Plasmodium infection inhibits tumor angiogenesis through effects on tumor-associated macrophages in a murine implanted hepatoma model
Bibliographic record
Abstract
Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death in China. The lack of an effective treatment for this disease results in a high recurrence rate in patients who undergo radical tumor resection, and the 5-year survival rate of these patients remains low. Our previous studies demonstrated that Plasmodium infection provides a potent antitumor effect by inducing innate and adaptive immunity in a murine Lewis lung carcinoma (LLC) model. Methods: This study aimed to investigate the inhibitory effect of Plasmodium infection on hepatocellular carcinoma in mice, and various techniques for gene expression analysis were used to identify possible signal regulation mechanisms. Results: We found that Plasmodium infection efficiently inhibited tumor progression and prolonged survival in tumor-bearing mice, which served as a murine implanted hepatoma model. The inhibition of tumor progression by Plasmodium infection was related to suppression of tumor angiogenesis within the tumor tissue and decreased infiltration of tumor-associated macrophages (TAMs). Further study demonstrated that matrix metalloprotease 9 (MMP-9) produced by TAMs contributed to tumor angiogenesis in tumor tissue and that the parasite-induced reduction in MMP-9 expression in TAMs resulted in the suppression of tumor angiogenesis. A mechanistic study revealed that the Plasmodium -derived hemozoin (HZ) that accumulated in TAMs inhibited IGF-1 signaling through the PI3-K and MAPK signaling pathways and thereby decreased the expression of MMP-9 in TAMs. Conclusions: Our study suggests that this novel approach of inhibiting tumor angiogenesis by Plasmodium infection is of high importance for the development of new therapies for cancer patients.
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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 | 0.000 |
| 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".