Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 6 November 2017 / Approved: 6 November 2017 / Online: 6 November 2017 (15:02:00 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Cancers 2017, 9, 160
DOI: 10.3390/cancers9120160

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, negative regulation of growth, cellular response to Zn2+- Cd2+, Wnt and NIK/NF-kappaB signalling, which refine 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.

Subject Areas

Bioinformatics; Ionizing radiation; Microarrays; Radiation-induced bystander effects; Transcriptomics

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