Microarray-Based Genomic Profiling in Low-Dose Radiation Research: Evidence, Limitations, and Translational...

Area of Science:

• Radiation Biology
• Molecular Epidemiology
• Genomics

Background:

• Assessing long-term health risks from low-dose ionizing radiation (≤100 mSv) is challenging, especially in retrospective studies with limited biological samples.
• While next-generation sequencing (NGS) is prevalent, DNA microarrays offer standardization, cost-effectiveness, and compatibility with archived specimens, maintaining relevance in radiation research.

Purpose of the Study:

• This review synthesizes the utility of microarray-based transcriptomic and epigenomic profiling for understanding low-dose radiation effects.
• It emphasizes applications in human observational studies, radiation epidemiology, and biodosimetry, proposing a framework for interpretable microarray analyses.

Main Methods:

• A narrative review of literature identified through targeted PubMed and Web of Science searches (2000-2025).
• Synthesis of evidence from experimental models and human populations to identify molecular pathways, variability sources, and reproducibility challenges.
• Discussion of machine learning's supportive role in data interpretation.

Main Results:

• Microarray analyses provide valuable insights into molecular responses to low-dose radiation.
• Key challenges include variability and cross-cohort comparability, necessitating standardized approaches.
• A conceptual framework is proposed to enhance the translational value of microarray data.

Conclusions:

• DNA microarrays are a mature, niche technology that effectively complements NGS platforms in radiation research.
• They are particularly suitable for retrospective cohort studies and long-term molecular monitoring for health risk assessment.
• Interpretable and biologically plausible machine learning approaches can support microarray data analysis.