Deregulation in STAT signaling is important for cutaneous T-cell lymphoma (CTCL) pathogenesis and cancer progression
Bibliographic record
Abstract
Deregulation of STAT signaling has been implicated in the pathogenesis for a variety of cancers, including CTCL. Constitutive activation of STAT5 and STAT3 was observed in early and late stages of CTCL, respectively. In early stages, IL-2, IL-7 and IL-15 signaling via JAK1 and JAK3 kinases is believed to be responsible for activating STAT5, while in advanced stages development of IL-21 autocrine signaling is thought to be important for STAT3 activation. Recent molecular evidence further suggests that upregulation of STAT5 in early disease stages results in increased expression of oncogenic miR-155 microRNA that subsequently targets STAT4 expression on mRNA level. STAT4 signaling is known to be critical for T helper (Th) 1 phenotype differentiation and its loss results in a switch from Th1 to Th2 phenotype in malignant T cells. During this switch the expression of STAT6 is often upregulated in CTCL. In advanced stages, activation of STAT3 and STAT5 may become completely cytokine-independent and be driven only via constitutively active JAK1 and JAK3 kinases. Further research into the molecular pathogenesis of JAK/STAT signaling in this cancer may enable us to develop effective therapies for our patients.
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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) | 0.002 | 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".