Cell cycle kinase inhibitor expression and hypoxia-induced cell cycle arrest in human cancer cell lines
Bibliographic record
Abstract
Flow cytometric analysis of fibroblasts, normal breast epithelial cells and breast or other cancer cell lines identified variation in the abilities of cell lines to undergo cell cycle arrest as a response to hypoxia. Human mammary epithelial cells (HMEC), normal fibroblasts (Hs68 and WI38), HeLa cervical carcinoma and HTB-30 breast carcinoma cells arrest in G(1)/S in response to severe hypoxia. Hep3B hepatocellular carcinoma cells did not exhibit orderly G(1)/S arrest in response to severe hypoxia. We found a general decrease in p16(INK4a) (p16) mRNA levels, with an associated decrease in p16 protein levels in both normal cells and in cancer cells, regardless of their cell cycle response to hypoxia. p27 protein levels did not correlate with the cell line's ability to enter a hypoxic G(1)/S arrest. Furthermore, cell lines that underwent G(1)/S arrest showed decreased expression of hypoxia inducible factor 1 (HIF-1alpha) and at least one member of INK4 or Sdi cell cycle kinase inhibitors families after 12-24 h of hypoxia. Conversely, Hep3B, which did not exhibit orderly hypoxia-associated G(1)/S arrest, also did not show decreased HIF-1alpha, INK4 or Sdi protein levels in hypoxia. Furthermore, Hep3B showed constitutive activating phosphorylation of Akt and inhibitory phosphorylation of GSK3beta, which was the opposite pattern to that exhibited by the cell lines showing the G(1)/S arrest phenotype. Inhibition of GSK3beta by lithium chloride treatment of HeLa cells converted the HIF-1alpha, p16 and p27 loss to levels unchanged by hypoxic exposure. Our results suggest that regulation of the cell cycle during hypoxia in either normal or cancer cells is not simply due to up-regulation of cell cycle kinase inhibitors. Furthermore, decreased protein expression of HIF-1alpha, p16 and p27 was associated with both a hypoxia-induced G(1)/S arrest phenotype and increased GSK3beta activity.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".