Inhibition of TLR4/TRIF/IRF3 Signaling Pathway by Curcumin in Breast Cancer Cells
Bibliographic record
Abstract
PURPOSE: Toll-like receptor 4 (TLR4) is over-expressed in breast tumors and thus contributing to the tumor progression and metastasis. Natural products have drawn attention in cancer immunotherapy due to their various biological activities. Curcumin is well investigated in different types of cancer. However, the mechanisms underlying its anti-inflammatory actions have not been extensively elucidated. For this purpose, we explored the inhibitory effects of curcumin on lipopolysaccharide (LPS)-induced TLR4 dependent TRIF signaling pathway in two subtypes of breast cancer cell lines (MCF-7 and MDA-MB-231) in this study. METHODS: In this context, the cytotoxicity of curcumin and LPS alone and the combination of curcumin with LPS on these cells was evaluated by WST-1 assay. The expression level of TLR4 and the release of type I interferon (IFN) levels were determined after treatment with curcumin and/or LPS by RT-PCR and ELISA analysis, respectively. Furthermore, the subcellular localization of TLR4 and interferon regulatory factor 3 (IRF3) were detected by immunofluorescence analysis. RESULTS: Curcumin treatment suppressed breast cancer cells viabilities and the activation of TLR4-mediated TRIF signaling pathway by the downregulation of TLR4 and IRF3 expression levels and the inhibition of type I IFN (IFN-α/β) levels induced by LPS. However, curcumin was more efficient in MDA- MB-231 cells than MCF-7 cells owing to its greater inhibitory efficacy in the LPS- enhanced TLR4 signaling pathway. Furthermore, IFN-α/β levels induced by TLR4 and IRF3 were decreased in these cells following curcumin treatment. CONCLUSIONS: Consequently, these results demonstrated that the activation of LPS stimulated TLR4/TRIF/IRF3 signaling pathway was mediated by curcumin in breast cancer cells, in vitro. However, more studies are necessary to examine the curcumin's anti-inflammatory activities on TLR4/MyD88/NF-κB as well as other signaling pathways downstream of TLRs in breast 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".