Attenuation of STAT3 Signaling Cascade by Daidzin Can Enhance the Apoptotic Potential of Bortezomib against Multiple Myeloma
Bibliographic record
Abstract
(Fabaceae) is a widely known phytoestrogen. DDZ can display anti-cancer activities against breast and prostate cancers, but its anti-oncogenic actions in multiple myeloma (MM) cells have not been studied. The signal transducer and activator of transcription 3 (STAT3) can control key processes including proliferation, differentiation, and survival in MM cells. Here, we noted that DDZ abrogated STAT3 activation (both constitutive as well as inducible) at Tyr705 and Ser727 in MM cells. Additionally, DDZ mitigated the phosphorylation of STAT3 upstream Janus-activated kinases (JAK1/2) and c-Src kinases. Pervanadate (tyrosine phosphatase blocker) exposure altered the DDZ-induced inhibition of STAT3 activation, thus affecting the action of this phytoestrogen on apoptosis. Moreover, DDZ impeded proliferation and augmented the apoptotic effects of bortezomib (Bor) in MM cells. Overall, the data indicate that DDZ may act as a potent suppressor of STAT3 signaling cascade, and the co-treatment of DDZ and Bor could be a promising therapeutic strategy, specifically in MM.
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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".