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Cancers 2017, 9(12), 160; doi:10.3390/cancers9120160

Integrative Bioinformatic Analysis of Transcriptomic Data Identifies Conserved Molecular Pathways Underlying Ionizing Radiation-Induced Bystander Effects (RIBE)

1
Department of Biochemistry and Molecular Biology, Faculty of Biology, National and Kapodistrian University of Athens, Zografou Campus, 15701 Athens, Greece
2
Metabolic Engineering and Bioinformatics Research Team, Institute of Biology Medicinal Chemistry & Biotechnology, National Hellenic Research Foundation, 11635 Athens, Greece
3
Department of Molecular Biology and Genetics, Democritus University of Thrace, 68100 Dragana, Greece
4
Enios Applications Private Limited Company, A17671 Athens, Greece
5
Physics Department, School of Applied Mathematical and Physical Sciences, National Technical University of Athens (NTUA), Zografou, 15780 Athens, Greece
*
Author to whom correspondence should be addressed.
Received: 6 November 2017 / Revised: 18 November 2017 / Accepted: 22 November 2017 / Published: 25 November 2017
(This article belongs to the Special Issue Radiation-Induced Carcinogenesis)
View Full-Text   |   Download PDF [1925 KB, uploaded 25 November 2017]   |  

Abstract

Ionizing radiation-induced bystander effects (RIBE) encompass a number of effects with potential for a plethora of damages in adjacent non-irradiated tissue. The cascade of molecular events is initiated in response to the exposure to ionizing radiation (IR), something that may occur during diagnostic or therapeutic medical applications. In order to better investigate these complex response mechanisms, we employed a unified framework integrating statistical microarray analysis, signal normalization, and translational bioinformatics functional analysis techniques. This approach was applied to several microarray datasets from Gene Expression Omnibus (GEO) related to RIBE. The analysis produced lists of differentially expressed genes, contrasting bystander and irradiated samples versus sham-irradiated controls. Furthermore, comparative molecular analysis through BioInfoMiner, which integrates advanced statistical enrichment and prioritization methodologies, revealed discrete biological processes, at the cellular level. For example, the negative regulation of growth, cellular response to Zn2+-Cd2+, and Wnt and NIK/NF-kappaB signaling, thus refining the description of the phenotypic landscape of RIBE. Our results provide a more solid understanding of RIBE cell-specific response patterns, especially in the case of high-LET radiations, like α-particles and carbon-ions. View Full-Text
Keywords: bioinformatics; ionizing radiation; microarrays; radiation-induced bystander effects; transcriptomics bioinformatics; ionizing radiation; microarrays; radiation-induced bystander effects; transcriptomics
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Yeles, C.; Vlachavas, E.-I.; Papadodima, O.; Pilalis, E.; Vorgias, C.E.; Georgakilas, A.G.; Chatziioannou, A. Integrative Bioinformatic Analysis of Transcriptomic Data Identifies Conserved Molecular Pathways Underlying Ionizing Radiation-Induced Bystander Effects (RIBE). Cancers 2017, 9, 160.

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