Antwi Samuel O, Jnr Siaw Ampem Darko, Armasu Sebastian M, Frank Jacob A, Yan Irene K, Ahmed Fowsiyo Y, Izquierdo-Sanchez Laura, Boix Loreto, Rojas Angela, Banales Jesus M, Reig Maria, Stål Per, Gómez Manuel Romero, Wangensteen Kirk J, Singal Amit G, Roberts Lewis R, Patel Tushar
Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida.
Division of Gastroenterology and Hepatology, Department of Internal Medicine, Mayo Clinic, Jacksonville, Florida.
Gastro Hep Adv. 2025 Jan 23;4(5):100621. doi: 10.1016/j.gastha.2025.100621. eCollection 2025.
Metabolic liver disease is the fastest-rising cause of hepatocellular carcinoma (HCC), but the underlying molecular processes that drive HCC development in the setting of metabolic perturbations are unclear. We investigated the role of aberrant DNA methylation in metabolic HCC development in a multicenter international study.
We used a case-control design, frequency-matched on age, sex, and study site. Genome-wide profiling of peripheral blood leukocyte DNA was performed using the 850k EPIC array. The study sample was split 80% and 20% for training and validation. Cell type proportions were estimated from the methylation data. Differential methylation analysis was performed adjusting for cell type, generating area under the receiver-operating characteristic curves (AUC-ROC).
We enrolled 272 metabolic HCC patients and 316 control patients with metabolic liver disease from 6 sites. Fifty-five differentially methylated CpGs were identified; 33 hypermethylated and 22 hypomethylated in cases vs controls. The panel of 55 CpGs discriminated between the cases and controls with AUC = 0.79 (95% confidence interval [CI] = 0.71-0.87), sensitivity = 0.77 (95% CI = 0.66-0.89), and specificity = 0.74 (95% CI = 0.64-0.85). The 55-CpG classifier panel performed better than a base model that comprised age, sex, race, and diabetes mellitus (AUC = 0.65, 95% CI = 0.55-0.75; sensitivity = 0.62, 95% CI = 0.49-0.75; and specificity = 0.64, 95% CI = 0.52-0.75). A multifactorial model that combined the 55 CpGs with age, sex, race, and diabetes yielded AUC = 0.78 (95% CI = 0.70-0.86), sensitivity = 0.81 (95% CI = 0.71-0.92), and specificity = 0.67 (95% CI = 0.55-0.78).
A panel of 55 blood leukocyte DNA methylation markers differentiates patients with metabolic HCC from control patients with benign metabolic liver disease, with a slightly higher sensitivity when combined with demographic and clinical information.
代谢性肝病是肝细胞癌(HCC)发病率上升最快的病因,但在代谢紊乱情况下驱动HCC发生发展的潜在分子机制尚不清楚。我们在一项多中心国际研究中调查了异常DNA甲基化在代谢性HCC发生发展中的作用。
我们采用病例对照设计,根据年龄、性别和研究地点进行频率匹配。使用850k EPIC芯片对外周血白细胞DNA进行全基因组分析。研究样本按80%和20%分为训练集和验证集。从甲基化数据估计细胞类型比例。进行差异甲基化分析时对细胞类型进行了校正,生成受试者操作特征曲线下面积(AUC-ROC)。
我们从6个地点招募了272例代谢性HCC患者和316例代谢性肝病对照患者。共鉴定出55个差异甲基化的CpG位点;病例组与对照组相比,33个高甲基化,22个低甲基化。这55个CpG位点组成的panel区分病例组和对照组的AUC = 0.79(95%置信区间[CI]=0.71-0.87),敏感性=0.77(95%CI=0.66-0.89),特异性=0.74(95%CI=0.64-0.85)。该由55个CpG组成的分类器panel比包含年龄、性别、种族和糖尿病的基础模型表现更好(AUC = 0.65,95%CI = 0.55-0.75;敏感性=0.62,95%CI = 0.49- .75;特异性=0.64,95%CI = 0.52-0.75)。一个将55个CpG位点与年龄、性别、种族和糖尿病相结合的多因素模型的AUC = 0.78(95%CI = 0.70-0.86),敏感性=0.81(95%CI = 0.71-0.92),特异性=0.67(95%CI = 0.55-0.78)。
一组55个血液白细胞DNA甲基化标志物可区分代谢性HCC患者与良性代谢性肝病对照患者,与人口统计学和临床信息相结合时敏感性略高。