The Multifunctional Protein Glyceraldehyde-3-Phosphate Dehydrogenase Is Both Regulated and Controls Colony-Stimulating Factor-1 Messenger RNA Stability in Ovarian Cancer
Bibliographic record
Abstract
Although glyceraldehyde-3-phosphate dehydrogenase's (GAPDH) predilection for AU-rich elements has long been known, the expected connection between GAPDH and control of mRNA stability has never been made. Recently, we described GAPDH binding the AU-rich terminal 144 nt of the colony-stimulating factor-1 (CSF-1) 3' untranslated region (UTR), which we showed to be an mRNA decay element in ovarian cancer cells. CSF-1 is strongly correlated with the poor prognosis of patients with ovarian cancer. We investigated the functional significance of GAPDH's association with CSF-1 mRNA and found that GAPDH small interfering RNA reduces both CSF-1 mRNA and protein levels by destabilizing CSF-1 mRNA. CSF-1 mRNA half-lives were decreased by 50% in the presence of GAPDH small interfering RNA. RNA footprinting analysis of the 144 nt CSF-1 sequence revealed that GAPDH associates with a large AU-rich-containing region. The effects of binding of GAPDH protein or ovarian extracts to mutations of the AU-rich regions within the footprint were consistent with this finding. In a tissue array containing 256 ovarian and fallopian tube cancer specimens, we found that GAPDH was regulated in these cancers, with almost 50% of specimens having no GAPDH staining. Furthermore, we found that low GAPDH staining was associated with a low CSF-1 score (P = 0.008). In summary, GAPDH, a multifunctional protein, now adds regulation of mRNA stability to its repertoire. We are the first to evaluate the clinical role of GAPDH protein in cancer. In ovarian cancers, we show that GAPDH expression is regulated, and we now recognize that one of the many functions of GAPDH is to promote mRNA stability of CSF-1, an important cytokine in tumor progression.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".