Anthocyanidins inhibit epithelial–mesenchymal transition through a TGFβ/Smad2 signaling pathway in glioblastoma cells
Bibliographic record
Abstract
Epidemiological studies have convincingly demonstrated that diets rich in fruits and vegetables play an important role in preventing cancer due to their polyphenol content. Among polyphenols, the anthocyanidins are known to possess anti-inflammatory, cardioprotective, anti-angiogenic, and anti-carcinogenic properties. Despite the well-known role of transforming growth factor-β (TGF-β) in high grade gliomas, the impact of anthocyanidins on TGF-β-induced epithelial-mesenchymal transition (EMT), a process that allows benign tumor cells to infiltrate surrounding tissues, remains poorly understood. The objective of this study is to investigate the impact of anthocyanidins such as cyanidin (Cy), delphinidin (Dp), malvidin (Mv), pelargonidin (Pg), and petunidin (Pt) on TGF-β-induced EMT and to determine the mechanism(s) underlying such action. Human U-87 glioblastoma (U-87 MG) cells were treated with anthocyanidins prior to, along with or following the addition of TGF-β. We found that anthocyanidins differently affected TGF-β-induced EMT, depending on the treatment conditions. Dp was the most potent EMT inhibitor through its inhibitory effect on the TGF-β Smad and non-Smad signaling pathways. These effects altered expression of the EMT mesenchymal markers fibronectin and Snail, as well as markedly reducing U-87 MG cell migration. Our study highlights a new action of anthocyanidins against EMT that supports their beneficial health and chemopreventive effects in dietary-based strategies against cancer. © 2016 Wiley Periodicals, Inc.
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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".