Molecular Adaptations to Repeated Radiation Exposure in Triple-Negative Breast Cancer: Dysregulation of Cell Adhesion, Mitochondrial Function, and Epithelial–Mesenchymal Transition
Bibliographic record
Abstract
Radiation resistance presents a significant challenge in the treatment of triple-negative breast cancer (TNBC). To investigate the molecular adaptations associated with radiation therapy resistance, MDA-MB-231 cells were subjected to a repeated radiation (RR) regimen totaling 57 Gy over 11 weeks, followed by clonal selection. The resulting radiation-adapted cells (MDA-MB-231RR) were analyzed using whole-transcriptome RNA sequencing, revealing substantial dysregulation of pathways related to cell adhesion, mitochondrial function, and epithelial–mesenchymal transition (EMT). These transcriptional changes were corroborated by functional assays. MDA-MB-231RR cells exhibited reduced expression of adhesion receptors (ITGB1, ITGA2, ITGA6) and extracellular matrix proteins (fibronectin, collagen, laminins), accompanied by significantly impaired cell adhesion to fibronectin, collagen, and laminin substrates. Mitochondrial dysfunction was supported by downregulation of oxidative phosphorylation genes (MTCO1, MTND1) and confirmed by JC-1 dye assays demonstrating a marked reduction in mitochondrial membrane potential. EMT-associated changes included increased mesenchymal markers and loss of epithelial markers (CTNNB1, SNAI2, CK19), consistent with enhanced migratory potential. Taken together, this study delineates key molecular features of radiation adaptation in TNBC, providing a foundation for the development of targeted therapies to overcome treatment resistance.
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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".