Targeting Hyaluronidase for Cancer Therapy: Antitumor Activity of Sulfated Hyaluronic Acid in Prostate Cancer Cells
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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