期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2019
卷号：116
期号：52
页码：26873-26880
DOI：10.1073/pnas.1911193116
出版社：The National Academy of Sciences of the United States of America
摘要：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.
关键词：liver cancer ; tumorigenic index ; quantitative analysis ; transcription factor clusters
