Genome-wide DNA methylation markers associated with metabolic liver cancer. Genome-wide DNA methylation markers associated with metabolic liver cancer

Background and Aims: Metabolic liver disease is the fastest rising cause of hepatocellular carcinoma (HCC) worldwide, 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. Results: We enrolled 272 metabolic HCC patients and 316 control patients with metabolic liver disease from six sites. Fifty-five differentially methylated CpGs were identified; 33 hypermethylated and 22 hypomethylated in cases versus controls. The panel of 55 CpGs discriminated between cases and controls with AUC=0.79 (95%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). Conclusions: 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. Overall design: 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. Cell type proportions were estimated from the methylation data. The study samples were split 80% and 20% for training and validation. Differential methylation analysis was performed with adjustment for cell type, and we generated area under the receiver-operating curves (ROC-AUC). ***NOTE FROM SUBMITTER: The data cannot be shared for 107 subjects due to European Union (EU) law (derived from EU patients that are protected by General Data Protection Regulation).***

背景与目的：代谢性肝病是全球范围内肝细胞癌（hepatocellular carcinoma, HCC）增长最快的病因，但代谢紊乱背景下驱动HCC发生的潜在分子机制仍不明确。本项多中心国际研究旨在探究异常DNA甲基化（DNA methylation）在代谢相关性HCC发生发展中的作用。
结果：本研究共纳入来自6个研究中心的272例代谢相关性HCC患者与316例伴代谢性肝病的对照受试者。最终鉴定出55个差异甲基化CpG位点（CpG），其中病例组相较于对照组存在33个高甲基化位点、22个低甲基化位点。该55个CpG位点组成的标志物组合可有效区分病例组与对照组，受试者工作特征曲线下面积（Area Under the Curve, AUC）为0.79（95%CI：0.71~0.87），灵敏度为0.77（95%CI：0.66~0.89），特异度为0.74（95%CI：0.64~0.85）。相较于仅纳入年龄、性别、种族及糖尿病史的基础模型（AUC=0.65，95%CI：0.55~0.75，灵敏度0.62，95%CI：0.49~0.75，特异度0.64，95%CI：0.52~0.75），本研究的55-CpG分类器表现更优。将55个CpG位点与年龄、性别、种族及糖尿病史整合构建多因素模型后，AUC达0.78（95%CI：0.70~0.86），灵敏度为0.81（95%CI：0.71~0.92），特异度为0.67（95%CI：0.55~0.78）。
结论：本研究构建的55个外周血白细胞DNA甲基化标志物组合，可有效区分代谢相关性HCC患者与良性代谢性肝病对照受试者；联合人口统计学及临床信息后，模型灵敏度略有提升。
总体研究设计：本研究采用病例-对照研究设计，以年龄、性别及研究中心进行频率匹配。采用850k EPIC芯片（850k EPIC array）对受试者外周血白细胞DNA进行全基因组甲基化谱分析。从甲基化数据中估算细胞类型比例。研究样本按80%与20%的比例划分为训练集与验证集。差异甲基化分析校正了细胞类型混杂因素，并计算了受试者工作特征曲线下面积（ROC-AUC）。
***提交者备注：***由于欧盟相关法律限制（本研究样本包含受《通用数据保护条例》（General Data Protection Regulation, GDPR）保护的欧盟患者），107例受试者的数据无法公开共享。