Interleukin 22 Signaling Promotes Cell Growth in Mantle Cell Lymphoma
Bibliographic record
Abstract
Mantle cell lymphoma (MCL) is a specific type of aggressive B-cell non-Hodgkin lymphoma. We recently found that IL-22RA1, one of the two subunits of the interleukin 22 (IL-22) receptor, is expressed in MCL cell lines but not benign lymphocytes. In view of normal functions of IL-22 signaling, we hypothesized that the aberrant expression of IL-22RA1 may contribute to the deregulation of various cell signaling pathways, thereby promoting cell growth in MCL. In this study, we first demonstrated the expression of IL-22RA1 in all three MCL cell lines and eight frozen tumors examined using reverse transcription-polymerase chain reaction and Western blot analysis. In support of the concept that IL-22 signaling is biologically important in MCL, we found that MCL cells treated with recombinant IL-22 had a significant increase in cell growth that was associated with STAT3 activation. To investigate the mechanism underlying the aberrant expression of IL-22RA1, we analyzed the gene promoter of IL-22RA1, and we found multiple binding sites for NF-κB, a transcriptional factor strongly implicated in the pathogenesis of MCL. Pharmacologic inhibition of NF-κB resulted in a substantial reduction in the level of IL-22RA1 protein expression in MCL cells. To conclude, IL-22RA is aberrantly expressed in MCL, and we have provided evidence that IL-22 signaling contributes to the pathogenesis of MCL.
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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 | 0.001 |
| 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".