Elucidating the role of interleukin-17F in cutaneous T-cell lymphoma
Bibliographic record
Abstract
Inappropriately regulated expression of interleukin (IL)-17A is associated with the development of inflammatory diseases and cancer. However, little is known about the role of other IL-17 family members in carcinogenesis. Here, we show that a set of malignant T-cell lines established from patients with cutaneous T-cell lymphoma (CTCL) spontaneously secrete IL-17F and that inhibitors of Janus kinases and Signal transducer and activator of transcription 3 are able to block that secretion. Other malignant T-cell lines produce IL-17A but not IL-17F. Upon activation, however, some of the malignant T-cell lines are able to coexpress IL-17A and IL-17F, leading to formation of IL-17A/F heterodimers. Clinically, we demonstrate that IL-17F messenger RNA expression is significantly increased in CTCL skin lesions compared with healthy donors and patients with chronic dermatitis. IL-17A expression is also increased and a significant number of patients express high levels of both IL-17A and IL-17F. Concomitantly, we observed that the expression of the IL-17 receptor is significantly increased in CTCL skin lesions compared with control subjects. Importantly, analysis of a historic cohort of 60 CTCL patients indicates that IL-17F expression is associated with progressive disease. These findings implicate IL-17F in the pathogenesis of CTCL and suggest that IL-17 cytokines and their receptors may serve as therapeutic targets.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".