Insulin‐like growth factor stimulation increases radiosensitivity of a pancreatic cancer cell line through endoplasmic reticulum stress under hypoxic conditions
Bibliographic record
Abstract
Tumor hypoxia is an obstacle to radiotherapy. Radiosensitivity under hypoxic conditions is determined by molecular oxygen levels, as well as by various biological cellular responses. The insulin-like growth factor (IGF) signaling pathway is a widely recognized survival signal that confers radioresistance. However, under hypoxic conditions the role of IGF signaling in radiosensitivity is still poorly understood. Here, we demonstrate that IGF-II stimulation decreases clonogenic survival under hypoxic conditions in the pancreatic cancer cell lines AsPC-1 and Panc-1, and in the human breast cancer cell line MCF-7. IGF treatment under hypoxic conditions suppressed increased radiation sensitivity in these cell lines by pharmacologically inhibiting the phosphoinositide 3-kinase-mammalian target of rapamycin pathway, a major IGF signal-transduction pathway. Meanwhile, IGF-II induced the endoplasmic reticulum stress response under hypoxia, including increased protein levels of CHOP and ATF4, mRNA levels of CHOP, GADD34, and BiP, as well as splicing levels of XBP-1. The response was suppressed by inhibiting phosphoinositide 3-kinase and mammalian target of rapamycin activity. Overexpression of CHOP in AsPC-1 cells increased radiation sensitivity by IGF-II simulation under hypoxic conditions, whereas suppression of CHOP expression levels with small hairpin RNA or a dominant negative form of a proline-rich extensin-like receptor protein kinase in hypoxia decreased IGF-induced radiosensitivity. IGF-induced endoplasmic reticulum stress contributed to radiosensitization independent of cell cycle status. Taken together, IGF stimulation increased radiosensitivity through the endoplasmic reticulum stress response under hypoxic conditions.
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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 | 0.001 |
| 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".