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

Low-dose ionizing radiation exposure remains a major challenge for long-term health risk assessment, particularly in retrospective cohorts with heterogeneous exposure scenarios and limited biological material. Although next-generation sequencing (NGS) technologies dominate contemporary molecular research, DNA microarrays remain relevant in radiation biology due to their standardization, reproducibility, cost-effectiveness, and compatibility with archived biospecimens. This narrative review examines the contribution of microarray-based transcriptomic and epigenomic profiling to the study of low-dose radiation effects (≤100 mSv, millisievert), with emphasis on human observational studies, radiation epidemiology, and biodosimetric applications. The literature was identified through targeted searches in PubMed and Web of Science (2000–2025). Evidence from experimental models and exposed populations is synthesized to identify recurrent molecular pathways, major sources of variability, and challenges affecting reproducibility and cross-cohort comparability. Based on this evidence, a conceptual framework is proposed to define conditions under which microarray-based analyses remain interpretable and translationally informative. Machine learning approaches are discussed in a supportive role, with emphasis on interpretability and biological plausibility. Overall, DNA microarrays are positioned as a mature, niche technology that complements next-generation sequencing platforms and remains particularly suited for retrospective cohort studies and long-term molecular monitoring in radiation research.