Early detection of gastric cancer using DNA features in red blood cells, with multicenter validation.

Authors

Xingyun Yao, Haobo Sun, Cheng Fang, Qingqu Guo, Jun Wang, Guofeng Chen, Songzhao Zhang, Baozhong Li, Yanwu Jin, Ganshu Xia, Zhiqiang Liu, Fei Meng, Jie Jin, Xiaofei Gao, Yuehua Han

Published in

Med (New York, N.Y.). Pages 101325. Oct 13, 2026. Epub Oct 13, 2026.

Abstract

Early detection of gastric cancer (GC) is limited by invasive endoscopy and poor sensitivity of blood-based biomarkers. Red blood cells (RBCs), the most abundant cells in the circulation, are anucleate but can retain DNA remnants that have been associated with pathological stress. Here, we report a non-invasive strategy for GC detection based on genome-wide profiling of DNA remnants retained in RBCs (rbcDNA).
We performed shallow whole-genome sequencing of rbcDNA from 1-2 mL of peripheral blood of 865 individuals, including 434 treatment-naive GC patients and 431 non-GC controls with benign gastric conditions. In the discovery cohort, we identified 1,000 reproducible rbcDNA read-depth features distinguishing GC from non-cancer states. A machine learning classifier based on dimensionally reduced GC-associated rbcDNA features was trained in the discovery cohort (n = 435), with the cutoff fixed at 90% specificity, and evaluated in a test cohort (n = 109) and three independent multicenter validation cohorts (n = 321).
The classifier achieved the areas under the receiver operating characteristic curve (AUCs) of 84% in nested cross-validation of the discovery cohort and 95% in the test cohort. Across three hospital-based multicenter validation cohorts, performance remained consistent, with 83%-90% overall sensitivity and 82%-89% sensitivity for stage I GC. Additionally, the classifier distinguished early-stage GC from precancerous lesions and other tumors and detected ∼80% of GC cases negative for tumor markers.
These findings demonstrate the potential of rbcDNA as a non-invasive biomarker for early-stage GC detection.
This work was supported by the Key Research and Development Program of Zhejiang Province (2026C02A1176 and 2024C03170).

PMID:
42869026
Bibliographic data and abstract were imported from PubMed on 14 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.