mTOR/MYC Axis Regulates O-GlcNAc Transferase Expression and O-GlcNAcylation in Breast Cancer
Bibliographic record
Abstract
UNLABELLED: Cancers exhibit altered metabolism characterized by increased glucose and glutamine uptake. The hexosamine biosynthetic pathway (HBP) uses glucose and glutamine, and directly contributes to O-linked-β-N-acetylglucosamine (O-GlcNAc) modifications on intracellular proteins. Multiple tumor types contain elevated total O-GlcNAcylation, in part, by increasing O-GlcNAc transferase (OGT) levels, the enzyme that catalyzes this modification. Although cancer cells require OGT for oncogenesis, it is not clear how tumor cells regulate OGT expression and O-GlcNAcylation. Here, it is shown that the PI3K-mTOR-MYC signaling pathway is required for elevation of OGT and O-GlcNAcylation in breast cancer cells. Treatment with PI3K and mTOR inhibitors reduced OGT protein expression and decreased levels of overall O-GlcNAcylation. In addition, both AKT and mTOR activation is sufficient to elevate OGT/O-GlcNAcylation. Downstream of mTOR, the oncogenic transcription factor c-MYC is required and sufficient for increased OGT protein expression in an RNA-independent manner and c-MYC regulation of OGT mechanistically requires the expression of c-MYC transcriptional target HSP90A. Finally, mammary tumor epithelial cells derived from MMTV-c-myc transgenic mice contain elevated OGT and O-GlcNAcylation and OGT inhibition in this model induces apoptosis. Thus, OGT and O-GlcNAcylation levels are elevated via activation of an mTOR/MYC cascade. IMPLICATIONS: Evidence indicates OGT as a therapeutic target in c-MYC-amplified cancers.
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) | 0.003 | 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".