Promising tumor inhibiting potentials of Fisetin through PI3K/AKT/mTOR pathway.
Bibliographic record
Abstract
Recently, Zhuo et al. reported an interesting data in their article. Fisetin, a dietary biofla-vonoid, reverses acquired Cisplatin-resistance of lung adenocarcinoma cells through MAPK/Survivin/Caspase pathway [1]. Tumor inhibiting potentials of Fisetin was reported earlier by different research groups. Initiation of BRAF/MEK/ERK (MAPK) pathway by activating PI3K/AKT/mTOR signaling induces epithelial to mesenchymal transition (EMT) in cancer cells, leading to cell invasion and metastasis. Targeting these signaling mechanisms is crucial to develop an effective drug in cancer treatment. Fisetin inhibits ADAM9 expressions, activates ERK1/2 in glioma cancer cells [2] and attenuates colon tumor growth by inhibiting heat shock factor 1 (HSF1) in HCT-116 colon carcinoma cells [3]. Pal et al. showed inhibition of EMT in melanoma cells with reduction in MMP-2 and MMP-9 levels [4]. Fisetin in combination with sorafenib (chemotherapeutic drug) also inhibits Snail1, Twist1, Slug and ZEB1 protein expressions. It was reported to inhibit tumor growth by down-regulating PI3K/AKT and mTOR signaling and expressing PTEN protein levels in A549 lung carcinoma [5] and inmultiple myeloma U266 cells [6]. Furthermore, it also decreases phosphorylation of AKT, mTOR, mitf & p70S6K proteins in human melanoma 451Lu cells [7]. In Swiss albino mice, Fisetin showed protection against benzo(a)pyrene-induced lung carcinogenesis by restoring PCNA expression levels [8]. It exhibits protection against UV-B induced inflammation by inhibiting NF-κB/PI3K/AKT pathway in SKH-1 hairless mice. It also reduced DNA damage by activating markers such as p53 and p21 proteins [4] and inhibits phosphorylation of H2AX protein- a key element in DNA damage detection. Novel inhibitors of PI3K, AKT, and mTOR are now under early clinical phase trials [9]. Since Fisetin acts through PI3K/AKT/mTOR pathway in many cancer cells, as evident from above examples, it might bring attention to use as a novel chemopreventive drug in cancer treatment.
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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 | 0.001 | 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) | 0.003 | 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".