Hypersialylation in Cancer: Modulation of Inflammation and Therapeutic Opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it