Integrative Analysis Identifies Potential DNA Methylation Biomarkers for Pan-Cancer Diagnosis and Prognosis

Abstract

Background: DNA methylation status is closely associated with diverse diseases, and could be more stable than gene expression, thus 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. Methods: Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. Findings: We identified seven CpG sites that could effectively separate tumor samples from adjacent normal tissue samples from 12 main cancers (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 are 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 repeated in independent datasets of 6 cancers 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 < 1e-4). Interpretation: DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as different types of cancers. The results demonstrate that our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential tissue-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers. Funding: This work was supported by the National High Technology Research and Development Program of China (2015AA020108), National Key Research and Development Program of China (2016YFC0902100), National Science Foundation of China (31671377, 31771460, 91629103) and Shanghai 111 Project (B14019). Declaration of Interest: The authors declare no potential conflicts of interest. Ethical Approval: Not applicable