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Updated: Jul 8, 2026
MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier (MSC) for Lung Cancer Screening
Published on: October 26, 2017
Liquid biopsy-based multi-omics approach for early detection of hepatocellular carcinoma (ASCEND-Hep): a multiphase
Chunming Wang1, Lei Cai1, Yongguang Yang2
1General Surgery Center, Department of Hepatobiliary Surgery II, Guangdong Provincial Research Center for Artificial Organ and Tissue Engineering, Guangzhou Clinical Research and Transformation Center for Artificial Liver, Institute of Regenerative Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Background:
Hepatocellular carcinoma (HCC), the third leading cause of cancer deaths, urgently requires innovative early detection strategies. This prospective study aimed to evaluate the feasibility of cell-free DNA (cfDNA) methylation, mutation, and/or alpha-fetoprotein (AFP) for HCC early detection model development.
Methods:
Peripheral blood samples were prospectively collected from 635 participants (288 HCC, 347 non-HCC), who were randomly assigned (6:4) to the training and validation sets to develop and validate a multi-omics early detection (MOED) model. The model was externally validated on 797 subjects (160 HCC, 637 non-HCC) and further blindly tested on 452 community-recruited high-risk individuals.
Results:
The methylation-based model showed superior performance over AFP and mutation-based models in the training set. In the validation set, the MOED model, integrating methylation with AFP levels ≥ 400 ng/mL, slightly increased sensitivity from 87.1% (95% CI: 79.6%-92.6%) to 88.8% (95% CI: 81.6%-93.9%) at 95.7% specificity (95% CI: 91.0%-98.4%), whereas adding mutation data did not improve the performance. In the independent validation set, the locked MOED exhibited 91.9% (95% CI: 86.5%-95.6%) overall sensitivity, 83.3% (95% CI: 51.6%-97.9%) stage 0 sensitivity, and 98.4% overall specificity (95% CI: 97.1%-99.2%). In high-risk individuals, the model demonstrated 92.9% (95% CI: 64.2%-99.6%) sensitivity, 90.6% (95% CI: 87.4%-93.1%) specificity, 24.1% (95% CI: 13.9%-37.9%) positive predictive value (PPV) and 99.7% (95% CI: 98.4%-100.0%) negative predictive value (NPV).
Conclusions:
The MOED model, integrating cfDNA methylation and AFP, is highly effective for HCC detection and promising for screening in high-risk populations.
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