IL-6 promotes MYC-induced B cell lymphomagenesis independent of STAT3
Bibliographic record
Abstract
The inflammatory cytokine IL-6 is known to play a causal role in the promotion of cancer, although the underlying mechanisms remain to be completely understood. Interplay between endogenous and environmental cues determines the fate of cancer development. The Eμ-myc transgenic mouse expresses elevated levels of c-Myc in the B cell lineage and develops B cell lymphomas with associated mutations in p53 or other genes linked to apoptosis. We generated Eμ-myc mice that either lacked the IL-6 gene, or lacked the STAT3 gene specifically in B cells to determine the role of the IL-6/JAK/STAT3 pathway in tumor development. Using the Eμ-myc lymphoma mouse model, we demonstrate that IL-6 is a critical tumor promoter during early stages of B cell lymphomagenesis. IL-6 is shown to inhibit the expression of tumor suppressors, notably BIM and PTEN, and this may contribute to advancing MYC-driven B cell tumorigenesis. Several miRNAs known to target BIM and PTEN are upregulated by IL-6 and likely lead to the stable suppression of pro-apoptotic pathways early during the tumorigenic process. STAT3, a classical downstream effector of IL-6, appears dispensable for Eμ-myc driven lymphomagenesis. We conclude that the growth-promoting and anti-apoptotic mechanisms activated by IL-6 are critically involved in Eμ-myc driven tumor initiation and progression, but the B cell intrinsic expression of STAT3 is not required.
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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) | 0.003 | 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".