Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks

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.