Human lung epithelial cells cultured in the presence of radon-emitting rock experience gene expression changes similar to those associated with tobacco smoke exposure
Bibliographic record
Abstract
Radon is the second leading cause of lung cancer, after tobacco smoke. While tobacco smoke-induced carcinogenesis has been studied extensively, far less is known about radon-induced carcinogenesis, particularly in relation to the influence of radon on gene expression. The objectives of the work described herein were to (a) determine if and how exposure to low dose radon-emitting rock influences cells, at the gene expression level, and (b) compare any gene expression changes resulting from the exposure to radon-emitting rock with those induced by exposure to tobacco smoke. Any potential radiation-induced gene expression changes were also compared to those induced by exposure to cannabis smoke, a non-carcinogen at low doses, used here as a smoke exposure comparator. Human lung epithelial cells were exposed to radon-emitting rock, tobacco smoke or cannabis smoke, over months, and RNA-sequencing was carried out. We found that the rock-exposed cells experienced significant gene expression changes, particularly of the gene AKR1C3, and that these changes, over time, increasingly reflected those associated with exposure to tobacco, but not cannabis, smoke. We postulate that the early gene expression changes common to both the radiation and tobacco smoke exposures constitute a related - potentially pre-carcinogenic - response. Our findings suggest that the length of time a dividing population of cells is exposed to a constant low concentration of radon (with a potential cumulative absorbed dose) could be an important risk parameter for neoplastic transformation/carcinogenesis.
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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) | 0.002 | 0.001 |
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".