In:
The Journal of Infectious Diseases, Oxford University Press (OUP), Vol. 223, No. 11 ( 2021-06-04), p. 1887-1896
Abstract:
Hepatitis B virus (HBV) infection is one of the main leading causes of hepatocellular carcinoma (HCC) worldwide. However, it remains uncertain how the reverse-transcriptase (rt) gene contributes to HCC progression. Methods We enrolled a total of 307 patients with chronic hepatitis B (CHB) and 237 with HBV-related HCC from 13 medical centers. Sequence features comprised multidimensional attributes of rt nucleic acid and rt/s amino acid sequences. Machine-learning models were used to establish HCC predictive algorithms. Model performances were tested in the training and independent validation cohorts using receiver operating characteristic curves and calibration plots. Results A random forest (RF) model based on combined metrics (10 features) demonstrated the best predictive performances in both cross and independent validation (AUC, 0.96; accuracy, 0.90), irrespective of HBV genotypes and sequencing depth. Moreover, HCC risk scores for individuals obtained from the RF model (AUC, 0.966; 95% confidence interval, .922–.989) outperformed α-fetoprotein (0.713; .632–.784) in distinguishing between patients with HCC and those with CHB. Conclusions Our study provides evidence for the first time that HBV rt sequences contain vital HBV quasispecies features in predicting HCC. Integrating deep sequencing with feature extraction and machine-learning models benefits the longitudinal surveillance of CHB and HCC risk assessment.
Type of Medium:
Online Resource
ISSN:
0022-1899
,
1537-6613
DOI:
10.1093/infdis/jiaa647
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2021
detail.hit.zdb_id:
1473843-0
