Analysis of STAT4 expression in cutaneous T-cell lymphoma (CTCL) patients and patient-derived cell lines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deregulation of STAT signaling has been implicated in the pathogenesis for a variety of cancers, including CTCL. Recent reports indicate that loss of STAT4 expression is an important prognostic marker for CTCL progression and is associated with the acquisition of T helper 2 cell phenotype by malignant cells. However, little is known about the molecular mechanism behind the downregulation of STAT4 in this cancer. In the current work we test the expression of STAT4 and STAT6 via RT-PCR and/or Western Blot in CTCL lesional skin samples and in immortalized patient-derived cell lines. In these malignant cell lines we correlate the expression of STAT4 and STAT6 with the T helper (Th) phenotype markers and test the effect of Histone Deacetylase (HDAC) inhibitors and siRNA-mediated knock down of miR-155 on STAT4 expression. Our findings demonstrate that STAT4 expression correlates with Th1 phenotype, while STAT6 is associated with the Th2 phenotype. Our results further document that STAT4 and STAT6 genes are inversely regulated in CTCL. Treatment with HDAC inhibitors upregulates STAT4 expression, while at the same time decreases STAT6 expression in MyLa cells. Also, siRNA-mediated knock down of miR-155 leads to upregulation in STAT4 expression in MyLa cells. In summary, our results suggest that loss of STAT4 expression and associated switch to Th2 phenotype during Mycosis Fungoides progression may be driven via aberrant histone acetylation and/or upregulation of oncogenic miR-155 microRNA.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it