SEARCHOTHERradiation oncology, genomics

other·radiation oncology, genomics, pulmonology, geriatrics, space medicine, bioinformatics·PMC10213902

Establishing a genomic radiation-age association for space exploration supplements lung disease differentiation

Frontiers in Public Health · 7 authors, 2 centres

AI SUMMARY

FIDELITY 100%

POPULATIONHealthy human blood samples from multiple public datasets (total 537 samples for rad-age analysis, 236 for lung disease analysis)

INTERVENTIONEx vivo gamma radiation exposure at various doses (0, 0.1, 0.5, 2 Gy)

COMPARISONYoung (≤29 years) vs. old (>29 years); control vs. radiation exposure levels; various lung disease groups vs. controls

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TL;DR

This study uses publicly available genomic datasets to establish an association between ionizing radiation exposure and biological aging, finding a chronological age cutoff of 29 years to distinguish young from old. Through interaction models and pathway analysis, 234 genes were identified linking radiation and aging, with IL-1 signaling and PRPP biosynthesis as key pathways. The findings suggest that genes associated with both radiation and aging, particularly LAPTM4B, may help differentiate lung diseases, supporting future risk assessment after radiation exposure.

Full summary

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**Background:** Biological aging estimation lacks a consensus reference variable. Ionizing radiation (IR) exposure shares mechanistic overlap with aging—including oxidative stress, inflammation, genomic instability, and cellular senescence—but limited data directly link IR to aging biomarkers like telomere length. This study aims to establish a genomics-based association between IR and aging using chronological age (CA) as a proxy for biological age, with the goal of providing a foundation for future biological age estimation and space radiation risk assessment.

**Methods:** Five publicly available GEO datasets (GSE20173, GSE21240, GSE23515, GSE42488, GSE53351) were used, all involving human blood samples exposed to gamma radiation. Two datasets (Data-A1: GSE42488, n=74; Data-A2: GSE53351, n=300) of healthy controls were combined to empirically determine an age cutoff between young and old using 12 aging-related genes (CDKN2A, FOXO1, SIRT1, IL6, TFAM, mTOR, TSC1, TP53, SIRT6, MLKL, ALOX15B, TNFAIP3). A for-loop tested each age from 21–65 as a cutoff, using t-tests and generalized linear models with AUC-ROC evaluation (1,000 permutations per cutoff). Two datasets with both age and radiation exposure (Data-B1: GSE21240, n=48; Data-B2: GSE23515, n=95) plus a radiation-only dataset (Data-R1: GSE20173, n=20) were combined for interaction testing. Linear models (gene ~ age * radiation) were run with age as both categorical (young/old) and continuous variables. Significant genes (p < 0.05) were used for preranked pathway and disease analysis via Ingenuity Pathway Analysis (IPA). Finally, 234 unique rad-age interaction genes were tested on a lung disease dataset (Data-D1: GSE42834, n=236) for differentiation across disease (control, pneumonia, TB, sarcoidosis, lung cancer), ethnicity, and sex using pairwise t-tests and linear models.

**Key Results:** An age cutoff of 29 years was empirically determined, with 5 of 12 aging genes significantly different and an average AUC of 0.672 at this threshold. Interaction models yielded 234 unique genes. Seventeen genes overlapped across multiple t-test comparisons, with 15 showing continued upregulation with increasing radiation exposure. Two genes (ASCC3 and SYNPO) showed expression changes with both age and radiation. IPA identified IL-1 signaling and PRPP biosynthesis as significant overlapping pathways. Common diseases included cancers of high-proliferating cells (digestive system cancers, non-melanoma tumors, head and neck tumors, breast cancer, carcinoma). From the 234 rad-age genes, 10 were significant in pairwise comparisons across train/test/validation sets of Data-D1. Three genes showed significant interactions: LAPTM4B was the only gene significant across disease, ethnicity, and sex (p = 0.004 for three-way interaction), with a fold change greater than 2. SRPK1 was significant with disease and sex, and ROM1 with disease only.

**Clinical Implications:** The study provides genomic evidence supporting an association between ionizing radiation exposure and aging processes, with inflammation (IL-1 signaling) and metabolism (PRPP biosynthesis) as key mechanistic links. The identification of LAPTM4B as a gene that differentiates lung diseases while accounting for ethnicity and sex suggests that rad-age genes may supplement disease risk assessment following radiation exposure. These findings could inform future biological age estimation methods and space radiation monitoring, though validation in larger, more diverse populations is needed before clinical application.

PICO

PPOPULATION

Healthy human blood samples from multiple public datasets (total 537 samples for rad-age analysis, 236 for lung disease analysis)

IINTERVENTION

Ex vivo gamma radiation exposure at various doses (0, 0.1, 0.5, 2 Gy)

OOUTCOME

Gene expression changes, pathway enrichment, disease differentiation via significant genes (e.g., LAPTM4B)

STUDY TYPE

other

SPECIALTY

radiation oncology

SUMMARISED BY

AI pipeline

FIDELITY CHECK

100% · A

ORIGINAL PAPER

PMC10213902

Establishing a genomic radiation-age association for space exploration supplements lung disease differentiation

Frontiers in Public Health

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ABSTRACT

Abstract Purpose One possible way to quantify each individual's response or damage from ionizing radiation is to estimate their accelerated biological age following exposure. Since there is currently no definitive way to know if biological age estimations are accurate, we aim to establish a rad-age association using genomics as its foundation. Methods Two datasets were combined and used to empirically find the age cutoff between young and old patients. With age as both a categorical and continuous variable, two other datasets that included radiation exposure are used to test the interaction between radiation and age. The gene lists are oriented in preranked lists for both pathway and diseases analysis. Finally, these genes are used to evaluate another dataset on the clinical relevance in differentiating lung disease given ethnicity and sex using both pairwise t -tests and linear models. Results Using 12 well-known genes associated with aging, a threshold of 29-years-old was found to be the difference between young and old patients. The two interaction tests yielded 234 unique genes such that pathway analysis flagged IL-1 signaling and PRPP biosynthesis as significant with high cell proliferation diseases and carcinomas being a common trend. LAPTM4B was the only gene with significant interaction among lung disease, ethnicity, and sex, with fold change greater than two. Conclusion The results corroborate an initial association between radiation and age, given inflammation and metabolic pathways and multiple genes emphasizing mitochondrial function, oxidation, and histone modification. Being able to tie rad-age genes to lung disease supplements future work for risk assessment following radiation exposure. Keywords: bioinformatics, biological aging, genomics, lung disease, ionizing radiation

CITATION EXPORT

Nathan A Ruprecht, Sonalika Singhal, Kalli Schaefer et al.. Establishing a genomic radiation-age association for space exploration supplements lung disease differentiation. Frontiers in Public Health. (2023). https://doi.org/10.3389/fpubh.2023.1161124

AUTHORS

7 · 2 CENTRES

Nathan A Ruprecht, Sonalika Singhal, Kalli Schaefer, Jappreet S Gill, Benu Bansal, Donald Sens, Sandeep K Singhal

1Department of Biomedical Engineering, University of North Dakota, Grand Forks, ND, United States
2Department of Pathology, University of North Dakota, Grand Forks, ND, United States