Downregulation of stromal BRCA1 drives breast cancer tumor growth via upregulation of HIF-1α, autophagy and ketone body production
Bibliographic record
Abstract
Our recent studies have mechanistically demonstrated that cancer-associated fibroblasts (CAFs) produce energy-rich metabolites that functionally support the growth of cancer cells. Also, several authors have demonstrated that DNA instability in the tumor stroma greatly contributes to carcinogenesis. To further test this hypothesis, we stably knocked-down BRCA1 expression in human hTERT-immortalized fibroblasts (shBRCA1) using an shRNA lentiviral approach. As expected, shBRCA1 fibroblasts displayed an elevated growth rate. Using immunofluorescence and immunoblot analysis, shBRCA1 fibroblasts demonstrated an increase in markers of autophagy and mitophagy. Most notably, shBRCA1 fibroblasts also displayed an elevation of HIF-1α expression. In accordance with these findings, shBRCA1 fibroblasts showed a 5.5-fold increase in ketone body production; ketone bodies function as high-energy mitochondrial fuels. This is consistent with the onset of mitochondrial dysfunction in BRCA1-deficient fibroblasts. Conversely, after 48 h of co-culturing shBRCA1 fibroblasts with a human breast cancer cell line (MDA-MB-231 cell), mitochondrial activity was enhanced in these epithelial cancer cells. Interestingly, our preclinical studies using xenografts demonstrated that shBRCA1 fibroblasts induced an ~2.2-fold increase in tumor growth when co-injected with MDA-MB-231 cells into nude mice. We conclude that a BRCA1 deficiency in the tumor stroma metabolically promotes cancer progression, via ketone production.
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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".