Role of interleukin‐17 in lymphangiogenesis in non‐small‐cell lung cancer: Enhanced production of vascular endothelial growth factor C in non‐small‐cell lung carcinoma cells
Bibliographic record
Abstract
Interleukin-17 (IL-17), a potent pro-inflammatory cytokine, plays an active role in inflammation and cancer. Recently, we found that increased IL-17-producing cells correlate with poor survival and increased lymphangiogenesis in non-small-cell lung cancer (NSCLC), but the mechanism is unknown. Here, we show that IL-17 promotes lymphangiogenesis via inducing vascular endothelial growth factor-C (VEGF-C) production by lung cancer cells. We found that IL-17 receptor (IL-17R) is expressed on the surface of Lewis lung carcinoma (LLC) cells but not on lymphatic endothelial cells (LEC). Moreover, LEC chemotaxis and tube formation (measures of net lymphangiogenic potential) were increased by conditioned medium from recombinant mouse IL-17 (rmIL-17)-stimulated LLC but not by rmIL-17. Interleukin-17 increased production of VEGF-C in lung cancer cell lines. The enhanced chemotaxis and endothelial cord formation in the presence of LLC/rmIL-17 was inhibited by addition of recombinant mouse VEGF R3/Fc chimera. Treatment of the A549 cells with rIL-17 significantly increased VEGF-C expression, which was extracellular signal-regulated protein kinase 1/2 (ERK 1/2) dependent. Importantly, we found significant correlations between IL-17 expression, VEGF-C expression and lymphatic vascular density (LVD) in NSCLC. We conclude that IL-17 is involved in lymphangiogenesis in NSCLC by enhancing production of VEGF-C, and IL-17 may be an important target for the treatment of NSCLC.
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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) | 0.001 | 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".