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 micro

SUBMITTER: Chen Y 

PROVIDER: S-EPMC7989040 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature