O-GlcNAc elevation through activation of the hexosamine biosynthetic pathway enhances cancer cell chemoresistance
Bibliographic record
Abstract
Chemoresistance has become a major obstacle to the success of cancer therapy, but the mechanisms underlying chemoresistance are not yet fully understood. O-GlcNAcylation is a post-translational modification that is regulated by the hexosamine biosynthetic pathway (HBP) and has an important role in a wide range of cellular functions. Here we assessed the role of O-GlcNAcylation in chemoresistance and investigated the underlying cellular mechanisms. The results showed that the HBP has an important role in cancer cell chemoresistance by regulating O-GlcNAcylation. An increase in the levels of O-GlcNAcylation indicates an increased resistance of cancer cells to chemotherapy. Acute treatment with doxorubicin (DOX) or camptothecin (CPT) induced O-GlcNAcylation through HBP activation. In fact, the chemotherapy agents activated the AKT/X-box-binding protein 1 (XBP1) axis and then induced the HBP. Furthermore, the observed elevation of cellular O-GlcNAcylation led to activation of survival signalling pathways and chemoresistance in cancer cells. Finally, suppression of O-GlcNAcylation reduced the resistance of both established and primary cancer cells to chemotherapy. These results provide significant novel insights regarding the important role of the HBP and O-GlcNAcylation in regulating cancer chemoresistance. Thus, O-GlcNAc inhibition might offer a new strategy for improving the efficacy of chemotherapy.
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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".