Silencing FGL1 promotes prostate cancer cell apoptosis and inhibits EMT progression
Bibliographic record
Abstract
Emerging evidence from recent studies demonstrates that the FGL1/LAG-3 interaction axis plays a crucial role in mediating tumor immune evasion mechanisms, particularly through the suppression of T lymphocyte effector functions. However, the role of FGL1 in prostate cancer (PCa) remains unclear. Data was downloaded from The Cancer Genome Atlas (TCGA) database, and subjected to differential expression analysis. Single gene differential analysis to determine the correlation between FGL1 and DNAJC12 expression levels in prostate cancer. The expression of FGL1 was silenced by siRNA in PC3 prostate cancer cells. Lentiviruses infected DU145 to overexpress FGL1. Cell proliferation, apoptosis and EMT-related markers were detected in vitro. Animal experiments further confirmed the effect of FGL1 on prostate cancer. Up-regulated gene FGL1 was identified as the selected gene in this study among 3011 Differentially expressed genes. FGL1 had the highest positive relation with DNAJC12. The OS of PCa patients with high expression of FGL1 was significantly shorter. After silencing FGL1, PC3 cell proliferation was inhibited by 0.58-fold, while apoptosis increased by 16%, and the expression of cleaved-caspase-3 increased, while the expression of DNAJC12 and BCL-2 decreased. After overexpression of FGL1, the number of DU145 cells increased by 2.05-fold, the expression of cleaved-caspase-3 was inhibited, E-cadherin expression decreased, while N-cadherin and Vimentin expression increased. Tumor growth was inhibited, and the expression of FN1, n-cadherin, Vimentin and β-catenin decreased, while the expression of E-cadherin increased after silencing FGL1. Silencing FGL1 promotes prostate cancer cell apoptosis and inhibits EMT progression. FGL1 may be an independent prognostic marker and therapeutic target in 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 | 0.000 |
| 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".