Role of Tumor Necrosis Factor-α and TRAIL in High-Dose Radiation–Induced Bystander Signaling in Lung Adenocarcinoma
Bibliographic record
Abstract
In the present study, ionizing radiation (IR)-induced bystander effects were investigated in two lung cancer cell lines. A549 cells were found to be more resistant to radiation-conditioned medium (RCM) obtained from A549 cells when compared with the H460 exposed to RCM procured from H460 cells. Significant release of tumor necrosis factor-alpha (TNF-alpha) was observed in A549 cells after IR/RCM exposure, and the survival was reversed with neutralizing antibody against TNF-alpha. In H460 cells, significant release of TNF-related apoptosis-inducing ligand (TRAIL), but not TNF-alpha, was observed in response to IR, RCM exposure, or RCM + 2Gy, and neutralizing antibody against TRAIL diminished clonogenic inhibition. Mechanistically, TNF-alpha present in RCM of A549 was found to mediate nuclear factor-kappaB (NF-kappaB) translocation to nucleus, whereas the soluble TRAIL present in RCM of H460 cells mobilized the nuclear translocation of PAR-4 (a proapoptotic protein). Analysis of IR-inducible early growth response-1 (EGR-1) function showed that EGR-1 was functional in A549 cells but not in H460 cells. A significant decrease in RCM-mediated apoptosis was observed in both A549 cells stably expressing small interfering RNA EGR-1 and EGR-1(-/-) mouse embryonic fibroblast cells. Thus, the high-dose IR-induced bystander responses in A549 may be dependent on the EGR-1 function and its target gene TNF-alpha. These findings show that the reduced bystander response in A549 cells is due to activation of NF-kappaB signaling by TNF-alpha, whereas enhanced response to IR-induced bystander signaling in H460 cells was due to release of TRAIL associated with nuclear translocation of PAR-4.
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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 | 0.001 | 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".