Mechanism of Radiation Carcinogenesis

A special issue of Cells (ISSN 2073-4409).

Deadline for manuscript submissions: 15 July 2024 | Viewed by 949

Special Issue Editor

Special Issue Information

Dear Colleagues,

Ionizing radiation is a well-known carcinogen with certain characteristics. Malignant tumors develop in a stochastic manner, meaning that the risk of cancer upon ionizing radiation exposure is dose-dependent; there is currently no recognized threshold dose for radiation carcinogenesis. Ionizing radiation can lead to the development of cancer in any tissue, in any race, and at any age. Cancer development after radiation exposure has a long latency period of several years to several decades, which highlights the potentially increased relative risk of people exposed at a young age.

Cancer developing as a consequence of ionizing radiation exposure cannot be discriminated from spontaneously occurring cancers due to a lack of suitable biomarkers. Better knowledge of the mechanisms of radiation carcinogenesis might allow us to decipher cellular and molecular pathways specifically induced by ionizing radiation, thus identifying those cancers which develop as a direct consequence of radiation exposure. This could help us in better risk estimation and prediction at the level of the individual as well as potentially developing new countermeasures and/or therapeutic tools. In addition, this could also help in identifying an individual’s predisposition to ionizing-radiation-induced cancer.

Traditionally it is considered that ionizing radiation, due to its DNA-damaging effect, increases the mutational burden of cells and induces genetic instability, which are major steps in cancer initiation and/or progression. While this effect is undoubtedly a major step in the carcinogenic process, it has been shown that ionizing radiation has much broader consequences, manifesting in several non-DNA targeted effects. Thus, it might impact the microenvironment of the tumor-initiating cells by altering intra- and intercellular communication pathways, leading to cellular senescence, inflammation, modifying local and systemic immune interactions, etc. These effects in the context of radiation carcinogenesis have been explored less. Therefore, this Special Issue aims at bringing together a collection of research articles, reviews, and short communication focusing on various aspects of radiation carcinogenesis that can contribute to identifying specific signatures of cancers induced by ionizing radiation. Clinical studies are not within the scope of the current Special Issue.

Dr. Katalin Lumniczky
Guest Editor

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Keywords

  • ionizing radiation
  • cancer
  • DNA damage
  • non-targeted effects
  • intercellular signaling
  • inflammation

Published Papers (1 paper)

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Research

20 pages, 3604 KiB  
Article
Single-Cell Radiation Response Scoring with the Deep Learning Algorithm CeCILE 2.0
by Sarah Rudigkeit and Judith Reindl
Cells 2023, 12(24), 2782; https://0-doi-org.brum.beds.ac.uk/10.3390/cells12242782 - 07 Dec 2023
Viewed by 771
Abstract
External stressors, such as ionizing radiation, have massive effects on life, survival, and the ability of mammalian cells to divide. Different types of radiation have different effects. In order to understand these in detail and the underlying mechanisms, it is essential to study [...] Read more.
External stressors, such as ionizing radiation, have massive effects on life, survival, and the ability of mammalian cells to divide. Different types of radiation have different effects. In order to understand these in detail and the underlying mechanisms, it is essential to study the radiation response of each cell. This allows abnormalities to be characterized and laws to be derived. Tracking individual cells over several generations of division generates large amounts of data that can no longer be meaningfully analyzed by hand. In this study, we present a deep-learning-based algorithm, CeCILE (Cell classification and in vitro lifecycle evaluation) 2.0, that can localize, classify, and track cells in live cell phase-contrast videos. This allows conclusions to be drawn about the viability of the cells, the cell cycle, cell survival, and the influence of X-ray radiation on these. Furthermore, radiation-specific abnormalities during division could be characterized. In summary, CeCILE 2.0 is a powerful tool to characterize and quantify the cellular response to external stressors such as radiation and to put individual responses into a larger context. To the authors knowledge, this is the first algorithm with a fully integrated workflow that is able to do comprehensive single-cell and cell composite analysis, allowing them to draw conclusions on cellular radiation response. Full article
(This article belongs to the Special Issue Mechanism of Radiation Carcinogenesis)
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