Application of Gene Editing Technology Based on Targeted Delivery Materials in TNBC
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer, lacking the expression of estrogen receptor, progesterone receptor, and HER2, which leads to poor prognosis and limited treatment options. Despite advances in targeted therapies, TNBC patients often fail to benefit due to its heterogeneity, drug resistance, and immune evasion. Gene editing technologies such as CRISPR/Cas9 and RNA interference offer promising strategies for precise gene modulation, yet their clinical translation is challenged by delivery efficiency, off-target effects, and safety concerns. This review systematically summarizes key regulatory genes implicated in TNBC progression, including those involved in invasion, proliferation, and chemoresistance, such as BRCA1/2, TP53, MUC1, EGFR, MYC, and others. We further discuss advances in gene editing tools and their combination with targeted delivery materialsranging from viral vectors (AAV, lentivirus) to nonviral platforms (lipid nanoparticles, polymer-based systems, and bioderived vesicles)highlighting their potential to enhance editing specificity, minimize immune response, and overcome tumor microenvironment barriers. Finally, we address biosafety, ethical concerns, and mitigation strategies such as high-fidelity Cas9 variants and AI-assisted off-target prediction. Collectively, this review provides a comprehensive framework for future research and clinical application of targeted gene editing in TNBC, aiming to develop safer, more effective, and personalized treatment strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".