MétaCan
Menu
Back to cohort
Record W2503929404 · doi:10.1158/1538-7445.am2016-1689

Abstract 1689: Resveratrol inhibits epithelial-to-mesenchymal transition and metastasis in colorectal cancer through regulating Snail/E-cadherin expression by TGFβ1/Smads signaling pathway

2016· article· en· W2503929404 on OpenAlexaff
Qing Ji, Zhifen Han, Lihong Zhou, Hua Sui, Xuan Liu, Jianlin Ren, Fenggang Hou, Ronghua Zhao, Qi Li

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsResveratrolEpithelial–mesenchymal transitionMetastasisCancer researchIn vivoTransplantationVimentinMedicinePathologyCancerBiologyImmunohistochemistryInternal medicinePharmacology

Abstract

fetched live from OpenAlex

Abstract Resveratrol has been an ideal alternative drug in the therapy of different cancers including colorectal cancer (CRC). Since the underlying mechanisms of resveratrol on the invasion and metastasis of CRC have not been fully elucidated, and epithelial-to-mesenchymal transition (EMT) is a key process associated with the progression of CRC, here we aimed to investigate the potential mechanism of resveratrol. We investigated the anticancer effect of resveratrol against LoVo cells in vitro and in vivo. In vivo, the impact of resveratrol on invasion and metastasis was investigated by mice tail vein injection model and mice orthotopic transplantation tumor model. In vivo imaging was applied to observe the lungs metastases, and hemaoxylin-eosin (HE) staining was used to evaluate metastatic lesions from lung metastases or hepatic metastases. In vitro, impact of resveratrol on the migration and invasion of LoVo cells was evaluated by transwell assay. Inhibition effect of resveratrol on TGF-β-induced EMT was examined by morphological observation. Epithelial phenotype marker E-cadherin and mesenchymal phenotype marker Vimentin were detected by western blot and immunofluorescence. The promoter activity of E-cadherin was measured using a dual-luciferase assay kit. The mRNA expression of Snail and E-cadherin was measured by RT-PCR. We demonstrated that, resveratrol inhibited the lung metastases of CRC LoVo cells in vivo. In addition, resveratrol reduced the rate of lung metastases and hepatic metastases in mice orthotopic transplantation. In vitro, TGF-β1-induced EMT promoted the invasion and metastasis of CRC, reduced the expression of E-cadherin and elevated the expression of Vimentin, and activated the TGF-β1/Smads signaling pathway. But resveratrol could inhibit the invasive and migratory ability of LoVo cells in a concentration-dependent manner, increased the expression of E-cadherin, repressed the expression of Vimentin, as well as the inhibition of TGF-β1/Smads signaling pathway. Meanwhile, resveratrol reduced the level of EMT-inducing transcription factors Snail and the transcription of E-cadherin during the initiation of TGF-β1-induced EMT. Our new findings provided evidence that, resveratrol could inhibit EMT in CRC through TGF-β1/Smads signaling pathway mediated Snail/E-cadherin expression, and this might the potential mechanism of resveratrol on the inhibition of invasion and metastases in CRC. Citation Format: Qing Ji, Zhifen Han, Lihong Zhou, Hua Sui, Xuan Liu, Jianlin Ren, Fenggang Hou, Ronghua Zhao, Qi Li. Resveratrol inhibits epithelial-to-mesenchymal transition and metastasis in colorectal cancer through regulating Snail/E-cadherin expression by TGFβ1/Smads signaling pathway. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1689.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.032
GPT teacher head0.343
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCancer ResearchSame topicCancer-related molecular mechanisms researchFrench-language works237,207