The NOTCH-RIPK4-IRF6-ELOVL4 Axis Suppresses Squamous Cell Carcinoma
Bibliographic record
Abstract
Receptor-interacting serine/threonine protein kinase 4 (RIPK4) and its kinase substrate the transcription factor interferon regulatory factor 6 (IRF6) play critical roles in the development and maintenance of the epidermis. In addition, ourselves and others have previously shown that RIPK4 is a NOTCH target gene that suppresses the development of cutaneous and head and neck squamous cell carcinomas (HNSCCs). In this study, we used autochthonous mouse models, where the expression of Pik3caH1047R oncogene predisposes the skin and oral cavity to tumor development, and show that not only loss of Ripk4, but also loss of its kinase substrate Irf6, triggers rapid SCC development. In vivo rescue experiments using Ripk4 or a kinase-dead Ripk4 mutant showed that the tumor suppressive function of Ripk4 is dependent on its kinase activity. To elucidate critical mediators of this tumor suppressive pathway, we performed transcriptional profiling of Ripk4-deficient epidermal cells followed by multiplexed in vivo CRISPR screening to identify genes with tumor suppressive capabilities. We show that Elovl4 is a critical Notch-Ripk4-Irf6 downstream target gene, and that Elovl4 loss itself triggers SCC development. Importantly, overexpression of Elovl4 suppressed tumor growth of Ripk4-deficient keratinocytes. Altogether, our work identifies a potent Notch1-Ripk4-Irf6-Elovl4 tumor suppressor axis.
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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 | 0.001 |
| 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".