5-hydroxymethylcytosine signature in plasma extracellular vesicle DNA as a diagnostic molecular biomarker for precancerous lesions of gastric cancer

Abstract

Aim: Precancerous lesions of gastric cancer (PLGC) represent a critical window for prevention. Developing non-invasive tools that can reliably detect these lesions is therefore a prerequisite for lowering gastric-cancer incidence. Recent work has highlighted the diagnostic promise of plasma extracellular vesicle DNAs (evDNAs) and the 5-hydroxymethylcytosine (5hmC)-Seal epigenomic platform. Here we profiled genome-wide 5hmC patterns in circulating evDNA to discover biomarkers and build a classification model.

Methods: We performed whole-genome 5hmC-Seal on plasma evDNAs from 67 PLGC patients and 67 healthy individuals. By identifying trend-expressed differentially hydroxymethylated regions (DhMRs), we used machine learning algorithms to screen for diagnostic biomarkers of PLGC and established a corresponding diagnostic model.

Results: We ultimately constructed a diagnostic model comprising nine DhMRs. In the test set, the area under the curve (AUC) value was 0.963, with an accuracy of 0.886, sensitivity of 95.45%, and specificity of 81.82%. These results indicate that DhMRs in evDNA can serve as diagnostic biomarkers for PLGC, with good diagnostic capability and reliability. Correlation analysis showed a strong association between the DhMRs in the diagnostic model and clinical pathological indicators of PLGC.

Conclusion: We developed a non-invasive diagnostic model for PLGC by profiling 5hmC in plasma evDNA. In both accuracy and inter-batch robustness, it surpasses all previously reported assays. Our findings establish plasma-evDNA 5hmC profiling as a reliable, minimally invasive strategy for the early detection and precise diagnosis of gastric precancerous lesions, and provide a new translational and clinical framework for future work.

Keywords

Precancerous lesions of gastric cancer / 5-hydroxymethylcytosine / extracellular vesicle DNAs / molecular biomarker

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