Truncated mucin type O-Glycans in Epithelial Cancers: Central Drivers or Blind Passengers?
Bibliographic record
Abstract
Aberrant expression of truncated mucin type O-glycans is a known hallmark of epithelial cancers and represents an attractive drug target due to the relative low expression of these glycoforms in normal human tissue. However, the underlying mechanisms resulting in truncation of O-glycans as well as their potential roles in cancer development has yet to be fully understood. To investigate O-glycan influence on tissue formation and cancer, we created a library of human, primary keratinocytes with genetically engineered deficiencies in O-glycosylation capacity. Using an organotypic 3D model of human skin, we show that induction of truncated O-glycans instigates delayed differentiation and change in tissue architecture in the basal layers. Furthermore, our genetically engineered cell-lines provide evidence that truncated O-glycans result in increased proliferation. To map the O-glycan landscape of human cancers in vivo, we conducted a systematic analysis of T-, Tn- and STn-antigen expression in several tissue samples from a range of epithelial cancers using immunohistochemistry. In order to probe the mechanisms underlying O-glycan truncation, we employed a strategy combining whole-genome, CRISPR-Cas mediated loss-of-function screening and flow cytometry sorting based on glycan-specific antibodies. This strategy yields a deeper understanding of the mutagenic landscape potentially resulting in O-glycan truncation in human cells (and potentially cancers), extending beyond perturbations of genes already known to results in O-glycan truncation, i.e. COSMC and C1GALT1. In conclusion, our work indicates potential roles for truncated O-glycans in tissue homeostasis, provides a mapping of O-glycoforms in human cancers and yields new insights into mechanisms underlying O-glycan truncation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".