Conclusion
Our study indicates that the miRAGe panel has low rate of both missed diagnosis and misdiagnosis rates, potentially serving as a useful diagnostic tool for HBV-related HCC in early stage, which may subsequently contribute to improve the prognosis.
Methods
We enrolled 108 early-stage HCC patients and 102 non-HCC individuals underlying HBV infection, collecting plasma exosomal miRNAs (exo-miRNAs) from all participants. These patients were randomly assigned to sequencing, screening, training, and validation group. After preliminary screening of candidate exo-miRNAs by next-generation high-throughput sequencing, qPCR data from the screening group were utilized in conjunction with the random forest machine learning algorithm to identify candidate exo-miRNAs with diagnostic potential. Subsequently, logistic regression diagnostic model was constructed using the relative expression levels of candidate exo-miRNAs, alpha-fetoprotein (AFP) levels and clinical parameters of gender and the presence of cirrhosis from the training group. The diagnostic accuracy of diagnostic model was subsequently validated in the validation group.
Purpose
Hepatocellular carcinoma (HCC) associated with Hepatitis B Virus (HBV) is one of the most severe malignancies in East Asia, where early diagnosis is crucial for improving patient prognosis. So we aim to identify effective early diagnostic model for HCC. Design and
Results
Firstly, we identified miR-212-5p, miR-1248, and miR-1250-5p as candidate exo-miRNAs with potential diagnostic value. The exo-miRNAs panel, which consisted of miR-212-5p, miR-1248, miR-1250-5p, along with clinical parameters of gender and cirrhosis, achieved an AUC of 0.8634 (95% CI: 0.8027-0.9241), demonstrating diagnostic performance non-inferior to AFP in the independent dataset. Subsequently, by combining exo-miRNAs, AFP level and clinical parameter of gender, we enhanced the diagnostic panel, miRAGe, which exhibited an AUC of 0.9499 (95% CI: 0.9192-0.9806), sensitivity of 0.8900, and specificity of 0.9468.