Hypersialylation in Cancer: Modulation of Inflammation and Therapeutic Opportunities
Bibliographic record
Abstract
Cell surface glycosylation is dynamic and often changes in response to cellular differentiation under physiological or pathophysiological conditions. Altered glycosylation on cancers cells is gaining attention due its wide-spread occurrence across a variety of cancer types and recent studies that have documented functional roles for aberrant glycosylation in driving cancer progression at various stages. One change in glycosylation that can correlate with cancer stage and disease prognosis is hypersialylation. Increased levels of sialic acid are pervasive in cancer and a growing body of evidence demonstrates how hypersialylation is advantageous to cancer cells, particularly from the perspective of modulating immune cell responses. Sialic acid-binding receptors, such as Siglecs and Selectins, are well-positioned to be exploited by cancer hypersialylation. Evidence is also mounting that Siglecs modulate key immune cell types in the tumor microenvironment, particularly those responsible for maintaining the appropriate inflammatory environment. From these studies have come new and innovative ways to block the effects of hypersialylation by directly reducing sialic acid on cancer cells or blocking interactions between sialic acid and Siglecs or Selectins. Here we review recent works examining how cancer cells become hypersialylated, how hypersialylation benefits cancer cells and tumors, and proposed therapies to abrogate hypersialylation of cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".