aceGenome-Wide DNA methylation profiling of tissue-plasma pairs identifies Novel cfDNA biomarkers for the diagnosis of gastric cancer
Screening for gastric cancer (GC) is critical for improving patient survival, yet current clinical methods suffer from invasiveness and limited sensitivity. The aim of this study is to develop a machine learning model for the non-invasive diagnosis of GC using the whole-genome methylation profile of circulating free DNA (cfDNA) in plasma. Plasma cfDNA from 61 patients with GC and 29 healthy controls. First, whole‑genome bisulfite sequencing (WGBS) was performed on 29 paired GC and adjacent normal tissues to iden…
A random forest model using eight plasma cfDNA methylation markers distinguished gastric cancer patients from healthy controls with an AUC of 0.938 on an independent test set, offering a non-invasive screening route.
The cohort is small (61 patients with gastric cancer and 29 healthy controls), and specificity on the test set was 75.00%, so the model is described only as promising rather than validated for screening.
Evidence
- Peer-reviewedPLOS Genetics2026-10-15
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Truvace Impact Record TRV-2026-1966, v1: “Genome-Wide DNA methylation profiling of tissue-plasma pairs identifies Novel cfDNA biomarkers for the diagnosis of gastric cancer.” Truvace, 2026-10-20. /record/TRV-2026-1966 (accessed at citation time). sha256 ab7ccf27e3e251f2…
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