METTL3-mediated m<sup>6</sup>A modification of lnc KCNQ1OT1 promotes doxorubicin resistance in breast cancer by regulating miR-103a-3p/MDR1 axis
Bibliographic record
Abstract
Doxorubicin (DOX) resistance in breast cancer (BC) poses a huge challenge for the therapeutic effect on BC. Lnc KCNQ1OT1 play crucial roles in chemotherapy resistance. However, the role and mechanism of lnc KCNQ1OT1 in DOX resistance BC have not been investigated, which merits further exploration. Based on MCF‐7 and MDA-MB-231 cells, MCF‐7/DOX and MDA-MB-231/DOX cells were established using gradient concentrations of DOX. IC50 values and cell viability were determined using MTT. Cell proliferation was investigated by colony formation. Flow cytometry was performed to examine cell apoptosis and cell cycle. Gene expression was examined using qRT-PCR and western blot. The interactions among METTL3, lnc KCNQ1OT1, miR-103a-3p, and MDR1 were validated with MeRIP-qPCR, RIP, and dual-luciferase reporter gene assays. The results showed that Lnc KCNQ1OT1 was highly expressed in DOX-resistant BC cells, and lnc KCNQ1OT1 depletion could enhance DOX sensitivity in BC cells and DOX-resistant BC cells. Besides, lnc KCNQ1OT1 was modulated by MELLT3 in the manner of m6A modification. MiR-103a-3p could interact with lnc KCNQ1OT1 and MDR1. Overexpression of MDR1 abolished the impacts of lnc KCNQ1OT1 depletion on DOX resistance in BC. In conclusion, our results unveiled that in BC cells and DOX-resistant BC cells, lnc KCNQ1OT1 could be mediated by METTL3 through m6A modification to elevate and stabilize its expression, further inhibiting miR-103a-3p/MDR1 axis to promote DOX resistance, which might provide novel thought to overcome DOX resistance in BC.
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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".