Keratinocyte-Specific Stat3 Heterozygosity Impairs Development of Skin Tumors in Human Papillomavirus 8 Transgenic Mice
Bibliographic record
Abstract
Human papillomaviruses (HPV) of the genus β are thought to play a role in human skin cancers, but this has been difficult to establish using epidemiologic approaches. To gain insight into the transforming activities of β-HPV, transgenic mouse models have been generated that develop skin tumors. Recent evidence suggests a central role of signal transducer and activator of transcription 3 (Stat3) as a transcriptional node for cancer cell-autonomous initiation of a tumor-promoting gene signature associated with cell proliferation, cell survival, and angiogenesis. Moreover, high levels of phospho-Stat3 have been detected in tumors arising in HPV8-CER transgenic mice. In this study, we investigate the in vivo role of Stat3 in HPV8-induced skin carcinogenesis by combining our established experimental model of HPV8-induced skin cancer with epidermis-restricted Stat3 ablation. Stat3 heterozygous epidermis was less prone to tumorigenesis than wild-type epidermis. Three of the 23 (13%) Stat3(+/-):HPV8 animals developed tumors within 12 weeks of life, whereas 54.3% of Stat3(+/+):HPV8 mice already exhibited tumors in the same observation period (median age for tumor appearance, 10 weeks). The few tumors that arose in the Stat3(+/-):HPV8 mice were benign and never progressed to a more malignant phenotype. Collectively, these results offer direct evidence of a critical role for Stat3 in HPV8-driven epithelial carcinogenesis. Our findings imply that targeting Stat3 activity in keratinocytes may be a viable strategy to prevent and treat HPV-induced skin 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".