Early detection, clinicopathological subtyping, and prognosis prediction for endometrial cancer patients using fragmentomics liquid biopsy assay
This DECIPHER-UCEC-2 study (Detecting Early Cancer by Inspecting ctDNA Features) demonstrates that by integrating cfDNA fragmentomics with machine learning, our liquid biopsy assay shows significant promise for EC's early detection, subtyping, and prognosis, potentially paving the way for enhanced patient outcomes.
Authors
- Bingnan Zhang (ORCID: https://orcid.org/0000-0002-1821-6243)
- Xiaodan Zhu
- N. Li
- Xue Wu (ORCID: https://orcid.org/0000-0002-5567-1325)
- Misi He (ORCID: https://orcid.org/0000-0001-8469-7690)
- Huayan Bao
- Wanxiangfu Tang (ORCID: https://orcid.org/0000-0002-9045-2478)
- Yazhou He (ORCID: https://orcid.org/0000-0003-2358-0143)
- Dan Zhu (ORCID: https://orcid.org/0000-0002-5857-1899)
- H. Wang
- Qing Rao (ORCID: https://orcid.org/0000-0001-6721-1337)
- Hui Zhou (ORCID: https://orcid.org/0000-0002-6318-894X)
- Zhixiong Lin (ORCID: https://orcid.org/0000-0003-4898-7441)
- Pingping He
- Wen-Kai Weng
Institutions
- Sun Yat-sen University (CN)
- Sun Yat-sen Memorial Hospital (CN)
- Chongqing Cancer Hospital (CN)
- Genesee Community College (US)
Publication Details
- Journal
- ESMO Open
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1016/j.esmoop.2025.105770
- Citations
- 1
- Primary Topic
- Cancer Genomics and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.94