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Background <p>Gastric cancer remains a global health challenge due to the difficulty of detecting it early in asymptomatic, high-risk populations. Current invasive diagnostic methods are impractical for widespread screening. Liquid biopsy using circulating tumor DNA (ctDNA) shows promise, but early detection is hindered by the low abundance and heterogeneity of ctDNA.</p> Methods <p>We developed a multimodal cfDNA assay integrating methylation, fragmentomic, and hotspot mutation profiling from a single blood draw to detect gastric cancer-specific molecular signatures. Using these signatures, a machine-learning model was trained on a discovery cohort of 110 nonmetastatic GC patients and 119 healthy controls, then validated on an independent cohort of 58 patients and 65 controls.</p> Results <p>The ensemble model achieved an AUC of 0.87 (95% CI: 0.80–0.93), with 70.7% sensitivity and 92.3% specificity for detecting nonmetastatic GC. Incorporating hotspot mutation profiling increased overall sensitivity to 75.9% without affecting specificity. Compared to a previous multi-cancer model, our ensemble model showed improved sensitivity across all stages, particularly for early-stage GC (72.7% vs. 36.4%).</p> Conclusions <p>This multimodal cfDNA assay provides a minimally invasive and effective strategy for early GC detection, making it a potential screening tool for high-risk populations.</p> Mini abstract <p>This study presents a novel multimodal cfDNA assay that combines methylation, fragmentomic, and hotspot mutation profiling, achieving 75.9% sensitivity and 92.3% specificity for early gastric cancer detection.</p>

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Multimodal analysis of cell-free DNA to improve early detection of gastric cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月23日

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  • Vo Duy Long,
  • Le Anh Khoa Huynh,
  • Dac Ho Vo,
  • Thi Hue Hanh Nguyen,
  • Thi Tuong Vi Van,
  • Giang Thi Huong Nguyen,
  • Thuy Nguyen Doan,
  • Viet Hai Nguyen,
  • Quang Dat Tran,
  • Quang Thong Dang,
  • Vu Tuan Anh Nguyen,
  • Le Minh Quoc Ho,
  • Thi Phuong Dung Ha,
  • Thi Ngoc Dung Dang,
  • Pham Thanh Nhan Nguyen,
  • Khac Tien Nguyen,
  • Van Chien Ho,
  • Thi Loc Le,
  • Thi Hong Nhung Nguyen,
  • Ngoc Hieu Tu,
  • Thanh Son Tran,
  • Thanh Xuan Jasmine,
  • Thi Loan Vo,
  • Thi Huong Thoang Nai,
  • Thuy Trang Tran,
  • My Hoang Truong,
  • Ngan Chau Tran,
  • Thanh Cong Nguyen,
  • Thi Truc Nguyen,
  • Bao Toan Le,
  • Van Phong Tang,
  • Thi Tu Nguyen,
  • Anh Tuan Nguyen,
  • Hoang Giang Vu,
  • Thi Van Phan,
  • Thi Ngoc Tien Nguyen,
  • Hoang Anh Cao,
  • Trong Hieu Nguyen,
  • Lan N. Tu,
  • Hoa Giang,
  • Minh Duy Phan,
  • Hoai-Nghia Nguyen,
  • Van Thien Chi Nguyen,
  • Le Son Tran

摘要

Background

Gastric cancer remains a global health challenge due to the difficulty of detecting it early in asymptomatic, high-risk populations. Current invasive diagnostic methods are impractical for widespread screening. Liquid biopsy using circulating tumor DNA (ctDNA) shows promise, but early detection is hindered by the low abundance and heterogeneity of ctDNA.

Methods

We developed a multimodal cfDNA assay integrating methylation, fragmentomic, and hotspot mutation profiling from a single blood draw to detect gastric cancer-specific molecular signatures. Using these signatures, a machine-learning model was trained on a discovery cohort of 110 nonmetastatic GC patients and 119 healthy controls, then validated on an independent cohort of 58 patients and 65 controls.

Results

The ensemble model achieved an AUC of 0.87 (95% CI: 0.80–0.93), with 70.7% sensitivity and 92.3% specificity for detecting nonmetastatic GC. Incorporating hotspot mutation profiling increased overall sensitivity to 75.9% without affecting specificity. Compared to a previous multi-cancer model, our ensemble model showed improved sensitivity across all stages, particularly for early-stage GC (72.7% vs. 36.4%).

Conclusions

This multimodal cfDNA assay provides a minimally invasive and effective strategy for early GC detection, making it a potential screening tool for high-risk populations.

Mini abstract

This study presents a novel multimodal cfDNA assay that combines methylation, fragmentomic, and hotspot mutation profiling, achieving 75.9% sensitivity and 92.3% specificity for early gastric cancer detection.