ReviewFrontiers in bioinformatics2026
Radiation-induced network rewiring in cancer: systems-level insights into environmental radioactivity and precision oncology.
Review in Frontiers in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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0 citing papers in PubMed.
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Authors and funding
2 authors.
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Abstract
The impact of ionising radiation (IR) from environmental and therapeutic sources is complex and extends beyond the damage to DNA. These perturbations are conveyed by regulatory networks, such as transcription factors, microRNAs, long non-coding RNA, and signaling cascades, which in the end affect the susceptibility, progression, and treatment response to cancer. The traditional reductionist approaches, which are very useful, are not enough to explain emergent properties of these dynamic systems. The system's biology paradigm has a great potential of providing the explanation of the rewiring of cellular networks by radiation and identify novel targets to be targeted in precision oncology. The review summarizes the current literature on radiation-induced alterations of network patterns and on the remodelling of the molecular landscape of cancer cells due to exposure to environmental radioactivity and therapeutic radiation exposure. We create awareness of methodological progress in the development and analysis of radiation-responsive regulatory networks, identify key hub molecules and pathways, and discuss the translation potential of using radiation-responsive regulatory networks in individual radiotherapy. This review is different from previous reviews that have concentrated on single mechanisms in single-omics viewpoints or focused on a single radiation type as environmental or therapeutic, since it brings together network topology analysis, multi-omics data and clinical translatability in the context of both types of radiation exposure. We explore how weaknesses in radiation-sensitive components of a tumor can be used to target radiation-resistant ones while at the same time, radiation-induced damage to normal tissues can be reduced by network-aware approaches.
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