Restoration of miR-23a expression by chidamide sensitizes CML cells to imatinib treatment with concomitant downregulation of CRYAB
Bibliographic record
Abstract
MicroRNAs (miRNAs) are involved in various processes from the initiation and development of cancers, including chronic myeloid leukemia (CML). In this report, we aimed to investigate the roles of miR-23a in the regulation of imatinib mesylate (IM) sensitivity in CML cells and the possible mechanisms involved in this process. We demonstrated that the expression of miR-23a was markedly low in bone marrow mononuclear cells from patients in whom IM treatment had failed and imatinib-resistant K562/G01 cells when compared to patients with optimal responses and imatinib-sensitive K562 cells, respectively. Overexpression of miR-23a was shown to induce apoptosis of K562/G01 cells and sensitize these cells to imatinib treatment. With the aid of bioinformatics analysis, we revealed that CRYAB could be a potential downstream effector of miR-23a, contributing to miR-23a-mediated IM resistance. We also observed that the expression of CRYAB was inversely correlated with miR-23a expression in CML cell lines and patient samples. Importantly, chidamide upregulated miR-23a expression and reversed the IM resistance of CML cells. Together, these findings strongly suggest that miR-23a acts as a tumor suppressor by downregulating CRYAB expression. Restoration of miR-23a by chidamide may therefore have a therapeutic effect in controlling the sensitivity of CML cells to imatinib.
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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 | 0.001 |
| 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".