Over-expression of ST3Gal-I promotes mammary tumorigenesis
Bibliographic record
Abstract
Changes in glycosylation are common in malignancy, and as almost all surface proteins are glycosylated, this can dramatically affect the behavior of tumor cells. In breast carcinomas, the O-linked glycans are frequently truncated, often as a result of premature sialylation. The sialyltransferase ST3Gal-I adds sialic acid to the galactose residue of core 1 (Galbeta1,3GalNAc) O-glycans and this enzyme is over-expressed in breast cancer resulting in the expression of sialylated core 1 glycans. In order to study the role of ST3Gal-I in mammary tumor development, we developed transgenic mice that over-express the sialyltransferase under the control of the human membrane-bound mucin 1 promoter. These mice were then crossed with PyMT mice that spontaneously develop mammary tumors. As expected, ST3Gal-I transgenic mice showed increased activity and expression of the enzyme in the pregnant and lactating mammary glands, the stomach, lungs and intestine. Although no obvious defects were observed in the fully developed mammary gland, when these mice were crossed with PyMT mice, a highly significant decrease in tumor latency was observed compared to the PyMT mice on an identical background. These results indicate that ST3Gal-I is acting as a tumor promoter in this model of breast cancer. This, we believe, is the first demonstration that over-expression of a glycosyltransferase involved in mucin-type O-linked glycosylation can promote tumorigenesis.
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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".