SNP in TXNRD2 Associated With Radiation-Induced Fibrosis: A Study of Genetic Variation in Reactive Oxygen Species Metabolism and Signaling
Bibliographic record
Abstract
PURPOSE: The aim of the study was to identify noninvasive markers of treatment-induced side effects. Reactive oxygen species (ROS) are generated after irradiation, and genetic variation in genes related to ROS metabolism might influence the level of radiation-induced adverse effects (AEs). METHODS AND MATERIALS: 92 breast cancer (BC) survivors previously treated with hypofractionated radiation therapy were assessed for the AEs subcutaneous atrophy and fibrosis, costal fractures, lung fibrosis, pleural thickening, and telangiectasias (median follow-up time 17.1 years). Single-nucleotide polymorphisms (SNPs) in 203 genes were analyzed for association to AE grade. SNPs associated with subcutaneous fibrosis were validated in an independent BC survivor material (n=283). The influence of the studied genetic variation on messenger ribonucleic acid (mRNA) expression level of 18 genes previously associated with fibrosis was assessed in fibroblast cell lines from BC patients. RESULTS: Subcutaneous fibrosis and atrophy had the highest correlation (r=0.76) of all assessed AEs. The nonsynonymous SNP rs1139793 in TXNRD2 was associated with grade of subcutaneous fibrosis, the reference T-allele being more prevalent in the group experiencing severe levels of fibrosis. This was confirmed in another sample cohort of 283 BC survivors, and rs1139793 was found significantly associated with mRNA expression level of TXNRD2 in blood. Genetic variation in 24 ROS-related genes, including EGFR, CENPE, APEX1, and GSTP1, was associated with mRNA expression of 14 genes previously linked to fibrosis (P≤.005). CONCLUSION: Development of subcutaneous fibrosis can be associated with genetic variation in the mitochondrial enzyme TXNRD2, critically involved in removal of ROS, and maintenance of the intracellular redox balance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".