Interleukin-17F Suppresses Hepatocarcinoma Cell Growth via Inhibition of Tumor Angiogenesis
Bibliographic record
Abstract
Previous studies have shown that interleukin-17F (IL-17F) can markedly inhibit the angiogenesis of endothelial cells, implying that it may play a role in antiangiogenic therapy for tumors. To explore its effect on antiangiogenic therapy for hepatocellular carcinoma (HCC), we constructed a recombinant retrovirus vector RV-IL-17F expressing IL-17F, transfected SMMC-7721 human hepatocarcinoma cells with RV-IL-17F, and investigated the effect of transgene IL-17F expression on human hepatocarcinoma cells in vitro and in vivo in animal model. We demonstrated that IL-17F expression exerted no direct effect on in vitro proliferation and cell cycle of SMMC-7721 hepatocarcinoma cells, while it downregulated IL-6, IL-8, and VEGF expression in SMMC-7721 cells at both protein and mRNA levels and IL-17F-expressing supernatant from SMMC-7721/RV-IL-17F directly inhibited ECV304 vascular endothelial cell growth. Moreover, SMMC-7721/RV-IL-17F exhibited a significant decrease in tumor size and microvessel density as compared to the SMMC-7721/RV control when transplanted in nude mice. This retarded tumor growth in vivo elicited by IL-17F was associated with direct suppression of vascular endothelial cells and reduced expression of proangiogenic factors IL-6, IL-8, and VEGF leading to the inhibition of tumor angiogenesis. Thus, our results indicate that IL-17F, a novel antiangiogenic factor, may be useful in antiangiogenic therapy for HCC.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".