bims-dinmec 2025-12-14 papers

Issue of 2025–12–14
two papers selected by
Lorena Ancona, Humanitas Research

Sci Rep. 2025 Dec 11.

Plasma-based digital PCR assay for early detection of gastric cancer using multiple methylation biomarkers.

Yun Young Lee, Joon An, Jinil Han, Youngho Moon, Sang-Il Lee.

Endoscopy is the gold standard for diagnosing gastric cancer (GC), but its invasiveness limits widespread participation and has not substantially reduced GC-related mortality. This study developed and validated a blood-based digital PCR assay for early GC detection using DNA methylation biomarkers. Genome-wide methylation profiles from over 10,000 samples were screened, and two candidates were validated in GC cell lines, tumors, matched non-cancerous tissues, and plasma. Plasma from 60 GC patients, including 38 with stage I disease, and 40 healthy controls was analyzed with a digital PCR assay targeting the selected biomarkers, using ACTB as a reference. GHR and GLRB methylation were identified as novel GC biomarkers, showing consistent hypermethylation in GC cell lines and tumor tissues. In plasma, the two-marker assay achieved 83.3% (95% CI 71.5%-91.7%) sensitivity and 90% (95% CI 76.3%-97.2%) specificity, clearly outperforming carcinoembryonic antigen (CEA) testing (10.0%; 95% CI 3.8%-20.5%). Incorporation of GATM methylation as a third marker increased sensitivity to 86.7% (95% CI 75.4%-94.1%) overall and 81.6% (95% CI 65.7%-92.3%) for stage I disease, while maintaining 90.0% specificity. This methylation-based digital PCR assay enabled accurate, noninvasive detection of GC, particularly at early stages, and may facilitate timely diagnosis and curative treatment.

Keywords: Biomarkers; Circulating tumor DNA; DNA methylation; Digital PCR; Early diagnosis; Gastric cancer

DOI: https://doi.org/10.1038/s41598-025-31314-5

J Adv Res. 2025 Dec 09. pii: S2090-1232(25)00995-6. [Epub ahead of print]

Noninvasive detection and differentiation of gastric malignancy using cell-free DNA biomarkers.

INTRODUCTION: Gastric cancer remains a major global health burden, with high mortality driven by late-stage diagnoses that limit treatment options and reduce survival. Current diagnostic methods such as endoscopy and biopsy are invasive, resource-intensive, and impractical for large-scale early detection.
OBJECTIVES: This study aimed to develop and validate an ensemble machine learning model integrating four cell-free DNA (cfDNA) fragmentomic feature classes derived from 5 × whole genome sequencing (WGS) data to non-invasively differentiate malignant gastric cancer from benign gastric lesions in high-risk or symptomatic patients.
METHODS: A total of 681 plasma samples were prospectively collected, comprising 329 from patients with gastric cancer or high-grade intraepithelial neoplasia (HGIN) and 352 from individuals with benign gastric conditions. The dataset was divided into a training cohort (n = 333) and a temporally independent validation cohort (n = 348). An external validation cohort of 305 participants was also included.
RESULTS: The ensemble model achieved an AUROC of 0.920 in cross-validation testing on the training cohort, 0.912 in the independent validation cohort, and 0.896 (95% CI 0.860-0.932) in the external cohort. At a pre-specified prediction threshold of 0.402, the model demonstrated 93.3% sensitivity and 71.9% specificity in the validation cohort, yielding a PPV of 71.3% and an NPV of 93.5%. In the external cohort, sensitivity and specificity were 91.7% and 69.1%, respectively (PPV 75.7%, NPV 88.8%). Model scores correlated with clinical stage, tumor grade, and histopathological subtype. Approximately 71% of non-cancer patients could have been spared unnecessary endoscopy.
CONCLUSIONS: The cfDNA fragmentomics-based ensemble model enables accurate, non-invasive differentiation between gastric cancer and benign gastric lesions in high-risk or symptomatic patients. This approach demonstrates strong potential as a pre-endoscopy triage tool, supporting earlier detection and more efficient use of diagnostic resources.

Keywords: Early detection; Fragmentomics; Gastric cancer; Machine learning; Stomach-related complications

DOI: https://doi.org/10.1016/j.jare.2025.12.005