Dissecting the Dual Nature of Hyaluronan in the Tumor Microenvironment
Bibliographic record
Abstract
Hyaluronan (HA) is a glycosaminoglycan with a simple structure but diverse and often opposing functions. The biological activities of this polysaccharide depend on its molecular weight and the identity of interacting receptors. HA is initially synthesized as high molecular-weight (HMW) polymers, which maintain homeostasis and restrain cell proliferation and migration in normal tissues. These HMW-HA functions are mediated by constitutively expressed receptors including CD44, LYVE-1, and STABILIN2. During normal processes such as tissue remodeling and wound healing, HMW-HA is fragmented into low molecular weight polymers (LMW-HA) by hyaluronidases and free radicals, which promote inflammation, immune cell recruitment and the epithelial cell migration. These functions are mediated by RHAMM and TLR2,4, which coordinate signaling with CD44 and other HA receptors. Tumor cells hijack the normally tightly regulated HA production/fragmentation associated with wound repair/remodeling, and these HA functions participate in driving and maintaining malignant progression. However, elevated HMW-HA production in the absence of fragmentation is linked to cancer resistance. The controlled production of HA polymer sizes and their functions are predicted to be key to dissecting the role of microenvironment in permitting or restraining the oncogenic potential of tissues. This review focuses on the dual nature of HA in cancer initiation vs. resistance, and the therapeutic potential of HA for chemo-prevention and as a target for cancer management.
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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 | 0.001 | 0.001 |
| 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".