Bibliographic record
Abstract
Purpose To identify prognosis predictors for patients with prostate cancer (PCa). Methods Four independent PCa microarray datasets (GSE32448, GSE16560, GSE79957 and GSE17951) were reanalyzed to characterize the expression of semaphorin-3F (SEMA3F) gene between PCa patients and normal prostate tissues and the correlation between SEMA3F expression and the age, tumor/nodes/metastasis (TNM) staging, Gleason Grade Group, prostate-specific antigen level and overall survival of PCa patients. Gene set enrichment analysis was applied to investigate the potential relevant mechanisms regarding the expression of SEMA3F and the proliferation of PCa cells. Results The level of SEMA3F was significantly higher in normal prostate tissues compared with that in PCa cells (P<0.0001). Prostate cancer patients with higher expression of SEMA3F were associated with better TNM staging, Gleason Grade Group and overall survival than those with lower expression SEMA3F cohort. The result of gene set enrichment analysis indicated that SEMA3F might inhibit the proliferation of PCa cells through biological processes involving G2-mitosis checkpoint, E2F target and mitotic spindle. Conclusion Data suggest that SEMA3F might be a tumor suppressor of PCa and a protective factor for patient with PCa.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".