Anti-cancer and Anti-angiogenic Effects of Curcumin and Epigallocathechin-3-Gallate in a Mouse Model of Renal Cancer
Bibliographic record
Abstract
Although conventional chemotherapeutic drugs are the first line of treatment for cancer, they have numerous side effects. One of the emerging challenges in cancer treatment is drug resistance. Nat-ural compounds have proven effective against various hallmarks of cancer for their multi-target inhibition properties, but especially for their ability to synergistically bypass low bioavailability. Methods: The present study investigated the in vivo antitumor effects of a combination of two natural dietary agents, epigallocatechin-3-gallate (EGCG) from Camelia Sinensis and curcumin, a component of turmeric (Curcuma longa). We aimed to compare, for the first time in vivo, the antiangiogenic and antitumor effects of sunitinib with the combination of curcumin and EGCG in a mouse model of renal cell carcinoma (ccRCC). It was shown that they are able to inhibit cell survival, proliferation of several type of cancer, including renal cell carcinoma (ccRCC), by mod-ulating different signaling pathways and that their combination respect to single compound syn-ergistically decreased angiogenesis. Results: Herein, we highlighted that these compounds inhib-ited the growth of xenografted renal cancer in nude mice by significant inhibition of tumor vol-ume, tumor weight and CD31 expression with no signs of hepatic toxicity. Moreover, mice treated with these natural compounds showed a significant reduction in angiogenesis and an improve-ment in survival rate with p<0.05. Finally, pretreatment of mice with a diet containing 0.6% curcumin before injection of tumor cells showed a significant inhibition of tumor engraftment in 60% of mice with respect to controls and other groups. Conclusions: Taken together, our data in-dicate, for the first time, that the combination of curcumin and EGCG acts in a synergistic manner to inhibit the growth and angiogenesis of ccRCC and with less toxicity than sunitinib and provide an important rationale for future clinical development for chemoprevention and treatment of re-nal cancer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".