AI Article Synopsis
- * Researchers identified unique methylation markers from cell-free DNA of HCC patients and used them to train machine learning models, which successfully differentiated HCC patients from high-risk individuals without the cancer in testing.
- * By integrating these methylation markers with existing serum biomarkers in a commercial test, they achieved improved detection accuracy for HCC, demonstrating potential for early diagnosis.
Article Abstract
This study exploited hepatocellular carcinoma (HCC)-specific circulating DNA methylation profiles to improve the accuracy of a current screening assay for HCC patients in high-risk populations. Differentially methylated regions in cell-free DNA between 58 nonmetastatic HCC and 121 high-risk patients with liver cirrhosis or chronic hepatitis were identified and used to train machine learning classifiers. The model could distinguish HCC from high-risk non-HCC patients in a validation cohort, with an area under the curve of 0.84. Combining these markers with the three serum biomarkers (AFP, lectin-reactive AFP, des-γ-carboxy prothrombin) in a commercial test, μTASWako, achieved an area under the curve of 0.87 and sensitivity of 68.8% at 95.8% specificity. HCC-specific circulating DNA methylation markers may be added to the available assay to improve the early detection of HCC.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.2217/fon-2022-1218 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!