ArticleLiver research (Beijing, China)2024
Plasma microRNA-15a/16-1-based machine learning for early detection of hepatitis B virus-related hepatocellular carcinoma.
Article in Liver research (Beijing, China), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Unifying and unique roles of non-coding RNA biomarkers in liver and heart fibrosis.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2026Review
- Harnessing plasma transcriptomics for non-invasive cancer biomarker identification: a comprehensive review.Discover oncology · 2025Review
- Wide-spectrum profiling of plasma cell-free RNA and the potential for health-monitoring.RNA biology · 2025Article
- A spreadable self-gelling hemostatic powder sensitizes CAR-NK cell therapy to prevent hepatocellular carcinoma recurrence postresection.Journal of nanobiotechnology · 2025Article
- Non-coding RNAs as key regulators in hepatitis B virus-related hepatocellular carcinoma.Frontiers in immunology · 2025Review
- Plasma microRNA-15a/16-1 serves as a non-invasive indicator of liver fibrosis severity in individuals with chronic hepatitis B.Non-coding RNA research · 2024Article
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Authors and funding
7 authors.
Funding
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Abstract
Background and aims: Hepatocellular carcinoma (HCC), which is prevalent worldwide and has a high mortality rate, needs to be effectively diagnosed. We aimed to evaluate the significance of plasma microRNA-15a/16-1 (miR-15a/16) as a biomarker of hepatitis B virus-related HCC (HBV-HCC) using the machine learning model. This study was the first large-scale investigation of these two miRNAs in HCC plasma samples. Methods: Using quantitative polymerase chain reaction, we measured the plasma miR-15a/16 levels in a total of 766 participants, including 74 healthy controls, 335 with chronic hepatitis B (CHB), 47 with compensated liver cirrhosis, and 310 with HBV-HCC. The diagnostic performance of miR-15a/16 was examined using a machine learning model and compared with that of alpha-fetoprotein (AFP). Lastly, to validate the diagnostic efficiency of miR-15a/16, we performed pseudotemporal sorting of the samples to simulate progression from CHB to HCC. Results: Plasma miR-15a/16 was significantly decreased in HCC than in all control groups ( Conclusions: We developed and validated a plasma miR-15a/16-based machine learning model, which exhibited better diagnostic performance for the early diagnosis of HCC compared to that of AFP.
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