PubMed HealthSearch

PubMed · 41074151

Identification of radiation-sensitive genes as biomarkers for biodosimetry: an ex vivo analysis of TNFRSF10B, ZMAT3, POLH, and PLK2 in human blood samples.

Abstract

BACKGROUND: Humans are exposed to ionizing radiation (IR), which causes direct and indirect DNA damage. Biodosimetry is a critical component of clinical care following radiation exposure, enabling accurate assessment and mitigation of health effects. The present study was conducted to investigate the ex vivo expression of the genes TNFRSF10B, ZMAT3, PLK2, and POLH in human peripheral blood samples exposed to X-radiation at doses of 0, 0.5, 2, and 4 Gy at 0, 4, 24, and 48 hours post-exposure. Investigating gene expression dynamics through biodosimetry is a novel approach that may provide insights into gene-specific responses, potentially enhancing the accuracy and sensitivity of radiation dose assessment. MATERIALS AND METHODS: Peripheral blood samples were collected from five healthy volunteers and exposed to 0, 0.5, 2, or 4 Gy radiation with a 6 MV linear accelerator. Following the extraction of RNA and cDNA synthesis, gene expression analysis via qRT&#x2012;PCR was performed. These genes were normalized against the housekeeping gene &#x3b2;-actin, and the &#x394;&#x394;Ct method was used for statistical analysis of gene expression. The data were subjected to statistical analysis, and the level of significance (p < 0.05) was determined to test the effects of dose and time on gene expression. RESULTS: The expression of the TNFRSF10B, ZMAT3, POLH, and PLK2 genes was markedly dose- and time-dependent in response to X-ray radiation in vitro. Whole-blood samples irradiated at doses of 0, 0.5, 2, and 4 Gy and analyzed at four time points, 0, 4, 24, and 48 hours, respectively, revealed marked changes in the expression levels of the genes studied, revealing the mechanisms of the response at the cellular level to ionizing radiation. Although minor inter-individual variation in gene expression was observed, it did not significantly affect the overall trends, and the results remained statistically robust. CONCLUSION: These findings highlight a robust biodosimetry framework: TNFRSF10B demonstrated the highest diagnostic performance (AUC = 0.94; sensitivity = 98%; specificity = 75%; cut-off = 1.11), making it a highly reliable biomarker for radiation exposure. PLK2 also exhibited strong discriminative capacity (AUC = 0.84; sensitivity = 90%; specificity = 80%; cut-off =2.5), particularly for minimizing false positives. ZMAT3 (AUC = 0.78; sensitivity/specificity = 75%; cut-off = 3.21) showed balanced early-phase performance, whilePOLH (AUC = 0.73; sensitivity = 80%; specificity = 60%; cut-off = 1.10) may serve as a complementary marker. Collectively, these findings support a multi-gene expression approach for accurate biodosimetric assessment and improved triage following radiation exposure.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mahsa Boogari, Hossein Mozdarani, Aziz Mahmoudzadeh, Amirabbas Ebrahimi. 2025-10-10. Identification of radiation-sensitive genes as biomarkers for biodosimetry: an ex vivo analysis of TNFRSF10B, ZMAT3, POLH, and PLK2 in human blood samples.. https://doi.org/10.1186/s12920-025-02209-1

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans