Bibliographic record
Abstract
In the past decade, aberrant O-GlcNAcylation has emerged as a new hallmark of cancer. O-GlcNAcylation is a post-translational modification that results when the amino-sugar β-D-N-acetylglucosamine (GlcNAc) is made in the hexosamine biosynthesis pathway (HBP) and covalently attached to serine and threonine residues in intracellular proteins by the glycosyltransferase O-GlcNAc transferase (OGT). O-GlcNAc moieties reflect the metabolic state of a cell and are removed by O-GlcNAcase (OGA). O-GlcNAcylation affects signaling pathways and protein expression by cross-talk with kinases and proteasomes and changes gene expression by altering protein interactions, localization, and complex formation. The HBP and O-GlcNAcylation are also recognized to mediate survival of cells in harsh conditions. Consequently, O-GlcNAcylation can affect many of the cellular processes that are relevant for cancer and is generally thought to promote tumor growth, disease progression, and immune escape. However, recent studies suggest a more nuanced view with O-GlcNAcylation acting as a tumor promoter or suppressor depending on the stage of disease or the genetic abnormalities, proliferative status, and state of the p53 axis in the cancer cell. Clinically relevant HBP and OGA inhibitors are already available and OGT inhibitors are in development to modulate O-GlcNAcylation as a potentially novel cancer treatment. Here recent studies that implicate O-GlcNAcylation in oncogenic properties of blood cancers are reviewed, focusing on chronic lymphocytic leukemia and effects on signal transduction and stress resistance in the cancer microenvironment. Therapeutic strategies for targeting the HBP and O-GlcNAcylation are also discussed.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".