Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value

DNA甲基化（DNA methylation）状态与多种疾病密切相关，且通常较基因表达更为稳定，因此异常DNA甲基化可成为肿瘤诊断、治疗及预后评估的重要生物标志物。然而，针对泛癌诊断与预后的DNA甲基化变化特征尚鲜有探索。本研究借助机器学习手段，系统分析了多种TCGA（The Cancer Genome Atlas，癌症基因组图谱）癌症的全基因组DNA甲基化模式。研究鉴定出7个CpG位点，可有效区分TCGA队列中12种主要癌症的肿瘤样本与癌旁正常组织样本（共1216例样本，曲线下面积AUC, Area Under Curve>0.99）。上述7个潜在诊断生物标志物在另外9种TCGA癌症队列及4个独立数据集（AUC>0.92）中得到了进一步验证。7个CpG位点中有3个与细胞分裂、DNA复制及细胞周期进程密切相关。本研究还鉴定出12个可有效区分26种不同癌症类型的CpG位点（共7605例样本），该结果在独立数据集及2例转移性异质性肿瘤中均得到重复验证（微平均AUC>0.89）。此外，本研究通过生存分析鉴定出一系列可显著预测7种不同癌症患者预后的潜在特征（p值