Gene expression profiling and immune cell-type deconvolution highlight robust disease progression and survival markers in multiple cohorts of CTCL patients
Bibliographic record
Abstract
CTCL follows different courses depending on the clinical stage at the time of diagnosis. Patients with early stage Mycosis Fungoides (MF) variant of CTCL may experience an indolent course over decades, whereas patients with advanced MF and Sézary Syndrome (SS) disease at diagnosis, often succumb within 5 years. Even within early stage CTCL/MF, a minority of patients will progress to more advanced stages. We recently generated RNA sequencing data on 284 CTCL-relevant genes for 157 patients and identified differentially expressed genes across stages I-IV. In this study, we aim to validate robust molecular markers linked to disease progression and survival. We performed multiple hypothesis testing-corrected analysis of variance (ANOVA) on the expression of individual genes across all CTCL samples and early stage (≤IIA) CTCL/MF patients. We used in silico immune cell-type deconvolution from gene expression data to estimate immune cell populations. Based on the analysis of all CTCL samples, we identified TOX, FYB, and CD52 as predictors of disease progression and poor survival. Among early stage (≤IIA) CTCL/MF patients, these 3 genes, along with CCR4, were valuable to predict disease progression. We validated these 4 genes in 3 independent, external Sézary Syndrome patient cohorts with RNA-Sequencing data. In silico immune cell-type deconvolution revealed that neutrophil infiltration in early stage MF conveyed a higher risk for disease progression. Also, NK cell infiltration in late stage MF/SS correlated with improved survival. TOX, FYB, CCR4 and CD52 are robust disease progression and decreased survival biomarkers in CTCL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".