Oncotarget

Research Papers:

Time-course gene profiling and networks in demethylated retinoblastoma cell line

Federico Malusa, Monia Taranta, Nazar Zaki, Caterina Cinti and Enrico Capobianco _

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Oncotarget. 2015; 6:23688-23707. https://doi.org/10.18632/oncotarget.4644

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Abstract

Federico Malusa1, Monia Taranta2, Nazar Zaki3, Caterina Cinti2 and Enrico Capobianco1,4

1 Laboratory of Integrative Systems Medicine (LISM), Institute of Clinical Physiology, CNR, Pisa, Italy

2 Experimental Oncology Unit, Institute of Clinical Physiology, CNR, Siena, Italy

3 College of Information Technology (CIT), United Arab Emirates University (UAEU), Al Ain, UAE

4 Center for Computational Science (CCS), University of Miami, Miami, FL, USA

Correspondence to:

Enrico Capobianco, email:

Keywords: retinoblastoma cell line, demethylation, gene expression profiling, co-expression networks, regulatory maps

Received: November 17, 2014 Accepted: May 31, 2015 Published: June 25, 2015

Abstract

Retinoblastoma, a very aggressive cancer of the developing retina, initiatiates by the biallelic loss of RB1 gene, and progresses very quickly following RB1 inactivation. While its genome is stable, multiple pathways are deregulated, also epigenetically. After reviewing the main findings in relation with recently validated markers, we propose an integrative bioinformatics approach to include in the previous group new markers obtained from the analysis of a single cell line subject to epigenetic treatment. In particular, differentially expressed genes are identified from time course microarray experiments on the WERI-RB1 cell line treated with 5-Aza-2’-deoxycytidine (decitabine; DAC). By inducing demethylation of CpG island in promoter genes that are involved in biological processes, for instance apoptosis, we performed the following main integrative analysis steps: i) Gene expression profiling at 48h, 72h and 96h after DAC treatment; ii) Time differential gene co-expression networks and iii) Context-driven marker association (transcriptional factor regulated protein networks, master regulatory paths). The observed DAC-driven temporal profiles and regulatory connectivity patterns are obtained by the application of computational tools, with support from curated literature. It is worth emphasizing the capacity of networks to reconcile multi-type evidences, thus generating testable hypotheses made available by systems scale predictive inference power. Despite our small experimental setting, we propose through such integrations valuable impacts of epigenetic treatment in terms of gene expression measurements, and then validate evidenced apoptotic effects.


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