Literature DB >> 21400513

Oncogenetic tree modeling of human hepatocarcinogenesis.

Thomas Longerich1, Michael Martin Mueller, Kai Breuhahn, Peter Schirmacher, Axel Benner, Christiane Heiss.   

Abstract

Classical comparative genomic hybridization (CGH) has been used to identify recurrent genomic alterations in human HCC. As hepatocarcinogenesis is considered as a stepwise process, we applied oncogenetic tree modeling on all available classical CGH data to determine occurrence of genetic alterations over time. Nine losses (1p, 4q, 6q, 8p, 9p, 13q, 16p, 16q and 17p) and ten gains (1q, 5p, 6p, 7p, 7q, 8q, 17q, 20p, 20q and Xq) of genomic information were used to build the oncogenetic tree model. Whereas gains of 1q and 8q together with losses of 8p formed a cluster that represents early etiology-independent alterations, the associations of gains at 6q and 17q as well as losses of 6p and 9p were observed during tumor progression. HBV-induced HCCs had significantly more chromosomal aberrations compared to HBV-negative tumors. Losses of 1p, 4q and 13q were associated with HBV-induced HCCs, whereas virus-negative HCCs showed an association of gains at 5p, 7, 20q and Xq. Using five aberrations that were significantly associated with tumor dedifferentiation a robust progression model of stepwise human hepatocarcinogensis (gain 1q → gain 8q → loss 4q → loss 16q → loss 13q) was developed. In silico analysis revealed that protumorigenic candidate genes have been identified for each recurrently altered hotspot. Thus, oncogenic candidate genes that are coded on chromosome arms 1q and 8q are promising targets for the prevention of malignant transformation and the development of biomarkers for the early diagnosis of human HCC that may significantly improve the treatment options and thus prognosis of HCC patients.
Copyright © 2011 UICC.

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Year:  2011        PMID: 21400513     DOI: 10.1002/ijc.26063

Source DB:  PubMed          Journal:  Int J Cancer        ISSN: 0020-7136            Impact factor:   7.396


  13 in total

1.  Integrative Genomic Analysis Identifies the Core Transcriptional Hallmarks of Human Hepatocellular Carcinoma.

Authors:  Coralie Allain; Gaëlle Angenard; Bruno Clément; Cédric Coulouarn
Journal:  Cancer Res       Date:  2016-09-12       Impact factor: 12.701

2.  Implementation of systems theory in liver cancer research.

Authors:  Federico Pinna; Kai Breuhahn
Journal:  Hepat Oncol       Date:  2015-01-12

3.  Long noncoding RNA HOTTIP/HOXA13 expression is associated with disease progression and predicts outcome in hepatocellular carcinoma patients.

Authors:  Luca Quagliata; Matthias S Matter; Salvatore Piscuoglio; Leila Arabi; Christian Ruiz; Alfredo Procino; Michal Kovac; Francesca Moretti; Zuzanna Makowska; Tujana Boldanova; Jesper B Andersen; Monika Hämmerle; Luigi Tornillo; Markus H Heim; Sven Diederichs; Clemente Cillo; Luigi M Terracciano
Journal:  Hepatology       Date:  2014-01-28       Impact factor: 17.425

Review 4.  Hepatocellular carcinoma and other malignancies in autoimmune hepatitis.

Authors:  Albert J Czaja
Journal:  Dig Dis Sci       Date:  2013-01-10       Impact factor: 3.199

Review 5.  [Genome-wide molecular screening for the identification of new targets in human hepatocellular carcinoma].

Authors:  T Longerich
Journal:  Pathologe       Date:  2012-11       Impact factor: 1.011

6.  A mathematical methodology for determining the temporal order of pathway alterations arising during gliomagenesis.

Authors:  Yu-Kang Cheng; Rameen Beroukhim; Ross L Levine; Ingo K Mellinghoff; Eric C Holland; Franziska Michor
Journal:  PLoS Comput Biol       Date:  2012-01-05       Impact factor: 4.475

7.  Inferring tree causal models of cancer progression with probability raising.

Authors:  Loes Olde Loohuis; Loes Olde Loohuis; Giulio Caravagna; Alex Graudenzi; Daniele Ramazzotti; Giancarlo Mauri; Marco Antoniotti; Bud Mishra
Journal:  PLoS One       Date:  2014-10-09       Impact factor: 3.240

8.  Identifying restrictions in the order of accumulation of mutations during tumor progression: effects of passengers, evolutionary models, and sampling.

Authors:  Ramon Diaz-Uriarte
Journal:  BMC Bioinformatics       Date:  2015-02-12       Impact factor: 3.169

9.  Defining order and timing of mutations during cancer progression: the TO-DAG probabilistic graphical model.

Authors:  Paola Lecca; Nicola Casiraghi; Francesca Demichelis
Journal:  Front Genet       Date:  2015-10-13       Impact factor: 4.599

Review 10.  MicroRNAs associated with HBV infection and HBV-related HCC.

Authors:  Kun-Lin Xie; Yan-Ge Zhang; Jun Liu; Yong Zeng; Hong Wu
Journal:  Theranostics       Date:  2014-09-19       Impact factor: 11.556

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