A tumorigenic index for quantitative analysis of liver cancer initiation and progression
Menée à l'aide de modèles murins de tumorigenèse hépatique et à partir de données portant sur des patients atteints d'un carcinome hépatocellulaire ou d'une maladie hépatique chronique, cette étude évalue la performance d'un système d'indice basé sur le transcriptome, notamment sur les facteurs de transcription, pour prédire le niveau de progression de maladies métaboliques, le stade d'une tumeur hépatique ou son pronostic
The mechanisms of hepatocarcinogenesis are poorly understood. In this study, we interrogated the temporal gene expression profiles in mouse livers during progression of chronic fatty liver diseases to carcinogenesis. By establishing an analytical system that focuses on transcription factor (TF) clusters, we identified a sudden switch from healthy liver to tumor tissues in the transcriptomes at precancer stage, prior to detection of any tumor nodule. We further developed a platform to calculate tumorigenic index (TI) based on the transcriptome, especially the TF clusters, to measure tumorigenic signal strengths. This quantitative analytical tool of TI is powerful in assessing cancer risk of chronic liver disease patients and in predicting tumor stages and prognosis of liver cancer patients.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.
Proceedings of the National Academy of Sciences , résumé, 2018