Protease nexin 1 inhibits hedgehog signaling in prostate adenocarcinoma
Bibliographic record
Abstract
Prostate adenocarcinoma (CaP) patients are classified into low-, intermediate-, and high-risk groups that reflect relative survival categories. While there are accepted treatment regimens for low- and high-risk patients, intermediate-risk patients pose a clinical dilemma, as treatment outcomes are highly variable for these individuals. A better understanding of the factors that regulate the progression of CaP is required to delineate risk. For example, aberrant activation of the Hedgehog (Hh) pathway is implicated in CaP progression. Here, we identify the serine protease inhibitor protease nexin 1 (PN1) as a negative regulator of Hh signaling in prostate. Using human CaP cell lines and a mouse xenograft model of CaP, we demonstrate that PN1 regulates Hh signaling by decreasing protein levels of the Hh ligand Sonic (SHH) and its downstream effectors. Furthermore, we show that SHH expression enhanced tumor growth while overexpression of PN1 inhibited tumor growth and angiogenesis in mice. Finally, using comparative genome hybridization, we found that genetic alterations in Hh pathway genes correlated with worse clinical outcomes in intermediate-risk CaP patients, indicating the importance of this pathway in CaP.
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.
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 | 0.000 |
| 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".