Recombinant human PRG4 (rhPRG4) suppresses breast cancer cell invasion by inhibiting TGFβ-Hyaluronan-CD44 signalling pathway
Bibliographic record
Abstract
Metastasis is the major cause of cancer-related morbidity and mortality. The ability of cancer cells to become invasive and migratory contribute significantly to metastatic growth, which necessitates the identification of novel anti-migratory and anti-invasive therapeutic approaches. Proteoglycan 4 (PRG4), a mucin-like glycoprotein, contributes to joint synovial homeostasis through its friction-reducing and anti-adhesive properties. Adhesion to surrounding extracellular matrix (ECM) components is critical for cancer cells to invade the ECM and eventually become metastatic, raising the question whether PRG4 has an anti-invasive effect on cancer cells. Here, we report that a full-length recombinant human PRG4 (rhPRG4) suppresses the ability of the secreted protein transforming growth factor beta (TGFβ) to induce phenotypic disruption of three-dimensional human breast cancer cell-derived organoids by reducing ligand-induced cell invasion. In mechanistic studies, we find that rhPRG4 suppresses TGFβ-induced invasiveness of cancer cells by inhibiting the downstream hyaluronan (HA)-cell surface cluster of differentiation 44 (CD44) signalling axis. Furthermore, we find that rhPRG4 represses TGFβ-dependent increase in the protein abundance of CD44 and of the enzyme HAS2, which is involved in HA biosynthesis. It is widely accepted that TGFβ has both tumor suppressing and tumor promoting roles in cancer. The novel finding that rhPRG4 opposes HAS2 and CD44 induction by TGFβ has implications for downregulating the tumor promoting roles, while maintaining the tumor suppressive aspects of TGFβ actions. Finally, these findings point to rhPRG4's potential clinical utility as a therapeutic treatment for invasive and metastatic breast 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 | 0.000 |
| 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".