In Journal of Cancer
Purpose: This study aims to develop liquid biopsy assays for early HCC diagnosis and prognosis. Methods: Twenty-three microRNAs were first consolidated as a panel (HCCseek-23 panel) based on their reported functions in HCC development. Serum samples were collected from 103 early-stage HCC patients before and after hepatectomy. Quantitative PCR and machine learning random forest models were applied to develop diagnostic and prognostic models. Results: For HCC diagnosis, HCCseek-23 panel demonstrated 81% sensitivity and 83% specificity for identifying HCC in the early-stage; it showed 93% sensitivity for identifying alpha-fetoprotein (AFP)-negative HCC. For HCC prognosis, the differential expressions of 8 microRNAs (HCCseek-8 panel: miR-145, miR-148a, miR-150, miR-221, miR-223, miR-23a, miR-374a, and miR-424) were significantly associated with disease-free survival (DFS) (Log-rank test p-value = 0.001). Further model improvement using these HCCseek-8 panel in combination with serum biomarkers (i.e. AFP, ALT, and AST) demonstrated a significant association with DFS (Log-rank p-value = 0.011 and Cox proportional hazards analyses p-value = 0.002). Conclusion: To the best of our knowledge, this is the first report to integrate circulating miRNAs, AST, ALT, AFP, and machine learning for predicting DFS in early HCC patients undergoing hepatectomy. In this setting, HCCSeek-23 panel is a promising circulating microRNA assay for diagnosis, while HCCSeek-8 panel is promising for prognosis to identify early HCC recurrence.
Wong Victor Chun-Lam, Wong Ming-In, Lee Victor Ho-Fun, Man Kwan, Ng Kevin Tak-Pan, Cheung Tan To
2023
HCC diagnosis, HCC prognosis, Hepatocellular carcinoma, Liquid biopsy, Machine learning, hepatectomy, miRNA fingerprints
