Somatostatin receptor‐1 induces cell cycle arrest and inhibits tumor growth in pancreatic cancer
Bibliographic record
Abstract
Functional somatostatin receptors (SSTR) are lost in human pancreatic cancer. Transfection of SSTR-1 inhibited pancreatic cancer cell proliferation in vitro. We hypothesize that stable transfection of SSTR-1 may inhibit pancreatic cancer growth in vivo possibly through cell cycle arrest. In this study, we examined the expression of SSTR-1 mRNA in human pancreatic cancer tissue specimens, and investigated the effect of SSTR-1 overexpression on cell proliferation, cell cycle, and tumor growth in a subcutaneous nude mouse model. We found that SSTR-1 mRNA was downregulated in the majority of pancreatic cancer tissue specimens. Transfection of SSTR-1 caused cell cycle arrest at the G(0)/G(1) growth phase, with a corresponding decline of cells in the S (mitotic) phase. The overexpression of SSTR-1 significantly inhibited subcutaneous tumor size by 71% and 43% (n = 5, P < 0.05, Student's t-test), and inhibited tumor weight by 69% and 47% (n = 5, P < 0.05, Student's t-test), in Panc-SSTR-1 and MIA-SSTR-1 groups, respectively, indicating the potent inhibitory effect of SSTR-1 on pancreatic cancer growth. Our data demonstrate that overexpression of SSTR-1 significantly inhibits pancreatic cancer growth possibly through cell cycle arrest. This study suggests that gene therapy with SSTR-1 may be a potential adjuvant treatment for pancreatic cancer.
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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".