PUBLICATION
Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks
- Authors
- Chen, Y., Yang, W., Chen, Q., Liu, Q., Liu, J., Zhang, Y., Li, B., Li, D., Nan, J., Li, X., Wu, H., Xiang, X., Peng, Y., Wang, J., Su, S., Wang, Z.
- ID
- ZDB-PUB-210325-2
- Date
- 2021
- Source
- Journal of translational medicine 19: 122 (Journal)
- Registered Authors
- Keywords
- Chronic hepatitis B (CHB), Chronic liver disease, Cirrhosis, Dynamic modular networks, HCC risk, Hepatocellular carcinoma (HCC), Sequential allosteric modules
- MeSH Terms
-
- Animals
- Carcinoma, Hepatocellular*/genetics
- Hepatitis B virus
- Hepatitis B, Chronic*
- Humans
- Liver Cirrhosis
- Liver Neoplasms*/genetics
- Zebrafish
- PubMed
- 33757544 Full text @ J Transl Med
Citation
Chen, Y., Yang, W., Chen, Q., Liu, Q., Liu, J., Zhang, Y., Li, B., Li, D., Nan, J., Li, X., Wu, H., Xiang, X., Peng, Y., Wang, J., Su, S., Wang, Z. (2021) Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks. Journal of translational medicine. 19:122.
Abstract
Background Discovering potential predictive risks in the super precarcinomatous phase of hepatocellular carcinoma (HCC) without any clinical manifestations is impossible under normal paradigm but critical to control this complex disease.
Methods In this study, we utilized a proposed sequential allosteric modules (AMs)-based approach and quantitatively calculated the topological structural variations of these AMs.
Results We found the total of 13 oncogenic allosteric modules (OAMs) among chronic hepatitis B (CHB), cirrhosis and HCC network used SimiNEF. We obtained the 11 highly correlated gene pairs involving 15 genes (r > 0.8, P < 0.001) from the 12 OAMs (the out-of-bag (OOB) classification error rate < 0.5) partial consistent with those in independent clinical microarray data, then a three-gene set (cyp1a2-cyp2c19-il6) was optimized to distinguish HCC from non-tumor liver tissues using random forests with an average area under the curve (AUC) of 0.973. Furthermore, we found significant inhibitory effect on the tumor growth of Bel-7402, Hep 3B and Huh7 cell lines in zebrafish treated with the compounds affected those three genes.
Conclusions These findings indicated that the sequential AMs-based approach could detect HCC risk in the patients with chronic liver disease and might be applied to any time-dependent risk of cancer.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping