Low expression of PRTN3 regulates the progression of gastric cancer by inhibition of cell cycle and promotion of apoptosis
Bibliographic record
Abstract
Background: Proteinase 3 (PRTN3) has been linked to the progression of different cancer types. In this study, the expression and cell biological function of PRTN3 were investigated in gastric cancer (GC) to assess its role in GC progression. Methods: The PRTN3 levels in 20 pairs of GC tissues were detected via quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) and Western blotting, while immunohistochemical staining was used to assess the PRTN3 levels in 47 GC tissue samples. The effects of stable lentivirus-mediated PRTN3 knockdown on GC cell proliferative, cell cycle, and apoptotic activity were evaluated using Cell Counting Kit-8 (CCK-8) and colony formation assays, nude mouse models, and flow cytometry. Results: Elevated levels of PRTN3 messenger RNA (mRNA) and protein were noted in GC tissues, mostly in the cytosol. High PRTN3 levels were positively correlated with GC tumor N staging. In vitro knockdown of PRTN3 suppressed cell cycle progression, promoted apoptotic induction, and decreased the concentrations of cell cycle-associated proteins (cyclin D1, CDK4, and CDK6) and apoptosis-related Bcl-2 while inducing the upregulation of Bax. Downregulation of PRTN3 inhibited GC cell growth both in vitro and in mouse models. Conclusions: Our study found that high expression of PRTN3 is associated with GC tumor N staging. And PRTN3 silencing could regulate GC progression by inhibiting the cell cycle and promoting apoptosis in GC cells, which could be a potential target for GC diagnosis and treatment.
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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".