Mitochondrial biogenesis in epithelial cancer cells promotes breast cancer tumor growth and confers autophagy resistance
Bibliographic record
Abstract
Here, we set out to test the novel hypothesis that increased mitochondrial biogenesis in epithelial cancer cells would "fuel" enhanced tumor growth. For this purpose, we generated MDA-MB-231 cells (a triple-negative human breast cancer cell line) overexpressing PGC-1α and MitoNEET, which are established molecules that drive mitochondrial biogenesis and increased mitochondrial oxidative phosphorylation (OXPHOS). Interestingly, both PGC-1α and MitoNEET increased the abundance of OXPHOS protein complexes, conferred autophagy resistance under conditions of starvation and increased tumor growth by up to ~3-fold. However, this increase in tumor growth was independent of neo-angiogenesis, as assessed by immunostaining and quantitation of vessel density using CD31 antibodies. Quantitatively similar increases in tumor growth were also observed by overexpression of PGC-1β and POLRMT in MDA-MB-231 cells, which are also responsible for mediating increased mitochondrial biogenesis. Thus, we propose that increased mitochondrial "power" in epithelial cancer cells oncogenically promotes tumor growth by conferring autophagy resistance. As such, PGC-1α, PGC-1β, mitoNEET and POLRMT should all be considered as tumor promoters or "metabolic oncogenes." Our results are consistent with numerous previous clinical studies showing that metformin (a weak mitochondrial "poison") prevents the onset of nearly all types of human cancers in diabetic patients. Therefore, metformin (a complex I inhibitor) and other mitochondrial inhibitors should be developed as novel anticancer therapies, targeting mitochondrial metabolism in cancer cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".