Comparative proteomic profiling of Hodgkin lymphoma cell lines
Bibliographic record
Abstract
Classical Hodgkin lymphoma (cHL) is a malignancy with complex pathogenesis. The hallmark of the disease is the presence of large mononucleated Hodgkin and bi- or multinucleated Reed/Sternberg (H/RS) cells. The origin of HRS cells in cHL is controversial as these cells show the coexpression of markers of several lineages. Using a proteomic approach, we compared the protein expression profile of cHL models of T- and B-cell derivation to find proteins differentially expressed in these cell lines. A total of 67 proteins were found differentially expressed between the two cell lines including metabolic proteins and proteins involved in the regulation of the cytoskeleton and/or cell migration, which were further validated by western blotting. Additionally, the expression of selected B- and T-cell antigens was also assessed by flow cytometry to reveal significant differences in the expression of different surface markers. Bioinformatics analysis was then applied to our dataset to find enriched pathways and networks, and to identify possible key regulators. In the present study, a proteomic approach was used to compare the protein expression profiles of two cHL cell lines. The identified proteins and/or networks, many of which not previously related to cHL, may be important to better define the pathogenesis of the disease, to identify novel diagnostic markers, and to design new therapeutic strategies.
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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 | 0.001 | 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) | 0.001 | 0.001 |
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".