Transcript profiling in peripheral T-cell lymphoma, not otherwise specified, and diffuse large B-cell lymphoma identifies distinct tumor profile signatures
Bibliographic record
Abstract
To glean biological differences and similarities of peripheral T-cell lymphoma-not otherwise specified [PTCL-NOS] to diffuse large B-cell lymphoma (DLBCL), a transcriptosome analysis was done on five PTCL-NOS and four DLBCL patients and validated by quantitative real-time reverse transcription-PCR on 10 selected genes. Normal peripheral blood T cells, peripheral blood B cells, and lymph node were used as controls. The resultant gene expression profile delineated distinct "tumor profile signatures" for PTCL-NOS and DLBCL. Several highly overexpressed genes in both PTCL-NOS and DLBCL involve the immune network, stroma, angiogenesis, and cell survival cascades that make important contributions to lymphomagenesis. Inflammatory chemokines and their receptors likely play a central role in these complex interrelated pathways: CCL2 and CXCR4 in PTCL-NOS and CCL5 and CCR1 in DLBCL. Highly overexpressed oncogenes unique to PTCL-NOS are SPI1, STK6, alpha-PDGFR, and SH2D1A, whereas in DLBCL they are PIM1, PIM2, LYN, BCL2A1, and RAB13. Oncogenes common to both lymphomas are MAFB, MET, NF-kappaB2, LCK, and LYN. Several tumor suppressors are also down-regulated (TPTE, MGC154, PTCH, ST5, and SUI1). This study illustrates the relevance of tumor-stroma immune trafficking and identified potential novel prognostic markers and targets for therapeutic intervention.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".