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 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".