Targeting Hyaluronidase for Cancer Therapy: Antitumor Activity of Sulfated Hyaluronic Acid in Prostate Cancer Cells
Bibliographic record
Abstract
The tumor cell-derived hyaluronidase (HAase) HYAL-1 degrades hyaluronic acid (HA) into proangiogenic fragments that support tumor progression. Although HYAL-1 is a critical determinant of tumor progression and a marker for cancer diagnosis and metastasis prediction, it has not been evaluated as a target for cancer therapy. Similarly, sulfated hyaluronic acid (sHA) has not been evaluated for biological activity, although it is an HAase inhibitor. In this study, we show that sHA is a potent inhibitor of prostate cancer. sHA blocked the proliferation, motility, and invasion of LNCaP, LNCaP-AI, DU145, and LAPC-4 prostate cancer cells, and induced caspase-8-dependent apoptosis associated with downregulation of Bcl-2 and phospho-Bad. sHA inhibited Akt signaling including androgen receptor (AR) phosphorylation, AR activity, nuclear factor κB (NFκB) activation, and VEGF expression. These effects were traced to a blockade in complex formation between phosphoinositide 3-kinase (PI3K) and HA receptors and to a transcriptional downregulation of HA receptors, CD44, and RHAMM, along with PI3K inhibition. Angiogenic HA fragments or overexpression of myristoylated Akt or HA receptors blunted these effects of sHA, implicating a feedback loop between HA receptors and PI3K/Akt signaling in the mechanism of action. In an animal model, sHA strongly inhibited LNCaP-AI prostate tumor growth without causing weight loss or apparent serum-organ toxicity. Inhibition of tumor growth was accompanied by a significant decrease in tumor angiogenesis and an increase in apoptosis index. Taken together, our findings offer mechanistic insights into the tumor-associated HA-HAase system and a preclinical proof-of-concept of the safety and efficacy of sHA to control prostate cancer growth and progression.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".