Molecular Mechanisms of Radiation Resistance in Breast Cancer: A Systematic Review of Radiosensitization Strategies
Bibliographic record
Abstract
Breast cancer remains one of the most prevalent malignancies worldwide, and radiation therapy is a central component of its management. However, intrinsic or acquired resistance to radiation significantly compromises therapeutic efficacy. This systematic review aimed to identify and evaluate molecular mechanisms and interventions that influence radiation sensitivity in breast cancer models. A comprehensive PubMed search was conducted using the terms “breast cancer” and “radiation resistance” for studies published between 2002 and 2024. Seventy-nine eligible studies were included. The most frequently investigated mechanisms included the dysregulation of the PI3K/AKT/mTOR and MAPK signaling pathways, enhanced DNA damage repair via non-homologous end joining (NHEJ), and the overexpression of cancer stem cell markers such as CD44+/CD24−/low and ALDH1. Several studies highlighted the role of non-coding RNAs, particularly the lncRNA DUXAP8 and microRNAs such as miR-21, miR-144, miR-33a, and miR-634, in modulating radiation response. Components of the tumor microenvironment, including cancer-associated fibroblasts and immune regulators, also contributed to radiation resistance. By synthesizing current evidence, this review provides a consolidated resource to guide future mechanistic studies and therapeutic development. This review highlights promising molecular targets and emerging strategies to enhance radiosensitivity and offers a foundation for translational research aimed at improving outcomes in radiation-refractory breast cancer.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".