miR-539 inhibits prostate cancer progression by directly targeting SPAG5
Bibliographic record
Abstract
BACKGROUND: We conducted multiple microarray datasets analyses from clinical and xenograft tumor tissues to search for disease progression-driving oncogenes in prostate cancer (PCa). Sperm-associated antigen 5 (SPAG5) attracted our attention. SPAG5 was recently identified as an oncogene participating in lung cancer and cervical cancer progression. However, the roles of SPAG5 in PCa progression remain unknown. METHODS: SPAG5 expression level in clinical primary PCa, metastatic PCa, castration resistant PCa, neuroendocrine PCa, and normal prostate tissues was investigated. We established multiple in vivo xenografts models using patient-derived tissues and investigated SPAG5 expression trend in these models. We also investigated the functions of SPAG5 in vivo and in vitro studies. Luciferase reporter assays were performed to investigate potential miRNAs that can regulate SPAG5. RESULTS: We identified that SPAG5 expression was gradually increased in PCa progression and its level was significantly associated with lymph node metastasis, clinical stage, Gleason score, and biochemical recurrence. Our results indicated that SPAG5 knockdown can drastically inhibit PCa cell proliferation, migration, and invasion in vitro and supress tumor growth and metastasis in vivo. We identified that miR-539 can directly target SPAG5. Ectopic overexpression of miR-539 can drastically inhibit SPAG5 expression and the restoration of SPAG5 expression can reverse the inhibitory effects of miR-539 on PCa cell proliferation and metastasis. CONCLUSION: Our results collectively showed a progression-driving role of SPAG5 in PCa which can be regulated by miR-539, suggesting that miR-539/SPAG5 can serve as a potential therapeutic target for PCa.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".