ArticleBMC medicine2024
Early detection of uterine corpus endometrial carcinoma utilizing plasma cfDNA fragmentomics.
Article in BMC medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Integrative cfDNA profiling from low-pass whole-genome sequencing enables tissue-of-origin prediction in cancer.Molecular biomedicine · 2026Article
- Liquid Biopsy Biomarkers in Endometrial Cancer: Current Landscape and Future Perspectives.Biomolecules · 2026Review
- Extracellular vesicle and particle biomarkers in cancer: a machine learning blueprint for liquid biopsy.Journal of nanobiotechnology · 2026Review
- Early detection of gastric cancer: a novel circulating microbiome DNA based liquid biopsy assay.NPJ precision oncology · 2026Article
- Systematic Multiplatform Discovery of Methylation Markers Enables Non-Invasive Early Detection of Endometrial Cancer.Cancer science · 2026Article
- ANKRD62 Modulates NF-κB Signaling to Promote Proliferation and Migration in UCEC.Cancer medicine · 2026Article
- Exploring the role of IGHV3-30 in the immune microenvironment and prognosis of uterine endometrial cancer.Discover oncology · 2026Article
- Review
- Exploring the Functional and Prognostic Significance of miR-217 in Uterine Corpus Endometrial Carcinoma.Current medicinal chemistry · 2026Article
- Liquid biopsy, multi-cancer early detection, and artificial intelligence: new frontiers in cancer screening from a technological and immunological perspective.Frontiers in immunology · 2026Review
- From multi-omics to deep learning: advances in cfDNA-based liquid biopsy for multi-cancer screening.Biomarker research · 2025Review
- Article
- Evaluation of Thrombomodulin, hsa-miR-18a-5p, and hsa-miR-18b-5p as Potential Prognostic Biomarkers in Uterine Corpus Endometrial Carcinoma.International journal of molecular sciences · 2025Article
- Review
- Recent therapeutic advances in gynecologic oncology: evolving roles of immunotherapy, antibody-drug conjugates, and clinical trial innovations.Frontiers in oncology · 2025Review
- Hybrid ResNet-ViT Framework for Endometrial Lesion Analysis: A Comparative Study of MRI and CT in Endometrial Cancer Classification.International journal of women's health · 2025Article
- Advancements in Minimally Invasive Techniques and Biomarkers for the Early Detection of Endometrial Cancer: A Comprehensive Review of Novel Diagnostic Approaches and Clinical Implications.Journal of clinical medicine · 2024Review
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignancy with a favorable prognosis if detected early. However, there is a lack of accurate and reliable early detection tests for UCEC. This study aims to develop a precise and non-invasive diagnostic method for UCEC using circulating cell-free DNA (cfDNA) fragmentomics.
methodsPeripheral blood samples were collected from all participants, and cfDNA was extracted for analysis. Low-coverage whole-genome sequencing was performed to obtain cfDNA fragmentomics data. A robust machine learning model was developed using these features to differentiate between UCEC and healthy conditions.
resultsThe cfDNA fragmentomics-based model showed high predictive power for UCEC detection in training (n = 133; AUC 0.991) and validation cohorts (n = 89; AUC 0.994). The model manifested a specificity of 95.5% and a sensitivity of 98.5% in the training cohort, and a specificity of 95.5% and a sensitivity of 97.8% in the validation cohort. Physiological variables and preanalytical procedures had no significant impact on the classifier's outcomes. In terms of clinical benefit, our model would identify 99% of Chinese UCEC patients at stage I, compared to 21% under standard care, potentially raising the 5-year survival rate from 84 to 95%.
conclusionThis study presents a novel approach for the early detection of UCEC using cfDNA fragmentomics and machine learning showing promising sensitivity and specificity. Using this model in clinical practice could significantly improve UCEC management and control, enabling early intervention and better patient outcomes. Further optimization and validation of this approach are warranted to establish its clinical utility.
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