Memory T‐Cell Phenotype in Cutaneous T‐Cell Lymphoma Is Modified by Germline Gene Gametocyte Specific Factor 1
Bibliographic record
Abstract
Cutaneous T-cell lymphoma (CTCL) is a heterogeneous group of lymphoproliferative disorders characterised by skin infiltration by malignant memory T cells. While most patients will present with an indolent disease, others will follow a highly aggressive clinical course. Currently, defining disease prognosis remains challenging. Ectopic expression of gametocyte-specific factor 1 (GTSF1) has emerged as a potential prognostic biomarker. However, its contribution to CTCL carcinogenesis remains unknown. Here, we report that GTSF1 contributes to carcinogenesis by partially modifying the memory/effector phenotype of the malignant T cells. GTSF1 knockdown in CTCL cells led to T-cell activation and production of IFNγ and TNFα. Advanced stages of the disease are associated with decreased production of these cytokines. Notably, we show that patients classified with high expression of GTSF1 are associated with a worse disease prognosis. Taken together, our findings indicate that GTSF1 expression in CTCL cells allows them to acquire memory T-cell phenotype. Malignant memory T cells have a decreased production of immune-responsive cytokines, leading to a diminished immune response and disease progression. GTSF1 is an important candidate as a prognostic biomarker. Furthermore, understanding the specific function of GTSF1 might help develop novel targeted treatment options for CTCL 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) | 0.001 | 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".