Wilechansky Robert M, Challa Prasanna K, Han Xijing, Hua Xinwei, Manning Alisa K, Corey Kathleen E, Chung Raymond T, Zheng Wei, Chan Andrew T, Simon Tracey G
Division of Gastroenterology and Hepatology, Oregon Health & Science University, Portland, Oregon.
Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts.
Cancer Prev Res (Phila). 2025 Apr 1;18(4):179-188. doi: 10.1158/1940-6207.CAPR-24-0440.
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.
尽管肝细胞癌(HCC)在易感人群中的发病率不断上升,但缺乏准确的早期检测工具。我们旨在研究在不同人群中,诊断前血浆代谢物与新发HCC之间的关联。在南方社区队列研究中的一项前瞻性巢式病例对照研究中,我们对150例新发HCC病例(诊断中位时间为7.9年)和100例肝硬化对照进行了诊断前液相色谱/质谱代谢组学分析。逻辑回归评估代谢物与HCC风险的关联。代谢物集富集分析确定了富集的通路,并使用随机森林分类器构建风险分类模型。候选代谢物在英国生物银行中进行了验证(12例新发HCC病例和24例肝硬化对照)。在逻辑回归分析中,七种代谢物与新发HCC相关(MeffP < 0.0004),包括N-乙酰甲硫氨酸(OR = 0.46;95%置信区间,0.31 - 0.66)。HCC中多种通路被富集,包括组氨酸和辅酶A代谢(FDR P < 0.001)。随机森林分类器确定了10种代谢物纳入HCC风险分类模型,与仅使用临床协变量相比,该模型改善了HCC风险分类(协变量的AUC = 0.66,10种代谢物加协变量的AUC = 0.88;P < 0.0001)。通过核磁共振波谱评估,英国生物银行的结果一致(协变量的AUC = 0.72,四种类似代谢物加协变量的AUC = 0.88;P = 0.04)。诊断前代谢物,主要是氨基酸和鞘脂衍生物,与HCC风险相关,并且在临床协变量之外改善了HCC风险分类。这些在诊断前数年即可检测到的代谢物谱,如果在更大规模研究中得到验证,可作为HCC检测和风险分层的早期生物标志物。预防相关性:我们的研究结果支持开展更大规模的前瞻性研究,以探讨诊断前血浆代谢组学在多种肝病病因的不同患者中对HCC预防性管理的作用。这种方法可以通过在诊断前数年识别代谢变化来改善HCC护理,潜在地加强筛查和早期检测实践。