Senescence is a permanent proliferation arrest in response to cell stress such as DNA damage. It contributes strongly to tissue aging and serves as a major barrier against tumor development. Most tumor cells are believed to bypass the senescence barrier (become “immortal”) by inactivating growth control genes such as TP53 and CDKN2A. They also reactivate telomerase reverse transcriptase. Senescence-to-immortality transition is accompanied by major phenotypic and biochemical changes mediated by genome-wide transcriptional modifications. This appears to happen during hepatocellular carcinoma (HCC) development in patients with liver cirrhosis, however, the accompanying transcriptional changes are virtually unknown. We investigated genome-wide transcriptional changes related to the senescence-to-immortality switch during hepatocellular carcinogenesis. Initially, we performed transcriptome analysis of senescent and immortal clones of Huh7 HCC cell line, and identified genes with significant differential expression to establish a senescence-related gene list. Through the analysis of senescence-related gene expression in different liver tissues we showed that cirrhosis and HCC display expression patterns compatible with senescent and immortal phenotypes, respectively; dysplasia being a transitional state. Gene set enrichment analysis revealed that cirrhosis/senescence-associated genes were preferentially expressed in non-tumor tissues, less malignant tumors, and differentiated or senescent cells. In contrast, HCC/immortality genes were up-regulated in tumor tissues, or more malignant tumors and progenitor cells. In HCC tumors and immortal cells genes involved in DNA repair, cell cycle, telomere extension and branched chain amino acid metabolism were up-regulated, whereas genes involved in cell signaling, as well as in drug, lipid, retinoid and glycolytic metabolism were down-regulated. Based on these distinctive gene expression features we developed a 15-gene hepatocellular immortality signature test that discriminated HCC from cirrhosis with high accuracy. Our findings demonstrate that senescence bypass plays a central role in hepatocellular carcinogenesis engendering systematic changes in the transcription of genes regulating DNA repair, proliferation, differentiation and metabolism.
Key words and phrases: Bayesian hierarchical model; hybrid Monte Carlo.MSC 2000: Primary 62J12; secondary 60J22.Abstract: Generalized linear models with random effects and/or serial dependence are commonly used to analyze longitudinal data. However, the computation and interpretation of marginal covariate effects can be difficult. This led Heagerty (1999Heagerty ( , 2002 to propose models for longitudinal binary data in which a logistic regression is first used to explain the average marginal response. The model is then completed by introducing a conditional regression that allows for the longitudinal, within-subject, dependence, either via random effects or regressing on previous responses. In this paper, the authors extend the work of Heagerty to handle multivariate longitudinal binary response data using a triple of regression models that directly model the marginal mean response while taking into account dependence across time and across responses. Markov Chain Monte Carlo methods are used for inference. Data from the Iowa Youth and Families Project are used to illustrate the methods.
Des modèlesà effets aléatoires età transition marginalisée pour des données longitudinales binaires multivariéesRésumé : On a souvent recoursà des modèles linéaires généralisés comportant des effets aléatoires et/ou une dépendance sérielle pour l'analyse de données longitudinales. Toutefois, le calcul et l'interprétation des effets marginaux des covariables est parfois ardu. Ceci a conduit Heagerty (1999Heagerty ( , 2002à proposer des modèles pour les données longitudinales binaires dans lesquels on commence par ajuster l'espérance marginale de la variable réponseà l'aide d'une régression logistique. La modélisation est ensuite complétée par une régression conditionnelle qui rend compte de la dépendance longitudinale intra-sujet au moyen d'effets aléatoires ou d'une régression sur les valeurs passées de la variable réponse. Dans cet article, les auteurś etendent les travaux de Heagerty au traitement de données longitudinales binaires multivariées grâceà trois régressions qui modélisent directement l'espérance marginale des variables réponses, en plus de décrire la dépendance entre elles et en fonction du temps. L'inférence s'appuie sur des méthodes de Monte-Carloà chaîne de Markov. Des données issues de l'étude sur la jeunesse et les familles de l'Iowa serventà illustrer le propos.
Box-Cox power transformation is a commonly used methodology to transform the distribution of a non-normal data into a normal one. Estimation of the transformation parameter is crucial in this methodology. In this study, the estimation process is hold via a searching algorithm and is integrated into wellknown seven goodness of fit tests for normal distribution. An artificial covariate method is also included for comparative purposes. Simulation studies are implemented to compare the effectiveness of the proposed methods. The methods are also illustrated on two different real life data applications. Moreover, an R package AID is proposed for implementation.
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