Gene Expression Differences between Low and High Stage Diffuse Large B Cell Lymphoma (DLBCL).
Bibliographic record
Abstract
Abstract Low stage DLBCL patients have better prognosis than those with advanced disease. We searched for differences in gene expression between disease stages to find correlations with tumor stage as possible therapeutic targets. We used 2 microarray datasets from the Leukemia/Lymphoma Molecular Profiling Project containing nodal and extra-nodal de novo DLBCL samples on Lymphochip (LC), spotted arrays of ~7400 and Affymetrix arrays (Affy) of ~45,000 elements. The Affy dataset was a subset of the LC samples. LC elements were median-centered, log2 transformed; Affy elements were array-centered to a 500 average. We compared average gene expression in stage I vs. III/IV, looking for elements with >2-fold differences between low and high stage, with p<.05, excluding Affy elements with an average <100. 35 stage I and 131 stage III/IV samples were analyzed with LC and 34 stage I and 118 stage III/IV samples with Affy. Differentially expressed genes were searched by NCBI Entrez and Stratagene PathwayArchitect software to find common pathways of regulation. From the LC data, 6 genes were identified as differentially expressed, 5 were higher in stage I, 1 was lower in stage I. For the Affy data, 29 genes were differentially expressed, 11 were higher in stage I, 18 were lower in stage I (Table 1). Differences were rarely >3-fold. A subset of these genes, CTGF (connective tissue growth factor), FN1 (fibronectin), INHBA (activin), POSTN (periostin), THBS1 (thrombospondin), and BCL2, are coregulated and/or coregulatory. Many are expressed in the microenvironment and associated with fibrosis, wound healing and Th2 immune response. The presence of these genes at higher levels and their known associations implied a pathway of TGF-β signaling present mainly in the low stage DLBCL. Although TGF-β levels were not different between stages, differences in localization/activation of TGF-β could account for observed differences. In conclusion, there are a small number of stage-specific gene expression differences in DLBCL, and many of these relate to TGF-β signaling in the microenvironment. CTGF and FN1 were previously identified as key prognostic molecules in DLBCL (Rosenwald et al, NEJM 2002). These differences may relate to different prognoses between DLBCL stages and constitute therapeutic targets. Genes Differentially Expressed by Stage higher in stage I higher in stage III/IV LC Affy LC Affy CTGF CTGF IGHM IBRDC2 (2 elements) SERPINA1 MYBPC1 HTR3A POSTN NCR3 BCL2 FN1 INHBA (2 elements) CRYM THBS1 EMP1 SIX1 KLHL14 MGC23911 PP1665 GPM6A (2 elements) FLJ33069 TUBB IgM rheum. factor RF-TT9 CD1C transcribed locus LOC150568 LOC346887 LOC283454 MGC17624 IMAGE:5311619 transcribed locus (2x)
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 | 0.000 |
| 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 | 0.000 |
| 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".