Stone Timothy C, Ward Vanessa, Hogan Aine, Ho Kai Man Alexander, Wilson Ash, McBain Hazel, Duku Margaret, Wolfson Paul, Cheung Sharon, Rosenfeld Avi, Lovat Laurence B
Division of Surgery & Interventional Science, University College London, Charles Bell House, 43-45 Foley Street, London, W1W 7TY, UK.
Wellcome/EPSRC Centre for Interventional & Surgical Sciences (WEISS), University College London, Charles Bell House, 43-45 Foley Street, London, W1W 7TY, UK.
Epigenomics. 2024 Jan;16(2):109-125. doi: 10.2217/epi-2023-0248. Epub 2024 Jan 16.
Salivary epigenetic biomarkers may detect esophageal cancer. A total of 256 saliva samples from esophageal adenocarcinoma patients and matched volunteers were analyzed with Illumina EPIC methylation arrays. Three datasets were created, using 64% for discovery, 16% for testing and 20% for validation. Modules of gene-based methylation probes were created using weighted gene coexpression network analysis. Module significance to disease and gene importance to module were determined and a random forest classifier generated using best-scoring gene-related epigenetic probes. A cost-sensitive wrapper algorithm maximized cancer diagnosis. Using age, sex and seven probes, esophageal adenocarcinoma was detected with area under the curve of 0.72 in discovery, 0.73 in testing and 0.75 in validation datasets. Cancer sensitivity was 88% with specificity of 31%. We have demonstrated a potentially clinically viable classifier of esophageal cancer based on saliva methylation.
唾液表观遗传生物标志物可能检测出食管癌。对来自食管腺癌患者和匹配志愿者的256份唾液样本进行了Illumina EPIC甲基化阵列分析。创建了三个数据集，其中64%用于发现，16%用于测试，20%用于验证。使用加权基因共表达网络分析创建基于基因的甲基化探针模块。确定模块对疾病的显著性和基因对模块的重要性，并使用得分最高的基因相关表观遗传探针生成随机森林分类器。一种成本敏感的包装算法使癌症诊断最大化。利用年龄、性别和七个探针，在发现数据集中检测食管腺癌的曲线下面积为0.72，在测试数据集中为0.73，在验证数据集中为0.75。癌症敏感性为88%，特异性为31%。我们已经证明了一种基于唾液甲基化的潜在临床可行的食管癌分类器。
