Hypoxia-regulated p53 and its effect on radiosensitivity in cancer cells
Bibliographic record
Abstract
PURPOSE: To determine the response of tumor suppressor p53 to hypoxia in different tumor cell lines and the involvement of p53 activity regulation in the effect of hypoxia on tumor cell sensitivity to radiation and hyperthermia. MATERIALS AND METHODS: Three tumor cell lines with functional p53 were treated with chronic or cyclic hypoxia followed by radiation or hyperthermia to investigate p53 activity and cell survival. Flow cytometry was used to investigate the effect of hypoxia-induced cell cycle arrest on radiosensitivity in KHT-C (mouse fibrosarcoma) cells. Transient transfection was performed to determine the role of altered p53 activity in KHT-C and SCC VII (mouse squamous-cell carcinoma) radiosensitivity. RESULTS: Aerobic radiosensitivity was decreased in KHT-C and SCC VII cells after in vitro chronic or cyclic hypoxia pretreatment, but in HT1080 cells, it was slightly increased after chronic hypoxia, and was unchanged after acute hypoxia pretreatment. Decreased radiosensitivity in hypoxia-pretreated KHT-C and SCC VII cells was unlikely due to hypoxia-induced cell cycle arrest, but rather seemed to be associated with increased expression of Mdm2 (mouse double minute-2) and decreased p53. Furthermore, hypoxia pretreatment inhibited the activation of p53 by radiation. Similar results were observed in hyperthermia treated KHT-C cells. Finally, decreased radiosensitivity was observed in both KHT-C and SCC VII cells transiently transfected with Mdm2 or anti-sense p53 cDNA. CONCLUSION: We demonstrated that hypoxia may decrease tumor cell radiosensitivity through the suppression of p53 activity in some tumor cell lines. These results suggested the response of p53 to hypoxia can be cell type specific and contribute to radiosensitivity of hypoxic cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".