Table4_Pan-Cancer DNA Methylation Analysis and Tumor Origin Identification of Carcinoma of Unknown Primary Site Based on Multi-Omics.XLSX

The metastatic cancer of unknown primary (CUP) sites remains a leading cause of cancer death with few therapeutic options. The aberrant DNA methylation (DNAm) is the most important risk factor for cancer, which has certain tissue specificity. However, how DNAm alterations in tumors differ among the regulatory network of multi-omics remains largely unexplored. Therefore, there is room for improvement in our accuracy in the prediction of tumor origin sites and a need for better understanding of the underlying mechanisms. In our study, an integrative analysis based on multi-omics data and molecular regulatory network uncovered genome-wide methylation mechanism and identified 23 epi-driver genes. Apart from the promoter region, we also found that the aberrant methylation within the gene body or intergenic region was significantly associated with gene expression. Significant enrichment analysis of the epi-driver genes indicated that these genes were highly related to cellular mechanisms of tumorigenesis, including T-cell differentiation, cell proliferation, and signal transduction. Based on the ensemble algorithm, six CpG sites located in five epi-driver genes were selected to construct a tissue-specific classifier with a better accuracy (>95%) using TCGA datasets. In the independent datasets and the metastatic cancer datasets from GEO, the accuracy of distinguishing tumor subtypes or original sites was more than 90%, showing better robustness and stability. In summary, the integration analysis of large-scale omics data revealed complex regulation of DNAm across various cancer types and identified the epi-driver genes participating in tumorigenesis. Based on the aberrant methylation status located in epi-driver genes, a classifier that provided the highest accuracy in tracing back to the primary sites of metastatic cancer was established. Our study provides a comprehensive and multi-omics view of DNAm-associated changes across cancer types and has potential for clinical application.未知原发灶转移性癌症（CUP）仍是癌症相关死亡的主要诱因之一，且可用治疗手段十分有限。异常DNA甲基化（DNAm）是癌症最重要的风险因素之一，具备一定的组织特异性。然而，肿瘤中的DNA甲基化改变在多组学调控网络中的差异机制仍未得到充分探索。因此，当前肿瘤原发灶预测的准确性仍有提升空间，且亟需进一步阐明其背后的潜在分子机制。本研究基于多组学数据与分子调控网络开展整合分析，揭示了全基因组范围的甲基化调控机制，并鉴定出23个表观驱动基因（epi-driver genes）。除启动子区域外，本研究还发现基因本体区域或基因间区域的异常甲基化，与基因表达水平存在显著关联。对表观驱动基因的富集分析显示，这些基因与肿瘤发生的核心细胞机制高度相关，包括T细胞分化、细胞增殖以及信号转导通路。基于集成算法（ensemble algorithm），研究人员从5个表观驱动基因中筛选出6个CpG位点（CpG sites），利用癌症基因组图谱（TCGA）数据集构建了准确率超过95%的组织特异性分类器。在独立验证数据集以及来自基因表达综合数据库（GEO）的转移性癌症数据集中，该分类器区分肿瘤亚型或原发灶的准确率均高于90%，展现出优异的稳健性与稳定性。综上，本研究通过大规模多组学数据的整合分析，揭示了不同癌症类型中DNA甲基化的复杂调控模式，并鉴定出参与肿瘤发生的表观驱动基因。基于表观驱动基因中的异常甲基化状态，本研究构建了一款可精准追溯转移性癌症原发灶的分类器，其预测准确率达到当前最优水平。本研究为跨癌症类型的DNA甲基化相关分子变化提供了全面的多组学视角，具备潜在的临床应用价值。