Targeting lncRNA DDIT4‐AS1 Sensitizes Triple Negative Breast Cancer to Chemotherapy via Suppressing of Autophagy
Bibliographic record
Abstract
In this study, it is found that the lncRNA, DNA damage inducible transcript 4 antisense RNA1 (DDIT4-AS1), is highly expressed in triple-negative breast cancer (TNBC) cell lines and tissues due to H3K27 acetylation in the promoter region, and promotes the proliferation, migration, and invasion of TNBC cells via activating autophagy. Mechanistically, it is shown that DDIT4-AS1 induces autophagy by stabilizing DDIT4 mRNA via recruiting the RNA binding protein AUF1 and promoting the interaction between DDIT4 mRNA and AUF1, thereby inhibiting mTOR signaling pathway. Furthermore, silencing of DDIT4-AS1 enhances the sensitivity of TNBC cells to chemotherapeutic agents such as paclitaxel both in vitro and in vivo. Using a self-activatable siRNA/drug core-shell nanoparticle system, which effectively deliver both DDIT4-AS1 siRNA and paclitaxel to the tumor-bearing mice, a significantly enhanced antitumor activity is achieved. Importantly, the codelivery nanoparticles exert a stronger antitumor effect on breast cancer patient-derived organoids. These findings indicate that lncRNA DDIT4-AS1-mediated activation of autophagy promotes progression and chemoresistance of TNBC, and targeting of DDIT4-AS1 may be exploited as a new therapeutic approach to enhancing the efficacy of chemotherapy against TNBC.
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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".