{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13(1)"],"submitter":["Zhou X"],"pubmed_abstract":["Tumor-derived circulating cell-free DNA (cfDNA) provides critical clues for cancer early diagnosis, yet it often suffers from low sensitivity. Here, we present a cancer early diagnosis approach using tumor fractions deciphered from circulating cfDNA methylation signatures. We show that the estimated fractions of tumor-derived cfDNA from cancer patients increase significantly as cancer progresses in two independent datasets. Employing the predicted tumor fractions, we establish a Bayesian diagnostic model in which training samples are only derived from late-stage patients and healthy individuals. When validated on early-stage patients and healthy individuals, this model exhibits a sensitivity of 86.1% for cancer early detection and an average accuracy of 76.9% for tumor localization at a specificity of 94.7%. By highlighting the potential of tumor fractions on cancer early diagnosis, our approach can be further applied to cancer screening and tumor progression monitoring."],"journal":["Nature communications"],"pagination":["7694"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9744803"],"repository":["biostudies-literature"],"pubmed_title":["Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis."],"pmcid":["PMC9744803"],"pubmed_authors":["Tian J","Yang W","Liu M","Dong M","Liu Q","Cheng W","Zhou X","Cheng Z"],"additional_accession":[]},"is_claimable":false,"name":"Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis.","description":"Tumor-derived circulating cell-free DNA (cfDNA) provides critical clues for cancer early diagnosis, yet it often suffers from low sensitivity. Here, we present a cancer early diagnosis approach using tumor fractions deciphered from circulating cfDNA methylation signatures. We show that the estimated fractions of tumor-derived cfDNA from cancer patients increase significantly as cancer progresses in two independent datasets. Employing the predicted tumor fractions, we establish a Bayesian diagnostic model in which training samples are only derived from late-stage patients and healthy individuals. When validated on early-stage patients and healthy individuals, this model exhibits a sensitivity of 86.1% for cancer early detection and an average accuracy of 76.9% for tumor localization at a specificity of 94.7%. By highlighting the potential of tumor fractions on cancer early diagnosis, our approach can be further applied to cancer screening and tumor progression monitoring.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2025-04-25T20:07:33.764Z","creation":"2025-02-19T01:07:51.047Z"},"accession":"S-EPMC9744803","cross_references":{"pubmed":["36509772"],"doi":["10.1038/s41467-022-35320-3"]}}