Neural protein gamma-synuclein interacting with androgen receptor promotes human prostate cancer progression
Bibliographic record
Abstract
BACKGROUND: Gamma-synuclein (SNCG) has previously been demonstrated to be significantly correlated with metastatic malignancies; however, in-depth investigation of SNCG in prostate cancer is still lacking. In the present study, we evaluated the role of SNCG in prostate cancer progression and explored the underlying mechanisms. METHODS: First, alteration of SNCG expression in LNCaP cell line to test the ability of SNCG on cellular properties in vitro and vivo whenever exposing with androgen or not. Subsequently, the Dual-luciferase reporter assays were performed to evaluate whether the role of SNCG in LNCaP is through AR signaling. Last, the association between SNCG and prostate cancer progression was assessed immunohistochemically using a series of human prostate tissues. RESULTS: Silencing SNCG by siRNA in LNCaP cells contributes to the inhibition of cellular proliferation, the induction of cell-cycle arrest at the G1 phase, the suppression of cellular migration and invasion in vitro, as well as the decrease of tumor growth in vivo with the notable exception of castrated mice. Subsequently, mechanistic studies indicated that SNCG is a novel androgen receptor (AR) coactivator. It interacts with AR and promotes prostate cancer cellular growth and proliferation by activating AR transcription in an androgen-dependent manner. Finally, immunohistochemical analysis revealed that SNCG was almost undetectable in benign or androgen-independent tissues prostate lesions. The high expression of SNCG is correlated with peripheral and lymph node invasion. CONCLUSIONS: Our data suggest that SNCG may serve as a biomarker for predicting human prostate cancer progression and metastasis. It also may become as a novel target for biomedical therapy in advanced prostate cancer.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".