ArticleBMC biology2025
Multimodal analysis of cell-free DNA enhances differentiation of early-stage breast cancer from benign lesions and healthy individuals.
Article in BMC biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Review
- Article
- Artificial intelligence-driven multimodal fusion for precision diagnosis and personalized management of breast cancer.Oncology reviews · 2026Review
- Multimodal analysis of cell-free DNA enhances differentiation of early-stage breast cancer from benign lesions and healthy individuals.BMC biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
46 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBreast cancer (BC) remains the second leading cause of cancer-related mortality among women worldwide. Liquid biopsy based on circulating tumor DNA (ctDNA) offers a promising noninvasive approach for early detection; however, differentiating malignant tumors from benign abnormalities remains a significant challenge.
resultsHere, we developed a multimodal approach to analyze cfDNA methylation and fragmentomic patterns in 273 BC patients, 108 individuals with benign breast conditions, and 134 healthy controls. Genome-wide analyses revealed distinct cfDNA copy number alterations and cytosine-enriched cleavage sites in BC patients. Targeted sequencing further revealed unique methylation patterns, including hypermethylation in GPR126, KLF3, and TLR10 and hypomethylation in TOP1 and MAFB. Our machine-learning model achieved an AUC of 0.90, with 93.6% specificity and 62.1-66.3% sensitivity for stage I-II cancers. In symptomatic populations, sensitivities were 50.0%, 68.2%, and 64.7% for BI-RADS categories 3, 4, and 5, respectively, with 96.1% specificity.
conclusionsThese findings underscore the potential of cfDNA biomarkers to enhance BC detection and reduce the rate of unnecessary biopsies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.