Implications of Proprotein Convertases in Ovarian Cancer Cell Proliferation and Tumor Progression: Insights for PACE4 as a Therapeutic Target
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Proprotein convertases are a family of kexin-like serine proteases that process proteins at single and multiple basic residues. Among the predicted and identified PC substrates, an increasing number of proteins having functions in cancer progression indicate that PCs may be potential targets for antineoplastic drugs. In support of this notion, we identified PACE4 as a vital PC involved in prostate cancer proliferation and progression, contrasting with the other co-expressed PCs. The aim of the present study was to test the importance of PCs in ovarian cancer cell proliferation and tumor progression. Based on tissue-expression profiles, furin, PACE4, PC5/6 and PC7 all displayed increased expression in primary tumor, ascites cells and metastases. These PCs were also expressed in variable levels in three model ovarian cell lines tested, namely SKOV3, CAOV3 and OVCAR3 cells. Since SKOV3 cells closely represented the PC expression profile of ovarian cancer cells, we chose them to test the effects of PC silencing using stable gene-silencing shRNA strategy to generate knockdown SKOV3 cells for each expressed PC. In vitro and in vivo assays confirmed the role of PACE4 in the sustainment of SKOV3 cell proliferation, which was not observed with the other three PCs. We also tested PACE4 peptide inhibitors on all three cell lines and observed consequent reduced cell proliferation which was correlated with PACE4 expression. Overall, these data support a role of PACE4 in promoting cell proliferation in ovarian cancer and provides further evidence for PACE4 as a potential therapeutic target.
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.
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 | 0.000 | 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) | 0.000 | 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