Primary liver cancer develops from multifactorial etiologies, resulting in extensive genomic heterogeneity. To probe the common mechanism of hepatocarcinogenesis, we interrogated temporal gene expression profiles in a group of mouse models with hepatic steatosis, fibrosis, inflammation, and, consequently, tumorigenesis. Instead of anticipated progressive changes, we observed a sudden molecular switch at a critical precancer stage, by developing analytical platform that focuses on transcription factor (TF) clusters. Coarse-grained network modeling demonstrated that an abrupt transcriptomic transition occurred once changes were accumulated to reach a threshold. Based on the experimental and bioinformatic data analyses as well as mathematical modeling, we derived a tumorigenic index (TI) to quantify tumorigenic signal strengths. The TI is powerful in predicting the disease status of patients with metabolic disorders and also the tumor stages and prognosis of liver cancer patients with diverse backgrounds. This work establishes a quantitative tool for triage of liver cancer patients and also for cancer risk assessment of chronic liver disease patients.
Keywords: liver cancer; quantitative analysis; transcription factor clusters; tumorigenic index.