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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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