Sanchez Jessica I, Fontillas Antoine C, Kwan Suet-Ying, Sanchez Caren I, Calderone Tiffany L, Lee Jana L, Elsaiey Ahmed, Cleere Darrel W, Wei Peng, Vierling John M, Victor David W, Beretta Laura
Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Margaret M. and Albert B. Alkek Department of Medicine, Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, TX, USA.
JHEP Rep. 2024 May 15;6(8):101119. doi: 10.1016/j.jhepr.2024.101119. eCollection 2024 Aug.
BACKGROUND & AIMS: The effectiveness of surveillance for hepatocellular carcinoma (HCC) in patients with cirrhosis is limited, due to inadequate risk stratification and suboptimal performance of current screening modalities.
We developed a multicenter prospective cohort of patients with cirrhosis 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.
We identified 150 metabolites with significant abundance changes in samples collected prior to HCC (Cases) compared to samples from patients who did not 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, and and single nucleotide polymorphisms, 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 six 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, 95% CI 0.83-0.93). Finally, 23 metabolites distinguished Cases with LI-RADS-3 lesions from Controls with LI-RADS-3 lesions, with reduced abundance of acyl-cholines and glycerophosphorylcholine-related lysophospholipids in Cases.
This study identified N-acetylglycine, amino acids, bile acids and choline-derived metabolites as biomarkers of HCC risk, and microbiota-derived metabolites as contributors to HCC development.
The 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, particularly blood biomarkers. Longitudinal collection of paired blood samples and MRI images from patients with cirrhosis 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 patients with cirrhosis undergoing HCC surveillance, a critical step to increasing curative treatment opportunities and reducing mortality.
由于风险分层不足以及当前筛查方式的性能欠佳，对肝硬化患者进行肝细胞癌（HCC）监测的有效性有限。
我们建立了一个对肝硬化患者进行MRI监测的多中心前瞻性队列，并对203例患者的612份纵向血清样本应用了全局非靶向代谢组学。其中，37例在随访期间发生了HCC。
我们在HCC发生前采集的样本（病例组）与未发生HCC的患者样本（对照组）中，鉴定出150种丰度有显著变化的代谢物。牛磺结合型胆汁酸和γ-谷氨酰氨基酸增加，而酰基胆碱和脱氧胆酸盐衍生物减少。包括丝氨酸和丙氨酸在内的7种氨基酸与HCC风险密切相关，而N-乙酰甘氨酸和甘油磷酸胆碱则具有很强的保护作用。利用这150种代谢物、年龄、性别和单核苷酸多态性进行机器学习，确定了15个具有最佳性能的变量。其中，N-乙酰甘氨酸在区分病例组和对照组时的曲线下面积（AUC）最高。当将病例组限制为HCC发生前1年内采集的样本（病例组-12M）时，鉴定出了包括微生物群衍生代谢物在内的其他代谢物。机器学习确定的前六个变量（甲胎蛋白、6-溴色氨酸、N-乙酰甘氨酸、水杨尿酸葡萄糖醛酸、硫酸睾酮和年龄）的组合在区分病例组-12M和对照组方面表现良好（AUC 0.88，95%CI 0.83-0.93）。最后，23种代谢物区分了具有LI-RADS-3类病变的病例组和具有LI-RADS-3类病变的对照组，病例组中酰基胆碱和甘油磷酸胆碱相关溶血磷脂的丰度降低。
本研究确定N-乙酰甘氨酸、氨基酸、胆汁酸和胆碱衍生代谢物为HCC风险的生物标志物，微生物群衍生代谢物为HCC发生的促成因素。
对肝硬化患者进行肝细胞癌（HCC）监测的有效性有限。迫切需要改进风险分层和新的筛查方式，特别是血液生物标志物。对肝硬化患者纵向采集配对血液样本和MRI图像，对于评估在观察到病变以获得HCC诊断的期间，血液和影像标志物如何早期呈阳性特别有价值。我们建立了一个对肝硬化患者进行对比增强MRI监测的多中心前瞻性队列，对203例患者的612份血清样本应用了非靶向代谢组学，并鉴定出与HCC发生风险相关的代谢物。这些生物标志物可能显著改善对接受HCC监测的肝硬化患者的早期HCC检测，这是增加治愈性治疗机会和降低死亡率的关键步骤。
