Proinflammatory Cytokine IL-1β Promotes Tumor Growth of Lewis Lung Carcinoma by Induction of Angiogenic Factors: In Vivo Analysis of Tumor-Stromal Interaction
Bibliographic record
Abstract
Inflammatory conditions are associated with tumor development. IL-1beta is a multifunctional and proinflammatory cytokine that affects nearly all types of cells. To investigate the role of IL-1beta in tumor growth in vivo, we transduced the retroviral vector coding human IL-1beta gene into mouse Lewis lung carcinoma (LLC) cells and subsequently inoculated the transformant (LLC/IL-1beta) to syngeneic C57BL/6 mice. Tumors derived from LLC/IL-1beta grew faster (240%, day 18, vs null-vector control LLC/neo; p < 0.01) and showed more abundant vasculature (250%, vs LLC/neo; p < 0.05), whereas LLC/IL-1beta cells, LLC/neo cells, and wild-type LLC cells did not show any significant difference in the growth rate in vitro. As compared with LLC/neo cells, LLC/IL-1beta cells secreted 2-fold the amount of vascular endothelial growth factor and >10-fold the amount of macrophage-inflammatory protein-2 (CXCL2), one of whose main functions is angiogenesis. Although LLC/IL-1beta itself did not secrete hepatocyte growth factor (HGF), the tumor derived from LLC/IL-1beta cells also contained a >4-fold higher concentration of HGF, another angiogenic factor. In situ hybridization of HGF mRNA in LLC/IL-1beta tumor sections demonstrated that stromal fibroblasts and infiltrating cells overexpressed HGF mRNA. Moreover, when cultured in the presence of HGF in vitro, LLC/IL-1beta cells secreted even larger amounts of vascular endothelial growth factor and macrophage-inflammatory protein-2. The antiangiogenic agent TNP-470 and anti-CXCR2 Ab inhibited the tumor growth of LLC/IL-1beta cells in vivo. These results indicated that secreting IL-1beta into the tumor milieu induces several angiogenic factors from tumor and stromal cells and thus promotes tumor growth through hyperneovascularization.
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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) | 0.001 | 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 | 0.001 |
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".