Inhibition of Constitutive Activation of STAT3 by Curcurbitacin-I (JSI-124) Sensitized Human B-Leukemia Cells to Apoptosis
Bibliographic record
Abstract
Phosphorylation of STAT3 on serine 727 regulates gene expression and is found to be elevated in many B-leukemia cells including chronic lymphocytic leukemia (CLL). It is, however, unclear whether targeting STAT3 will be an effective antileukemia therapy. In this study, we assessed in vitro antileukemia activity of the STAT3 inhibitor JSI-124 (cucurbitacin I). JSI-124 potently induces apoptosis in 3 B-leukemia cell lines (BJAB, I-83, and NALM-6) and in primary CLL cells and was associated with a reduction in serine 727 phosphorylation of STAT3. Similarly, knockdown of STAT3 expression induced apoptosis in these leukemia cells. In addition, we found that JSI-124 and knockdown of STAT3 decreased antiapoptotic protein XIAP expression and overexpression of XIAP blocked JSI-124-induced apoptosis. Furthermore, we found that combined treatment of JSI-124 and TRAIL increased apoptosis associated with an increase in death receptor 4 expression. Besides apoptosis, we found that JSI-124 also induced cell-cycle arrest prior to apoptosis in B-leukemia cells. This corresponded with reduced expression of the cell-cycle regulatory gene, cdc-2. Thus, we present here for the first time that JSI-124 induced suppression of serine 727 phosphorylation of STAT3, leading to apoptosis and cell-cycle arrest through alterations in gene transcription in B-leukemia cells.
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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".