 10, 16
Tracey G. Simon 4, 10, 13, 17
Show full list: 10 authors
2
 
1Division of Gastroenterology and Hepatology, Oregon Health & Science University, Portland, Oregon.
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4Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, Tennessee.
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5Department of Cardiology, Peking University Third Hospital, Beijing, China.
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7Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
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8Broad Metabolism Program, Broad Institute, Cambridge, Massachusetts.
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9Liver Center, Massachusetts General Hospital, Boston, Massachusetts.
Publication typeJournal Article
Publication date2025-02-28
scimago Q1
wos Q2
SJR1.239
CiteScore6.0
Impact factor2.9
ISSN19406207, 19406215
Abstract

Despite increasing incidence of hepatocellular carcinoma (HCC) in vulnerable populations, accurate early detection tools are lacking. We aimed to investigate the associations between prediagnostic plasma metabolites and incident HCC in a diverse population. In a prospective, nested case–control study within the Southern Community Cohort Study, we conducted prediagnostic LC/MS metabolomic profiling in 150 incident HCC cases (median time to diagnosis, 7.9 years) and 100 controls with cirrhosis. Logistic regression assessed metabolite associations with HCC risk. Metabolite set enrichment analysis identified enriched pathways, and a random forest classifier was used for risk classification models. Candidate metabolites were validated in the UK Biobank (N = 12 incident HCC cases and 24 cirrhosis controls). In logistic regression analysis, seven metabolites were associated with incident HCC (MeffP < 0.0004), including N-acetylmethionine (OR = 0.46; 95% confidence interval, 0.31–0.66). Multiple pathways were enriched in HCC, including histidine and CoA metabolism (FDR P < 0.001). The random forest classifier identified 10 metabolites for inclusion in HCC risk classification models, which improved HCC risk classification compared with clinical covariates alone (AUC = 0.66 for covariates vs. 0.88 for 10 metabolites plus covariates; P < 0.0001). Findings were consistent in the UK Biobank (AUC = 0.72 for covariates vs. 0.88 for four analogous metabolites plus covariates; P = 0.04), assessed via nuclear magnetic resonance spectroscopy. Prediagnostic metabolites, primarily amino acid and sphingolipid derivatives, are associated with HCC risk and improve HCC risk classification beyond clinical covariates. These metabolite profiles, detectable years before diagnosis, could serve as early biomarkers for HCC detection and risk stratification if validated in larger studies.

Prevention Relevance: Our findings support the need for larger prospective studies examining the role of prediagnostic plasma metabolomics for the preventive management of HCC in diverse patients across multiple etiologies of liver disease. This approach could improve HCC care by identifying metabolic changes years before diagnosis, potentially enhancing screening and early detection practices.

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