Data from Synergistic Function of Smad4 and PTEN in Suppressing Forestomach Squamous Cell Carcinoma in the Mouse
Bibliographic record
Abstract
<div>Abstract<p>The genetic bases underlying esophageal tumorigenesis are poorly understood. Our previous studies have shown that coordinated deletion of the <i>Smad4</i> and <i>PTEN</i> genes results in accelerated hair loss and skin tumor formation in mice. Herein, we exemplify that the concomitant inactivation of <i>Smad4</i> and <i>PTEN</i> accelerates spontaneous forestomach carcinogenesis at complete penetrance during the first 2 months of age. All of the forestomach tumors were invasive squamous cell carcinomas (SCCs), which recapitulated the natural history and pathologic features of human esophageal SCCs. A small population of the SCC lesions was accompanied by adenocarcinomas at the adjacent submucosa region in the double mutant mice. The rapid progression of forestomach tumor formation in the <i>Smad4</i> and <i>PTEN</i> double knockout mice corresponded to a dramatic increase in esophageal and forestomach epithelial proliferation. The decreased expression of p27, p21, and p16 together with the overexpression of cyclin D1 contributed cooperatively to the accelerated forestomach tumorigenesis in the double mutant mice. Our results point strongly to the crucial relevance of synergy between Smad4 and PTEN to suppress forestomach tumorigenesis through the cooperative induction of cell cycle inhibitors. (Cancer Res 2006; 66(14): 6972-81)</p></div>
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".