FUCA1: An Underexplored p53 Target Gene Linking Glycosylation and Cancer Progression
Bibliographic record
Abstract
Cancer is a difficult-to-cure disease with high worldwide incidence and mortality, in large part due to drug resistance and disease relapse. Glycosylation, which is a common modification of cellular biomolecules, was discovered decades ago and has been of interest in cancer research due to its ability to influence cellular function and to promote carcinogenesis. A variety of glycosylation types and structures regulate the function of biomolecules and are potential targets for investigating and treating cancer. The link between glycosylation and carcinogenesis has been more recently revealed by the role of p53 in energy metabolism, including the p53 target gene alpha-L-fucosidase 1 (FUCA1), which plays an essential role in fucosylation. In this review, we summarize roles of glycan structures and glycosylation-related enzymes to cancer development. The interplay between glycosylation and tumor microenvironmental factors is also discussed, together with involvement of glycosylation in well-characterized cancer-promoting mechanisms, such as the epidermal growth factor receptor (EGFR), phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt) and p53-mediated pathways. Glycan structures also modulate cell–matrix interactions, cell–cell adhesion as well as cell migration and settlement, dysfunction of which can contribute to cancer. Thus, further investigation of the mechanistic relationships among glycosylation, related enzymes and cancer progression may provide insights into potential novel cancer treatments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".