Hypoxia-Inducible Factor-1-Mediated Activation of Stanniocalcin-1 in Human Cancer Cells
Bibliographic record
Abstract
Stanniocalcin-1 (STC1) is an endocrine hormone originally discovered in the corpuscles of Stannius, endocrine glands on kidneys of bony fishes, and also has been identified in mammals. The mammalian STC1 gene is widely expressed in various tissues and appears to be involved in diverse biological processes. There is growing evidence to suggest that altered patterns of gene expression have a role in human cancer development. Recently STC1 has been identified as a stimulator of mitochondrial respiration and has been hypothesized to be functionally related to the Warburg effect, of which hypoxia-inducible factor (HIF)-1 plays a key role in reprogramming tumor metabolism. This prompted us to examine the involvement of HIF-1 in the regulation of STC1 expression in tumor hypoxia. Our data reveal that hypoxia can stimulate STC1 gene expression in various human cancer cell lines, including those derived from colon carcinomas, nasopharyngeal cancer (CNE-2, HONE-1, HK-1), and ovarian cancer (CaOV3, OVCAR3, SKOV3). By far, the greatest response was observed in CNE-2 cells. In further studies on CNE-2 cells, desferrioxamine, cobalt chloride, and O(2) depletion all increased HIF-1alpha protein and STC1 mRNA levels. Desferrioxamine treatment, when coupled with Fe replenishment, abolished these effects. RNA interference studies further confirmed that endogenous HIF-1alpha was a key factor in hypoxia-induced STC1 expression. The ability of vascular endothelial growth factor to stimulate STC1 expression in CNE-2 cells was comparatively low. Collectively, the present findings provide the first evidence of HIF-1 regulation of STC1 expression in human cancer cells. The studies have implications as to the role of STC1 in hypoxia induced adaptive responses in tumor 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) | 0.002 | 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".