ArticleGut microbes2026
A machine-learning informed circulating microbial DNA signature for early diagnosis of esophageal adenocarcinoma.
Article in Gut microbes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Review
- Machine learning applications in the detection and treatment of esophageal cancer.Discover oncology · 2026Review
- A machine learning-based online prediction model for recurrence risk in patients withFrontiers in cellular and infection microbiology · 2026Article
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Authors and funding
14 authors.
Funding
Abstract
Esophageal adenocarcinoma (EAC) has seen a dramatic rise in incidence in developed countries over the past three decades. Early detection of its precursors-gastroesophageal reflux disease (GERD), Barrett's esophagus (BE), and high-grade dysplasia (HGD) is critical for cancer prevention. This study presents the development and validation of a novel liquid biopsy assay based on circulating microbial DNA (cmDNA) for the early detection of EAC and HGD. Using metagenomic sequencing, we identified significant differences in microbial diversity and composition between EAC and HGD patients, as well as between BE and GERD patients. A total of 46 microbial candidates in tissue and 419 in serum were upregulated in EAC & HGD, with 11 consistently elevated in both sample types. Following qRT-PCR validation and LASSO regression, a 6-marker cmDNA panel was selected. This signature was incorporated into a diagnostic model trained with the XGBoost algorithm, achieving an AUC of 0.93 in the training cohort (52 HGD & EAC cases vs. 54 BE & GERD controls). Importantly, the model demonstrated robust performance in an independent testing cohort (23 HGD & EAC cases vs. 22 BE & GERD controls), yielding AUCs of 0.91 for EAC and 0.88 for HGD. These findings highlight the diagnostic potential of cmDNA-based profiling and support its utility as a minimally invasive, accurate, and generalizable tool for early detection of esophageal adenocarcinoma.
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Registered trials
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