Samuel O. Antwi , Ampem Darko Jnr. Siaw , Sebastian M. Armasu , Jacob A. Frank , Irene K. Yan , Fowsiyo Y. Ahmed , Laura Izquierdo-Sanchez , Loreto Boix , Angela Rojas , Jesus M. Banales , Maria Reig , Per Stål , Manuel Romero Gómez , Kirk J. Wangensteen , Amit G. Singal , Lewis R. Roberts , Tushar Patel
{"title":"与代谢性肝癌相关的全基因组DNA甲基化标记","authors":"Samuel O. Antwi , Ampem Darko Jnr. Siaw , Sebastian M. Armasu , Jacob A. Frank , Irene K. Yan , Fowsiyo Y. Ahmed , Laura Izquierdo-Sanchez , Loreto Boix , Angela Rojas , Jesus M. Banales , Maria Reig , Per Stål , Manuel Romero Gómez , Kirk J. Wangensteen , Amit G. Singal , Lewis R. Roberts , Tushar Patel","doi":"10.1016/j.gastha.2025.100621","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and Aims</h3><div>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.</div></div><div><h3>Methods</h3><div>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).</div></div><div><h3>Results</h3><div>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).</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":73130,"journal":{"name":"Gastro hep advances","volume":"4 5","pages":"Article 100621"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Genome-Wide DNA Methylation Markers Associated With Metabolic Liver Cancer\",\"authors\":\"Samuel O. Antwi , Ampem Darko Jnr. Siaw , Sebastian M. Armasu , Jacob A. Frank , Irene K. Yan , Fowsiyo Y. Ahmed , Laura Izquierdo-Sanchez , Loreto Boix , Angela Rojas , Jesus M. Banales , Maria Reig , Per Stål , Manuel Romero Gómez , Kirk J. Wangensteen , Amit G. Singal , Lewis R. Roberts , Tushar Patel\",\"doi\":\"10.1016/j.gastha.2025.100621\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background and Aims</h3><div>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.</div></div><div><h3>Methods</h3><div>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).</div></div><div><h3>Results</h3><div>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).</div></div><div><h3>Conclusion</h3><div>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.</div></div>\",\"PeriodicalId\":73130,\"journal\":{\"name\":\"Gastro hep advances\",\"volume\":\"4 5\",\"pages\":\"Article 100621\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Gastro hep advances\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772572325000081\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Gastro hep advances","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772572325000081","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
背景和目的代谢性肝病是肝细胞癌(HCC)发病率上升最快的原因,但在代谢紊乱的背景下,驱动HCC发展的潜在分子过程尚不清楚。我们在一项多中心国际研究中研究了异常DNA甲基化在代谢性HCC发展中的作用。方法采用病例对照设计,频率匹配年龄、性别和研究地点。使用850k EPIC阵列对外周血白细胞DNA进行全基因组分析。研究样本分为80%和20%进行训练和验证。根据甲基化数据估计细胞类型比例。根据细胞类型进行差异甲基化分析,在受体工作特征曲线(AUC-ROC)下产生区域。结果我们从6个地点纳入272例代谢性HCC患者和316例代谢性肝病对照患者。鉴定出55个差异甲基化CpGs;与对照组相比,33例高甲基化,22例低甲基化。55个cpg组区分病例和对照组的AUC = 0.79(95%可信区间[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)。55-CpG分类器面板优于包含年龄、性别、种族和糖尿病的基础模型(AUC = 0.65, 95% CI = 0.55-0.75;灵敏度= 0.62,95% CI = 0.49-0.75;特异性= 0.64,95% CI = 0.52-0.75)。将55个CpGs与年龄、性别、种族和糖尿病相结合的多因子模型得出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)。结论55个血液白细胞DNA甲基化标志物可将代谢性HCC患者与良性代谢性肝病对照患者区分开来,结合人口统计学和临床信息,其敏感性略高。
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), 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.
Methods
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).
Results
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).
Conclusion
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.