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

BACKGROUND: 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). METHODS: 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). FINDINGS: 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. CONCLUSIONS: These findings demonstrate the potential of rbcDNA as a non-invasive biomarker for early-stage GC detection. FUNDING: This work was supported by the Key Research and Development Program of Zhejiang Province (2026C02A1176 and 2024C03170).