Predicting locus-specific DNA methylation levels in cancer and paracancer tissues.

ABSTRACT: Aim: To predict base-resolution DNA methylation in cancerous and paracancerous tissues. Material & methods: We collected six cancer DNA methylation datasets from The Cancer Genome Atlas and five cancer datasets from Gene Expression Omnibus and established machine learning models using paired cancerous and paracancerous tissues. Tenfold cross-validation and independent validation were performed to demonstrate the effectiveness of the proposed method. Results: The developed cross-tissue prediction models can substantially increase the accuracy at more than 68% of CpG sites and contribute to enhancing the statistical power of differential methylation analyses. An XGBoost model leveraging multiple correlating CpGs may elevate the prediction accuracy. Conclusion: Thi

SUBMITTER: Zhang S 

PROVIDER: S-EPMC11158003 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature