Proinflammatory Cytokine IL-1β Promotes Tumor Growth of Lewis Lung Carcinoma by Induction of Angiogenic Factors: In Vivo Analysis of Tumor-Stromal Interaction
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it